55.2% of the top 10,000 tokens are HIGH RISK. 131 are confirmed honeypots – tokens where you can buy but cannot sell. 1,865 are upgradeable proxy contracts, of which 139 are controlled by a single private key that can silently replace the entire token implementation in one transaction. Only 18.7% pass all 127 security checks and receive a CLEAN verdict.

What Token Audit Found

BNB Smart Chain is the most dangerous chain in the dataset: 68.3% high risk and only 7.6% clean. Ethereum shows 59 confirmed honeypots – tokens that passed as legitimate long enough to enter the CoinGecko top 10,000, then trapped every buyer inside. The two most widespread risk patterns: 35.9% of tokens have no enforceable supply cap (unlimited inflation possible), and 34.4% have no timelock on privileged admin functions (instant malicious governance possible, no delay, no warning).

What Other Tools Miss

Token Audit runs 127 checks across 9 modules – Ownership, Supply, Liquidity, Transfer, Approve, Permit, Pausability, Reentrancy, and Proxy Analysis. The checks that matter most are the ones competitors cannot run: transitive approve() call graph analysis, phantom balanceOf detection, EIP-2612 permit preload, reentrancy invariants, and asymmetric pause detection (pause that blocks sells but not buys). These threats are invisible to GoPlus, CertiK Skynet Token Scan, TokenSniffer, and Honeypot.is – which together cover fewer than 40 checks, all at the interface level. The 599 verdicts in this dataset driven by Approve and Reentrancy findings represent tokens that every competing tool would have passed as clean. This same deployer-behavioral approach powers ChainAware’s token launch auditor agent, which combines contract risk with deployer wallet screening in a single call.

Co-Founder Statement

“We assumed the top 10,000 by market cap would be the safer end of the market. What we found is that more than half carry meaningful risk vectors – and roughly 600 of those are threats that no other automated tool would detect. The sophisticated operators know exactly which checks existing tools run, and they design around them. Token Audit was built to catch what they build.” – Martin Ploom, Co-Founder, ChainAware.ai

Proxy Risk: 139 Tokens One Transaction Away From a Honeypot

Token Audit’s proxy classification goes beyond detecting whether a contract is upgradeable. It identifies who controls the upgrade – and the answer matters enormously. Of 1,865 proxy contracts in the top 10,000, 139 are EOA-controlled: a single unprotected private key can replace the entire implementation in one block, with no timelock, no multisig, no governance vote. BSC carries the highest concentration – 75 of those 139 EOA-controlled proxies are on BSC, where the tactic is a known professional rug pull pattern. On the positive side, 153 UUPS proxies have permanently locked or renounced their upgrade path – Token Audit surfaces this as an explicit positive signal rather than treating all proxies as equally risky. This deployer-level behavioral scoring is the same methodology behind ChainAware’s Wallet Auditor, applied here to contract creators and LP providers instead of individual users.

For the full methodology, chain-by-chain breakdown, finding frequency analysis, honeypot signal correlations, and the complete competitive comparison against GoPlus, TokenSniffer, CertiK Skynet, and Honeypot.is, read the deep-dive: ChainAware Token Audit Launched – We Tested 10,000 CoinGecko Tokens. Here Are the Results. →

ChainAware.ai is the Web3 Agentic Growth Infrastructure – 20M+ wallet personas, 98% fraud detection accuracy, 127-check Token Audit, Agent Trust Score for 274,000+ ERC-8004 agents available via the Prediction MCP. Named in CB Insights’ AI Fraud Prevention Market Map. chainaware.ai

The post 55% of the Top 10,000 CoinGecko Tokens Are High Risk. ChainAware Token Audit Shows Why. first appeared on ChainAware.ai.

ChainAware has indexed 274,792 ERC-8004 agents across Ethereum, BSC, Base, and Avalanche. The data reveals a striking picture of the current agent ecosystem: 26% of indexed agents score Untrusted, 21.1% are flagged as farm-detected Sybil operations, and only 21.1% reach the Sovereign tier. More than half of all registered ERC-8004 agents carry material trust risk — and until today, no infrastructure existed to surface that risk before an interaction.

The Problem Agent Trust Score Solves

Agentic commerce — where AI agents execute transactions autonomously on behalf of users — is accelerating rapidly. McKinsey estimates AI agents could mediate $3-5 trillion in global commerce by 2030 . Today, 68% of new DeFi protocols launched in Q1 2026 include at least one autonomous AI agent. Every one of those agents initiates transactions without human approval — and every one of those transactions is a trust decision that previously had no infrastructure layer behind it.

The ERC-8004 Identity Registry tells you an agent exists. It does not tell you whether to trust it. Voting-based reputation systems can be gamed in hours — an operator deploying 50 agent wallets can manufacture a full review history at near-zero cost. ChainAware’s Agent Trust Score bypasses the reputation layer entirely and scores the human controlling the agent: their on-chain behavioral history, who funded their wallet, and whether they have previously created rug pull pools or honeypot tokens.

What the Data Shows

Additional signals: 21.1% carry the FARM_DETECTED flag, 9.5% have unknown feeder addresses, 7.6% use EIP-7702 delegated ownership, and 741 agents have confirmed rug pull history in their feeder chain.

CB Insights Validation

The launch follows ChainAware’s inclusion in the CB Insights AI Fraud Prevention Market Map , placing ChainAware in the On-Chain Intelligence category alongside Chainalysis, Elliptic, and TRM Labs. Read the full analysis in our CB Insights market map coverage.

Co-Founder Statement

“We indexed 274,792 ERC-8004 agents and found that more than half score either Untrusted or Elevated Risk. The agentic economy is being built on top of a registry that has no trust infrastructure. Agent Trust Score is the infrastructure that closes that gap.” — Martin Ploom, Co-Founder, ChainAware.ai

Integration

The Agent Trust Score API returns a 0-1000 score, tier, and flag set for any indexed ERC-8004 agent in under 100ms. It integrates natively with ChainAware’s Prediction MCP server. Enterprise rate limits, SLA, and webhook notifications are available on request.

For the full technical breakdown, integration guide, and ERC-8004 ecosystem data analysis, read the deep-dive: Agent Trust Score: On-Chain Trust Scoring for the Agentic Commerce Era →

ChainAware.ai is the Web3 Agentic Growth Infrastructure — 20M+ wallet personas, 98% fraud detection accuracy, <100ms API latency. Named in CB Insights’ AI Fraud Prevention Market Map. chainaware.ai

The post ChainAware Launches Agent Trust Score — On-Chain Trust Scoring for the Agentic Commerce Era first appeared on ChainAware.ai.

Something fundamental is changing in how commerce works. AI agents — software systems that can perceive, decide, and act autonomously — are beginning to transact. They are paying for APIs, settling invoices, executing DeFi strategies, managing DAO treasuries, and interacting with financial infrastructure in ways that traditional systems were never designed to handle. McKinsey estimates AI agents could mediate $3-5 trillion in global commerce by 2030 . Today, 68% of new DeFi protocols launched in Q1 2026 already include at least one autonomous AI agent.

Every one of those agents initiates transactions without human approval. Furthermore, every one of those transactions is a trust decision — a question of whether the agent on the other side of the interaction is controlled by a legitimate operator, or by someone whose on-chain history includes rug pulls, honeypot tokens, mixer exposure, and Sybil farming at scale. Until today, no infrastructure existed to answer that question. ChainAware’s Agent Trust Score is that infrastructure.

Read the launch announcement: ChainAware Launches Agent Trust Score — Official Announcement →

Table of Contents

1. The Agentic Commerce Era: Why Trust Is the Missing Layer
2. The Agentic Commerce Trust Stack: Where Agent Trust Score Fits
3. CB Insights Validation: ChainAware in the AI Fraud Prevention Market Map
4. What ERC-8004 Gives You — and What It Doesn’t
5. State of the ERC-8004 Registry: Trust Analysis of 274,792 Agents
6. The Five Signals Only ChainAware Provides
7. The Data Moat: Why This Cannot Be Replicated
8. How the Agent Trust Score Works
9. Score Tiers: What Each One Means
10. The Compounding Risk of Unscreened Agent Access
11. Integration Guide for DeFi Protocol Builders
12. Guide for Agent Creators: How Your Score Is Determined
13. How Agent Trust Score Compares to Other Platforms
14. Frequently Asked Questions

The Agentic Commerce Era: Why Trust Is the Missing Layer

Agentic commerce is not a future scenario — it is happening now, across every DeFi protocol that accepts agent-initiated transactions. Consequently, DeFi protocol builders face an immediate and urgent problem: how do you trust an agent you have never met, whose controlling wallet was created last week, whose funding source you cannot trace, and whose operator may have a history of financial fraud under a different wallet identity?

The scale of the shift is concrete. Morgan Stanley projects that nearly half of all online shoppers will use AI shopping agents by 2030 , accounting for approximately 25% of their total spending. In Web3 specifically, the transition is even faster — agents are moving from advisory roles (suggesting trades) to execution roles (completing them). The distinction between advice and execution is the distinction between a bad recommendation and an empty wallet.

Three converging forces are accelerating this shift in Web3. First, ERC-8004 went live on Ethereum mainnet in January 2026, giving AI agents a standardized on-chain identity for the first time. Second, x402 — the open payment protocol championed by Coinbase, Google Cloud, and Circle — provides agents with stablecoin-native payment rails, enabling micropayments without human login flows. Third, trust infrastructure has lagged behind. As Google Cloud’s Global Head of Strategy for Web3 stated at Consensus 2026: “The biggest friction points center on the fact that most products are still built for humans, not agents.” Agent Trust Score is ChainAware’s response to that friction at the trust layer specifically.

For a deep dive into the commercial context, see our article on why the first step in agentic commerce is trust, not integration.

The Know Your Agent (KYA) Imperative

Know Your Agent — KYA — is emerging as the agent-layer equivalent of KYC. Unlike KYC, however, KYA for Web3 is necessarily on-chain behavioral rather than documentary. There are no passports in DeFi. Instead, there is transaction history — permanent, public, immutable, and available for scoring without touching any personal data. KYA answers the same fundamental question KYC does — who is this entity, should I trust them? — using behavioral pattern analysis across 20M+ wallet personas trained on confirmed fraud and legitimate address populations.

The regulatory tailwind is real. The EU AI Act , which takes full effect in August 2026, creates documentation and risk assessment requirements for high-risk AI systems. Autonomous agents with financial execution permissions are a clear candidate for high-risk classification. Protocols operating in EU-regulated markets need demonstrable risk controls for agent interactions — Agent Trust Score satisfies that requirement without adding friction for legitimate agents.

The Agentic Commerce Trust Stack: Where Agent Trust Score Fits

The agentic economy has built four of its five required infrastructure layers. The fifth — trust — launched today. Understanding where Agent Trust Score sits in the full stack clarifies both what it does and why no existing protocol addresses the same problem.

This framework — which we call the Agentic Commerce Trust Stack — maps the four distinct problems any agentic commerce protocol must solve before autonomous transactions can happen safely. Each layer answers a different question. Moreover, each layer requires different infrastructure. x402 handles payment rails. ERC-8183 handles disputes after transactions complete. ERC-8004 handles identity registration. Agent Trust Score handles the question that must be answered before any interaction begins: is this agent controlled by someone whose on-chain history warrants the trust implied by autonomous execution?

The stack is not theoretical. Mastercard completed Europe’s first live AI-agent bank payment in 2026. Visa launched an agent-focused payment framework. TON Foundation launched Agentic Wallets in April 2026, allowing AI agents on Telegram to autonomously store and spend funds within user-defined limits. The payment rails are live. The dispute layer is being built. The identity layer has 274,792 registered agents. The trust layer — until today — was the missing piece.

CB Insights Validation: ChainAware in the AI Fraud Prevention Market Map

Context matters for a product launch in a nascent category. ChainAware’s inclusion in the CB Insights AI Fraud Prevention Market Map provides that context clearly. CB Insights mapped 200+ of the most promising companies building AI-powered fraud prevention infrastructure, from deepfake detection to on-chain intelligence and — their exact words — “agentic trust infrastructure.” ChainAware appears in the On-Chain Intelligence category alongside Chainalysis, Elliptic, and TRM Labs.

The CB Insights placement is significant for three reasons. First, it validates the methodology — CB Insights uses Mosaic scores and predictive signals, not self-reported data, to select companies. Second, it identifies “agentic trust infrastructure” as a standalone market segment within fraud prevention — confirming that Agent Trust Score addresses a recognized institutional need, not a speculative niche. Third, it positions ChainAware as the Web3-native player in a category otherwise dominated by forensic analytics firms whose methodology is reactive rather than predictive.

For the full analysis of what the CB Insights placement means for ChainAware’s market position, see our CB Insights market map coverage article. For the competitive landscape of agent trust platforms specifically, see our Agent Trust Infrastructure Race analysis comparing six platforms across 19 capabilities.

What ERC-8004 Gives You — and What It Doesn’t

ERC-8004 is a well-designed standard that solves a specific problem: giving AI agents a standardized, verifiable on-chain identity. Every registered agent receives an ERC-721 NFT representing its identity, a controlling owner wallet, an agent payment wallet, and a URI pointing to its agent card JSON. The registry answers the question “does this agent exist?” cleanly and cryptographically.

The standard does not answer the question “should I trust this agent?” — and the specification explicitly leaves scoring to third parties. This design choice is correct architecturally: the registry should be a neutral data layer, not an opinion engine. However, it means that every protocol integrating ERC-8004 agents is responsible for answering the trust question independently. Most currently do not — they query the registry to confirm the agent exists and proceed. Agent Trust Score is what fills the gap between “agent exists” and “agent is safe to interact with autonomously.”

The Voting-Based Reputation Problem

ERC-8004 also includes a built-in Reputation Registry — a standard interface for peer feedback. On paper, this sounds like a trust mechanism. In practice, it is a manufactured-trust system waiting to be exploited. An operator deploying 50 agent wallets can have each one review every other, generating a full positive reputation history in hours at a cost measured in gas fees. On BSC or Base, that cost is less than a dollar. The result is indistinguishable from genuine reputation on any platform that reads the registry naively.

ChainAware does not read the ERC-8004 Reputation Registry to compute the Agent Trust Score. Instead, we look behind the agent at the behavioral history of the wallets controlling it and funding its controller. That history is immutable — it cannot be manufactured overnight. An operator who rugged three liquidity pools in Q4 2025 and registered 47 agents in Q1 2026 carries that history forward regardless of how many peer reviews the new agents accumulate. For a detailed analysis of why voting-based reputation fails at scale, see our guide to Web3 Wallet Auditing Providers in 2026.

State of the ERC-8004 Registry: First Ever Trust Analysis of 274,792 Agents

ChainAware has indexed the complete ERC-8004 registry — 274,792 agents across Ethereum mainnet, BSC, Base, and Avalanche — and applied the Agent Trust Score to produce the first comprehensive behavioral trust analysis of the agentic economy. The results reveal a striking picture that should concern any DeFi protocol granting autonomous execution access to registry agents.

Trust Score Distribution

The headline finding is stark: more than half of all ERC-8004 registered agents — 51.8% — carry Elevated Risk or Untrusted scores. This is not a marginal tail of bad actors. It is the majority of the registry. Protocols granting autonomous execution access to unscreened ERC-8004 agents are, statistically, granting that access to a population where more than half present material trust risk.

Flag Analysis: What Is Driving Low Scores

• FARM_DETECTED: 58,061 agents (21.1%) — one in five agents belongs to a Sybil fleet where a single operator controls multiple agents
• FEEDER_UNKNOWN: 26,124 agents (9.5%) — nearly 1 in 10 agents has an owner wallet with an obfuscated or untraceable funding source
• EIP7702_DELEGATED: 20,946 agents (7.6%) — the registered owner has delegated control to a secondary address, potentially obscuring the real controller
• FEEDER_CEX_VERIFIED: 2,691 agents (1.0%) — confirmed CEX-funded owners, the strongest legitimacy signal available
• FEEDER_RUG_HISTORY: 741 agents (0.3%) — agents whose owner wallet was funded by a confirmed rug pull operator
• CREATOR_RUG_HISTORY: 59 agents (0.02%) — agents controlled directly by confirmed rug pull creators
• CREATOR_HONEYPOT_HISTORY: 3 agents — agents whose owner has previously created honeypot token contracts

The farm detection finding deserves particular attention. 58,061 agents — 21.1% of the entire registry — are controlled by fleet operators running multiple agents simultaneously, frequently registered in the same block. Individual agent scoring is structurally blind to this pattern. ChainAware detects it because we maintain an owner profile database tracking fleet size across all indexed chains — a capability that requires the full registry index, not just individual agent lookups.

For the full competitive context of how these signals compare to what RNWY, SkyeProfile, AXIS T-Score, and DJD Agent Score provide, see our Agent Trust Infrastructure Race analysis.

The Five Signals Only ChainAware Provides

Agent Trust Score combines signals that no other agent trust platform currently provides. Each one addresses a specific threat model that the other approaches structurally cannot reach. We intentionally do not publish the exact weights, thresholds, or model coefficients behind these signals — doing so would allow bad actors to calibrate their behavior to stay just below each detection threshold. What we do publish are the signal categories and what each one means for your trust decision.

Signal 1: Owner Wallet Behavioral Fraud Score

The owner wallet is the human or entity controlling the agent. ChainAware scores it using a predictive AI model trained on 20M+ wallet personas across Ethereum, BSC, Base, and beyond — achieving 98% fraud detection accuracy. This is not a blacklist check. Rather, it is a forward-looking behavioral prediction: given this wallet’s complete transaction history, what is the probability it will engage in fraudulent activity? The model retrains continuously on new confirmed fraud cases, meaning evasion strategies become stale quickly.

The fraud score is the primary input to the Agent Trust Score — a clean wallet starts at a high baseline, while a fraud-flagged wallet scores low regardless of any other signal. For the full methodology behind the fraud prediction model, see our AI-Powered Blockchain Analysis guide.

Signal 2: Feeder Address Analysis

The feeder address is the wallet that funded the owner. No other agent trust platform traces and scores this signal. ChainAware traces feeder addresses for approximately 38% of indexed agents. The feeder signal has three variants that carry different trust implications. A CEX-verified feeder (Binance, Coinbase, Kraken, OKX withdrawal address) implies the owner passed KYC somewhere upstream — ChainAware’s strongest positive signal. An unknown or obfuscated feeder is itself a risk signal. A feeder with confirmed fraud status applies hard suppression to the final score.

When the feeder address has rug pull or honeypot history in ChainAware’s database, the owner may have deliberately cycled wallets to obscure a fraud track record — the feeder criminal record check is what catches this pattern. For more on how feeder analysis works in practice, see our Forensic vs AI Blockchain Analysis guide.

Signal 3: Criminal Record — Rug Pull and Honeypot History

ChainAware maintains a database built from on-chain liquidity pair history and token audit data spanning over a year of activity. This database records which wallet addresses created pools that subsequently exhibited rug pull patterns, and which wallet addresses previously deployed honeypot token contracts. Before computing the Agent Trust Score, ChainAware cross-references both the owner wallet and the feeder address against this database.

Criminal record signals result in hard caps on the Agent Trust Score that no other signal can override. This is the signal that connects yesterday’s token fraud to today’s agent deployment. An operator who rugged pools on PancakeSwap in Q4 2025 and registered 40 agents in Q1 2026 is caught by this check. No other agent trust platform makes that connection because no other platform maintains a paired rug pull database and cross-references it against agent registry data. For background on how rug pull and honeypot patterns are detected, see our guide to Rug Pull vs Pump and Dump in Web3.

Signal 4: Trust Delegation

Agent payment wallets are frequently fresh addresses created specifically for the agent — they have no transaction history, no counterparty network, and no behavioral record. A naive scoring approach penalises every newly deployed agent regardless of the owner’s reputation, producing low scores for legitimate agents and making the score useless as a gate for new deployments.

ChainAware’s trust delegation mechanism solves this: the owner wallet’s reputation sets a floor for the agent wallet’s effective score. A reputable developer deploying their first agent wallet scores well through delegation. A fraud-flagged owner cannot delegate any meaningful trust — the delegation collapses, and the agent score reflects the owner’s history rather than the wallet’s lack of it.

Signal 5: Fleet-Level Farm Detection

Every competitor in this market scores agents individually. ChainAware maintains an owner profile database tracking agent fleet size across all indexed chains. Owners controlling unusually large numbers of agents — particularly those registered in tight time windows — receive farm modifiers that suppress scores across their entire fleet, regardless of how any individual agent scores in isolation.

This fleet-level view catches the specific agentic commerce attack pattern that individual scoring cannot surface: one operator manufacturing ecosystem depth through a controlled population of agents, each of which appears clean when scored independently. Our data shows 58,061 agents — 21.1% of the entire ERC-8004 registry — are already in this category today.

The Data Moat: Why This Cannot Be Replicated

Agent Trust Score is built on three proprietary data assets accumulated over years of continuous operation. A competitor starting today cannot purchase these assets, compress the time required to build them, or replicate them from publicly available sources alone. Each one compounds in value as it grows — making ChainAware’s intelligence advantage wider over time, not narrower.

20M+ Wallet Personas: Years of Behavioral Training Data

ChainAware’s fraud prediction model is trained on more than 20 million wallet personas across 8 blockchains. Each persona represents a complete behavioral fingerprint — transaction history, timing patterns, counterparty networks, protocol diversity, AML exposure, and dozens of derived features. Building this dataset required years of continuous on-chain data collection, labeling of confirmed fraud cases, and iterative model retraining against real-world fraud outcomes. The result is 98% fraud detection accuracy on held-out test data.

This persona depth cannot be replicated quickly. A new entrant would need years of historical blockchain data, a confirmed fraud label dataset from real fraud cases, and the engineering infrastructure to process and update 20 million behavioral profiles continuously. Furthermore, the model improves as the dataset grows — each new confirmed fraud case sharpens the prediction boundary. ChainAware’s advantage in this dimension compounds daily.

One Year of On-Chain Pair History: The Criminal Record Database

The rug pull and honeypot criminal record check that powers Agent Trust Score’s hardest caps requires a database of historical liquidity pair creation and token audit results — accumulated over more than a year of continuous on-chain monitoring. ChainAware has tracked pair creation across PancakeSwap, Uniswap, and other major DEX venues, cross-referencing creator wallet addresses against confirmed fraud outcomes: pools where liquidity was removed in rug pull patterns, and token contracts that embedded honeypot mechanics preventing buyers from selling.

This database is what catches the serial fraudster who registers new agents after previous fraud campaigns. It connects the rug puller of November 2025 to the agent creator of February 2026 — a connection that exists only if you have the historical data linking both events to the same wallet address. No competitor in the agent trust scoring market currently maintains this database. Furthermore, building it retroactively is impossible: the historical pair data exists on-chain, but the labeling of fraud outcomes requires the passage of time to observe liquidity removal patterns after the fact. For the data behind this detection engine, see our Rug Pull Tracker report and our AI-Based Predictive Fraud Detection guide.

38% Feeder Coverage: The Funding Chain Network

ChainAware traces feeder addresses — the wallets that funded owner wallets — for approximately 38% of indexed ERC-8004 agents. This coverage reflects the on-chain reality: some owner wallets receive funds from multiple sources, some from bridge or faucet infrastructure that does not produce a single attributable feeder, and some from deliberately obfuscated multi-hop paths. The 38% with traceable single-hop feeders represents the population where the funding chain reveals meaningful intelligence.

Feeder coverage is itself a compounding asset. As ChainAware indexes more agents and more wallet interactions over time, the feeder network graph grows denser — revealing connections between previously unlinked wallet clusters. An operator who cycled through three funding wallets across different campaigns may appear disconnected today but becomes linkable as the transaction graph accumulates more data points. Additionally, the CEX label database — identifying which addresses are verified exchange hot wallets — improves as exchange infrastructure evolves and new verified addresses are confirmed. The feeder signal is the one no competitor has reached because building it requires both the feeder tracing infrastructure and the fraud intelligence to score what you find at the other end of the funding chain.

Why the Moat Compounds

Each of these three data assets improves as it grows larger and older. More wallet personas means better fraud prediction boundary precision. More pair history means more confirmed criminal records attached to operator wallets. More feeder coverage means more fraud connections surfaced across the registry. A competitor starting today with identical engineering resources would still need years to catch up — and by the time they did, ChainAware’s assets would be proportionally larger still. This is the definition of a compounding data moat: the advantage is not a snapshot, it is a trajectory.

How the Agent Trust Score Works

The Agent Trust Score uses a multi-layer pipeline that combines owner fraud probability, trust delegation, feeder analysis, farm detection, and criminal record hard caps into a single 0-1000 score. Each layer applies a specific transformation before passing the result to the next.

We intentionally do not publish the exact weights, thresholds, or multipliers used in each layer. Publishing precise thresholds would allow bad actors to calibrate their behavior to stay just below each detection cap — which would directly undermine the system’s ability to catch sophisticated fraud. Our scoring model retrains continuously on new confirmed fraud patterns. What works as evasion today becomes detectable tomorrow as the model updates.

What we do publish is what each layer measures and why it matters:

• Owner fraud probability — the primary driver of the base score. A clean owner wallet starts at a high baseline. A fraud-flagged wallet scores low regardless of any other input.
• Experience and on-chain history — a modest bonus for owners with genuine DeFi activity over time. Experience adds to the score but cannot compensate for high fraud probability.
• Trust delegation — lifts a fresh agent wallet when the owner has strong history. Collapses when the owner is fraud-flagged.
• Feeder modifier — adjusts the score based on who funded the owner. CEX-verified feeders boost the score. Unknown or fraud feeders suppress it.
• Farm modifier — suppresses scores across an entire fleet when the owner controls an unusually large number of agents or registers them in bulk.
• Criminal record hard caps — override all other signals when confirmed rug pull or honeypot history is found. These caps are absolute and cannot be offset by positive signals elsewhere.

The result is a score between 0 and 1000. The full methodology overview — covering signal categories without exposing exact parameters — is available at chainaware.ai/learn/agent-trust-score.

Score Tiers: What Each One Means for Protocol Builders

The Agent Trust Score maps to five tiers on the 0-1000 scale. Each tier carries a specific operational recommendation for DeFi protocol builders. The right threshold for your protocol depends on the risk profile of the transactions involved — a high-value lending protocol and a low-value DEX swap use different thresholds against the same score.

Sovereign (800-1000) — Full Autonomous Access

Sovereign agents have strong owner fraud probability, a clean or CEX-verified feeder address, no criminal record signals, and no farm detection flags. Sovereign is appropriate for high-value autonomous operations: large-value lending, treasury management, and governance participation with financial consequences. Protocols can grant Sovereign agents the same execution permissions they would grant to established protocol participants.

Trusted (600-799) — Standard Integration

Trusted agents have strong owner fraud probability, a generally clean feeder, and no hard-cap signals. Trusted is appropriate for standard DeFi integrations — trading agents, yield optimisers, and automated compliance workflows where individual transaction risk is moderate and human monitoring is available as a backstop.

Provisional (400-599) — Monitoring Required

Provisional agents show mixed signals: moderate fraud probability, unknown feeder, or a fresh payment wallet. Provisional agents should not receive unsupervised autonomous execution access for high-value operations. However, they are appropriate for lower-risk automated workflows with active monitoring — read-only queries, low-value token swaps, or agentic onboarding flows where individual transaction size is capped.

Elevated Risk (200-399) — Restricted Access Only

Elevated Risk agents carry weak owner history, obfuscated feeders, or farm detection signals. These agents should not be permitted autonomous financial execution. If your protocol needs to serve Elevated Risk agents — for example in a permissionless DEX context — transaction size limits, velocity caps, and real-time monitoring should all be active simultaneously.

Untrusted (0-199) — Block

Untrusted agents carry active fraud signals, confirmed rug pull or honeypot history, confirmed farm detection, sanctioned address exposure, or repeat offender status. These agents should be blocked at the access control layer before any transaction reaches the execution layer. The score is not borderline — it reflects definitive fraud signals from immutable on-chain history. For context on the types of on-chain fraud that produce Untrusted scores, see our AI-Based Predictive Fraud Detection guide.

The Compounding Risk of Unscreened Agent Access

Human-initiated fraud and agent-initiated fraud differ in one fundamental operational characteristic: velocity. A fraudulent human interacting with your protocol manually can execute perhaps dozens of interactions before detection. A fraudulent agent operating autonomously executes thousands of interactions in the same period — at machine speed, without sleep, without rate-limit awareness unless you specifically implement it, and with the full behavioral sophistication of the AI model powering it.

Therefore, the cost of a single misidentified agent is not comparable to the cost of a single misidentified human user. The exposure scales with the agent’s operational capacity. A lending protocol that grants a fraudulent agent autonomous execution access for six hours faces losses that scale with protocol TVL and agent transaction rate. Traditional fraud detection tools are particularly poorly suited to this environment. Rule-based systems flag agent behavior as suspicious because agents naturally exhibit the patterns those rules target: high velocity, cross-category activity, unusual timing distributions. Consequently, you end up blocking legitimate agents while missing sophisticated fraudulent ones engineered to mimic human behavioral patterns.

The Farm Attack at Scale

Agent farming is a specific attack pattern that compounds differently from individual fraud. Consider the operational math: one operator registers a large fleet of agents across BSC and Base, each appearing clean individually. Each agent interacts with your protocol at modest frequency. Collectively, that generates thousands of agent interactions per day from a single coordinated operator. Furthermore, because each agent appears to be an independent participant, your protocol’s per-user rate limits and monitoring thresholds are never triggered on any single agent. Across a week, you may process tens of thousands of transactions from what is effectively a single fraud operation — without any individual agent exceeding your anomaly detection thresholds.

ChainAware’s fleet-level farm detection catches this pattern before the first transaction. When agents from the same owner wallet query the Agent Trust Score API, they return FARM_DETECTED — regardless of how clean any individual agent appears. The trust decision happens at the fleet level, not the individual agent level, because the fraud pattern exists at the fleet level. Our data shows 58,061 agents — 21.1% of the entire ERC-8004 registry — already carry the FARM_DETECTED flag. For the broader context of how predictive models compare to forensic analytics for this class of threat, see our Forensic vs AI analysis guide.

The Serial Fraudster Rotation Pattern

A second compounding risk pattern is the serial fraudster who rotates wallet identities between campaigns. The typical sequence: Wallet A runs a rug pull campaign, extracts funds, and becomes known to forensic databases. Wallet B is then created fresh, funded from Wallet A — the feeder relationship recorded immutably on-chain — and used to register new agents on ERC-8004. Every platform that scores only the agent or the current owner wallet sees a clean Wallet B. ChainAware traces the feeder and scores Wallet A, which carries the rug pull history. Wallet B’s agents receive suppressed scores regardless of how clean Wallet B’s own transaction history appears.

The data confirms this pattern is live in the current registry: 741 agents carry the FEEDER_RUG_HISTORY flag, meaning their owner wallet was funded by a confirmed rug pull operator. These 741 agents appear completely clean to any platform that does not trace the feeder chain. They represent confirmed serial fraudsters using fresh wallets to continue operations under new identities. For the macro picture of rug pull losses, see our Rug Pull Tracker report.

Integration Guide for DeFi Protocol Builders

Adding Agent Trust Score to a DeFi protocol requires one additional step between the ERC-8004 registry lookup and transaction execution. That step takes under 100ms and returns a structured output the protocol’s access control layer can act on directly.

The Trust-Aware Integration Pattern

Agent initiates transaction
  ↓
Resolve agent_id → owner_address + agent_wallet (ERC-8004 registry)
  ↓
GET /erc8004/agent/{chain_id}/{agent_id}/trust-score
  ↓
Response:
{
  "agent_trust_score": 882,
  "tier": "Sovereign",
  "flags": ["FEEDER_CEX_VERIFIED"],
  "scored_at": "2026-07-12T09:14:00Z"
}
  ↓
score ≥ protocol_threshold → execute
score < protocol_threshold → reject or route to human review

The threshold is your decision. Different use cases warrant different risk tolerances:

MCP Integration

Agent Trust Score integrates natively with ChainAware’s Prediction MCP server. Any Claude-based DeFi agent can call agent trust scoring as a native tool call without custom API integration code. For teams building on the MCP stack, our library of 32 ready-made agents includes agent verification logic that can be cloned and deployed in under 30 minutes. For DeFi credit scoring alongside agent trust verification, see our DeFi Credit Score Platform Comparison.

Latency and Rate Limits

Agent Trust Score returns results in under 100ms for pre-indexed agents. ChainAware pre-indexes the full ERC-8004 registry continuously, so the vast majority of queries return cached scores without a live computation cycle. Enterprise plans include dedicated rate limits, SLA guarantees, webhook notifications for score changes, and a dedicated integration engineer. Free tier covers the first 1,000 queries per month with no API key required for public indexed agents. For the AML and MiCA compliance context, see our DeFi Compliance and KYT/AML guide.

Guide for Agent Creators: How Your Score Is Determined

If you are deploying ERC-8004 agents, understanding how your agents are scored enables you to optimize for legitimate trust signals. The score reflects real behavioral history — it cannot be manufactured, but it can be legitimately improved over time through genuine on-chain activity.

What Improves Your Score

The primary lever is your owner wallet’s fraud probability — keeping it low requires genuine, diverse on-chain activity over time. Specifically, interacting with a range of DeFi protocols (lending, trading, staking, bridging) across multiple months produces a behavioral profile that scores well. Additionally, using a CEX withdrawal as your owner wallet’s funding source (Binance, Coinbase, Kraken) immediately flags your agent as FEEDER_CEX_VERIFIED and applies the maximum feeder boost. Furthermore, deploying agents with consistent patterns over time rather than in bulk avoids farm detection signals.

What Permanently Caps Your Score

Criminal record signals are immutable. A confirmed rug pull or honeypot in your on-chain history permanently caps your agents — regardless of how clean your current behavior is. These caps exist because the on-chain events that trigger them are permanent. A wallet that drained a liquidity pool cannot remove that event from the blockchain. Consequently, there is no path to a high Agent Trust Score for operators with confirmed fraud history. For context on how rug pull and honeypot patterns are detected, see our Rug Pull Detector learn page.

How Agent Trust Score Compares to Other Platforms

Agent trust scoring is a new market with several emerging approaches. Each platform answers a different question about the same agent. Understanding the distinction matters for protocol builders choosing a trust gating system — selecting the wrong approach means the specific fraud pattern you face is precisely the one your chosen platform cannot detect.

RNWY is the most established competitor and provides strong review quality analysis and sybil detection. However, their core methodology solves fake reviews — not fake owners. ChainAware solves fake owners. These are complementary approaches: DeFi protocols can use both simultaneously, with RNWY for reputation display and ChainAware as the fraud intelligence gate before execution. For the full 19-capability comparison across all six platforms, see our Agent Trust Infrastructure Race.

Frequently Asked Questions

Which chains does Agent Trust Score cover?

Agent Trust Score indexes ERC-8004 agents across Ethereum mainnet, BSC (BNB Chain), Base, and Avalanche C-Chain, with Mantle in progress. The owner wallet and feeder scoring draws on ChainAware’s broader behavioral intelligence database, which covers 8 blockchains including Polygon, TON, TRON, and HAQQ. Chain coverage expands continuously as new ERC-8004 registry deployments go live.

Does it require any personal data or KYC?

No. Agent Trust Score is derived entirely from public on-chain data. No personal information is collected, no identity verification is required, and no data is stored beyond what is already publicly available on the blockchain. The product is compatible with DeFi’s privacy-first ethos and compliant with GDPR by design.

Can an agent improve its score over time?

Yes — through the owner wallet’s behavioral history, not through the agent wallet itself. As the owner wallet accumulates genuine on-chain experience and maintains a clean fraud probability score, the Agent Trust Score improves. However, criminal record signals are permanent — they do not improve over time because the underlying on-chain events are immutable.

Why don’t you publish the exact scoring weights?

Publishing exact thresholds and weights would allow bad actors to calibrate their behavior to stay just below each detection cap. This is the same reason credit scoring agencies (FICO, Experian) publish the signal categories they use — payment history, credit utilization, account age — without publishing the precise weights. Our fraud model retrains continuously, so even partial knowledge of current parameters becomes stale quickly. The signal categories are published at chainaware.ai/learn/agent-trust-score. The exact parameters remain proprietary.

What happens when an agent is transferred to a new owner?

ERC-8004 agents are ERC-721 NFTs and can be transferred between wallets. When ChainAware detects an ownership transfer, the Agent Trust Score recalculates using the new owner wallet’s behavioral history. The score tracks the current controlling entity, not the original registrant. An agent cannot inherit a previous owner’s strong score after transfer.

How does EIP-7702 delegation affect the score?

When EIP-7702 delegation is detected, ChainAware scores both the registered owner and the delegate address. The Agent Trust Score takes the less favorable of the two results. Agents with EIP-7702 delegation are flagged explicitly in the API response as EIP7702_DELEGATED, giving protocol builders the option to apply additional scrutiny regardless of the final numerical score. Currently 7.6% of indexed agents — 20,946 agents — use EIP-7702 delegation.

How is Agent Trust Score different from Wallet Reputation Score?

Both use the same 0-1000 scale, making them directly comparable. However, Agent Trust Score applies the scoring to multiple addresses simultaneously — owner wallet, agent wallet, and feeder address — and combines them using trust delegation logic and fleet-level farm detection signals that do not exist in the standalone Wallet Reputation Score. Additionally, Agent Trust Score cross-references the criminal record database for rug pull and honeypot history. For the Wallet Reputation Score methodology, see our Wallet Auditor learn page.

What is the free tier?

The free tier covers 1,000 queries per month for indexed public agents on Ethereum, BSC, Base, and Avalanche. No API key required to start — simply query beta.chainaware.ai/agent-trust-score with any agent ID, owner address, or agent wallet. Enterprise plans with higher rate limits, SLA, webhooks, and dedicated integration support are available via chainaware.ai/schedule.

Further Reading

• Agent Trust Score — Methodology Overview
• The Agent Trust Infrastructure Race: Six Platforms Compared
• The First Step in Agentic Commerce Isn’t Integration. It’s Trust.
• CB Insights AI Fraud Prevention Market Map — ChainAware Coverage
• AI-Powered Blockchain Analysis for Crypto Security 2026
• Rug Pull Tracker — $569M on PancakeSwap V2
• DeFi Compliance: Complete KYT and AML Guide 2026
• Web3 Wallet Auditing Providers in 2026 — Complete Comparison
• Forensic vs AI-Powered Blockchain Analysis
• DeFi Credit Score Platforms Compared 2026
• Prediction MCP Setup Guide
• 32 Ready-Made Agents

ChainAware.ai is the Web3 Agentic Growth Infrastructure — behavioral intelligence for DeFi protocols, AI agents, and individual crypto users. 20M+ wallet personas, 98% fraud detection accuracy, <100ms API latency across 8 blockchains. Named in CB Insights’ AI Fraud Prevention Market Map in the On-Chain Intelligence category. chainaware.ai

The post ChainAware Launches Agent Trust Score – On-Chain Trust Scoring for the Agentic Commerce Era first appeared on ChainAware.ai.

The smart contract security audit market is broken. Manual audits cost $5,000 to $150,000 and take weeks. Meanwhile, thousands of new tokens launch every single day – and the vast majority of retail investors check exactly nothing before they buy. On Binance Smart Chain alone, 95% of new liquidity pools end in rug pulls. The tools that exist – GoPlus, TokenSniffer, Honeypot.is – catch the obvious scams. They completely miss the sophisticated ones.

Today, ChainAware is changing that. Token Audit is live: 127 automated security checks across 9 analysis modules, powered by deep code analysis and ChainAware’s behavioral intelligence layer. To validate the system, we ran it against the top 10,000 tokens on CoinGecko, sorted by market capitalization. Those are not random memecoins – they are the most-traded, most-held tokens in crypto. The results are alarming.

This article presents every finding. Specifically, you will learn what the most dangerous patterns look like at scale, which chains produce the highest risk concentrations, and why the tools you are currently using are systematically missing the threats that matter most.

The Study: 10,000 CoinGecko Tokens, 13,000 Audits, 6 Chains

The dataset covers the top 10,000 tokens by market capitalization on CoinGecko as of July 2026. Because many tokens exist simultaneously on multiple blockchains – USDT, for example, runs on Ethereum, BSC, Polygon, Base, and Arbitrum – the total audit count reaches 12,998 individual contract audits across 6 chains. Consequently, each audit is independent: the same token contract deployed on ETH and BSC receives two separate audits, because the contract code, liquidity structure, and ownership configuration can differ significantly between deployments.

Furthermore, this is not a random sample of newly launched tokens. These are established, widely-traded assets – the tokens that appear in your wallet app, on DeFi dashboards, and in portfolio trackers. If findings this severe appear in the top 10,000 by market cap, the situation in the broader universe of hundreds of thousands of tokens is considerably worse.

Each audit runs 127 checks across 9 modules: Ownership, Supply, Liquidity, Transfer, Approve, Permit, Pausability, Reentrancy, and the proprietary Honeypot Pattern module. Three detection layers underpin each audit: deep code analysis for semantic code-level findings, direct on-chain RPC calls for live state verification, and ChainAware’s behavioral database for creator and LP trust scoring. Results are stored in a structured database with one scalar column per finding – enabling the statistical analysis below.

Chain Distribution

Token classification matters for accurate results. Reflection tokens, rebasing tokens, ERC-4626 vault tokens, and bridge tokens all have non-standard transfer mechanics that would trigger false positives in a naive static analysis tool. Token Audit identifies these token types using dedicated classifiers and adjusts its findings accordingly – for example, a reflection token legitimately fails the transfer conservation check by design, and Token Audit documents this distinction rather than incorrectly flagging it as a theft vector. Accurate false-positive management at scale is essential for a tool that will be embedded in high-volume platform integrations, where a false positive on a major legitimate token destroys user trust far faster than a false negative on an obscure scam.

Ethereum leads by audit count, reflecting the concentration of established DeFi protocols on the oldest EVM chain. BSC’s 26.7% share is notable: despite hosting a smaller share of top-10,000 market cap tokens, it accounts for a disproportionate share of the worst findings – as the chain-by-chain breakdown below demonstrates.

The Headline Results: 55% of the Top 10,000 Tokens Are High Risk

The single most important finding from this study is also the most unsettling one. Among the top 10,000 tokens by market cap – the most established, most liquid, most widely held tokens in the entire crypto market – 55.2% receive a HIGH RISK verdict from ChainAware Token Audit.

Only 18.7% of audited tokens receive a CLEAN verdict – meaning they pass all critical security checks, have no meaningful rug pull vectors, and carry no significant code-level risks. Put another way, more than 4 in every 5 tokens in the top 10,000 carry some level of meaningful security concern.

These numbers require context. HIGH RISK does not automatically mean the token is a scam. Many HIGH RISK findings reflect architectural choices that are widespread in legitimate DeFi protocols: uncapped mint functions controlled by governance contracts, upgradeable proxy architectures managed by multisigs, or LP positions not locked because the team chose a different treasury structure. However, HIGH RISK does mean that the token contract contains mechanisms a malicious actor could use to harm investors – and that investors deserve to know about them before committing capital.

Moreover, 131 confirmed honeypots in the top 10,000 is not a small number. These are tokens where the Token Audit’s simulation analysis module confirmed that you can buy – but cannot sell. Twelve of those honeypots were found on Ethereum, the chain most associated with institutional quality and regulatory oversight. The assumption that “top 10,000 by market cap = safe” is demonstrably false.

Results by Chain: BSC Is the Most Dangerous

BSC stands out dramatically. Only 7.6% of BSC token deployments in the top 10,000 are clean – the lowest of any chain in the study. Meanwhile, 68.3% are high risk and another 23.0% are suspicious. Combined, that means 91.3% of top-10,000 BSC tokens carry some security concern. This finding is consistent with BSC’s broader reputation: Chainalysis research identifies BSC as hosting approximately 71% of all rug pull scams globally, driven by lower transaction fees that make deploying fraudulent contracts nearly cost-free.

Base, by contrast, is the cleanest chain in the study at 22.5% clean. Its 40.0% high risk rate reflects a newer, more curated DeFi ecosystem. Nevertheless, 40% high risk across Base’s top tokens is not a reassuring figure.

What Drives the Risk: The Two Dominant Findings

Two findings appear far more frequently than any other in the dataset, together driving 76% of all HIGH RISK verdicts. Understanding them is essential to understanding why so many established tokens carry elevated risk scores.

Finding #1: 35.9% of Tokens Have No Mint Cap (INV_S2_NO_MINT_CAP)

The most common single finding across the entire dataset: 4,668 tokens – 35.9% of all audited contracts – have a mint function with no enforceable supply cap. This means the token’s owner, governance contract, or admin address can create unlimited new tokens at any time, diluting every existing holder’s position to zero.

Critically, this finding appears almost exclusively in HIGH RISK verdicts. Cross-referencing the two columns shows that zero CLEAN tokens carry NO_MINT_CAP – a perfect separation. Every CLEAN token in the dataset either has no mint function at all or has a mint function with an immutable, on-chain cap. The 4,668 NO_MINT_CAP tokens are split between HIGH RISK (4,439) and HONEYPOT (75), with only 154 in the SUSPICIOUS tier.

For investors, the implication is straightforward: a token with an uncapped mint function carries a structural risk that no amount of team credibility or market cap size eliminates. The inflation vector exists regardless of whether the team currently intends to use it.

Finding #2: 34.4% of Tokens Have No Timelock on Privileged Functions (INV_O6_NO_TIMELOCK)

The second-most common finding: 4,470 tokens – 34.4% – have privileged administrative functions (ownership transfer, fee modification, upgrade execution, mint authorization) with no timelock. A timelock requires that any privileged action be announced on-chain and delayed by a minimum period – typically 24 to 72 hours – giving the community time to react if a malicious or compromised admin executes a dangerous change.

Without a timelock, a single administrative transaction can drain a protocol, rug liquidity, or convert a functioning token into a honeypot in a single block. The attacker’s advantage is complete: investors cannot react to changes they cannot anticipate. Adding a timelock costs developers essentially nothing but a few lines of Solidity – which makes its absence in 34.4% of the top-10,000 tokens particularly striking.

Together, NO_MINT_CAP and NO_TIMELOCK account for the overwhelming majority of high-risk verdicts in this dataset. Both findings are invisible to honeypot simulation tools like Honeypot.is – which only checks whether a sell transaction reverts. Furthermore, both are absent from the GoPlus Security API’s detection layer. ChainAware’s Ownership and Supply modules specifically scan for these patterns using deep code analysis, which can trace through function call chains to confirm whether an enforceable cap or delay mechanism actually exists – not merely whether the contract declares one.

Liquidity Risk: 25.7% of Tokens Have Completely Unlocked LP

Beyond the supply and ownership findings, the Liquidity module produced the study’s most operationally urgent results. Liquidity is the primary signal that drives 42% of all verdicts – more than any other module – because liquidity risk is both the most directly dangerous and the most immediately verifiable.

The 25.7% unlocked LP figure is particularly significant. When LP tokens remain in the deployer’s wallet, the entire liquidity backing the token can be removed in a single transaction. Every investor who holds the token is exposed to total loss within one block. The deployer may have committed publicly to never removing liquidity – but without an on-chain lock, that commitment is entirely unenforceable. For how ChainAware detects LP lock status across both V2 (ERC-20 LP tokens) and V3 (NFT positions), see the Liquidity Verification module documentation.

Notably, liquidity lock expiry detection – finding INV_L3_LOCK_EXPIRED – currently has zero hits in the dataset. This finding detects LP locks that have already expired but the associated tokens have not yet been removed. Its absence likely reflects the study’s population: tokens with expired locks often appear after rug pulls have occurred, meaning the token may have been delisted or the pool may have been drained before it entered the CoinGecko top-10,000 dataset.

Confirmed Honeypots: 131 Tokens Where You Can Buy But Cannot Sell

Token Audit’s simulation analysis module forks the relevant blockchain, executes a real buy transaction inside the fork, then attempts a sell. When the sell reverts – meaning the token architecture actively prevents investors from exiting their positions – the verdict is HONEYPOT. This study confirmed 131 honeypots across the top 10,000 CoinGecko tokens.

Ethereum’s 59 confirmed honeypots deserve special attention. The assumption that Ethereum’s higher gas costs and more sophisticated user base filter out honeypot contracts is not supported by this data. Sophisticated honeypots on Ethereum often work precisely because they look legitimate: verified source code, reasonable tax rates, functioning buy mechanics, and professional-looking documentation. The sell block is implemented deep in the transfer call graph – typically using assembly instructions or layered delegation patterns that simple rule-based scanners do not detect.

What Makes a Honeypot: The Three Strongest Signals

Signal 1: hp_CUSTOM_TRANSFER_ENTRY_POINT – Present in 63% of confirmed honeypots (correlation +0.34). This finding fires when the token contract routes transfer calls through a non-standard function before reaching the standard _transfer implementation. Custom entry points are the primary mechanism honeypot developers use to insert sell-blocking logic while keeping the standard ERC-20 interface intact.

Signal 2: hp_UNEXPECTED_EVENTS_IN_TRANSFER – The single highest-correlation honeypot predictor at +0.46, present in 50% of confirmed honeypots. When a transfer function emits events beyond the standard Transfer(from, to, amount) required by ERC-20, it almost always indicates hidden logic inserting itself into the transfer path.

Signal 3: hp_LAYERED_TRANSFER_DELEGATION – Present in 53% of confirmed honeypots and 10.2% of all tokens. Layered delegation means the transfer function calls internal functions that call further internal functions, each potentially adding conditions. Professional honeypots use five or six levels specifically to bury the sell-blocking condition deep enough that automated scanners trace only the outer layers. For how ChainAware’s transfer invariant checking works, see the Transfer Verification documentation.

What Only ChainAware Finds: The Sophisticated Threats

The most significant contribution of this study is not the headline numbers – it is the class of threats that appear in this dataset and cannot be detected by any competing automated tool. ChainAware Token Audit runs 127 checks. Competitors like GoPlus run approximately 40. CertiK Skynet’s free Token Scan runs 19. The gap between those check counts corresponds directly to classes of threat that are invisible to current market-standard tools.

Transfer Conservation Analysis: The Silent Value Drain

Token Audit’s most technically distinctive check is Transfer Conservation (INV_T1_CONSERVATION_FAIL): the invariant that when Alice transfers 100 tokens to Bob, Alice’s balance decreases by exactly 100 and Bob’s balance increases by exactly 100. If sender_lost does not equal recipient_gained, value is being silently diverted – typically to a hidden fee recipient not disclosed anywhere in the token’s interface. Conservation-failing tokens pass every honeypot simulation test. Honeypot.is returns CLEAN. GoPlus returns CLEAN. The investor loses capital on each trade while the token technically allows selling.

The Phantom Balance variant (INV_T5_PHANTOM_BALANCEOF) – found in 5 tokens – is even more sophisticated. The token maintains two separate balance mappings: one that balanceOf() reads and displays to the investor, and a different one that _transfer() actually debits. Your wallet shows you holding 10,000 tokens while the transfer mechanism has already marked your real balance as zero. For the full invariant specification, see the Transfer Invariants documentation.

Permit Correctness: The EIP-2612 Attack Surface

EIP-2612 permit() is implemented in 30% of tokens in this dataset (3,903 tokens). No automated scanner other than ChainAware checks whether the permit implementation is actually correct. Finding INV_P7_PRELOADED_PERMIT – a constructor-time unlimited approval grant – appears in 21 tokens. These 21 tokens allow the deployer to drain any holder’s position at any time using a signature created before any investor bought the token. For how ChainAware detects permit vulnerabilities, see the Permit Verification module.

Approve Security: The Transitive Attack

ChainAware’s Approve module traces the complete call graph of approve() – catching tokens where calling approve(spender, 1000) also silently writes the caller’s balance to zero as a hidden side effect. Finding INV1_EXTRA_STATE_WRITE appears in 63 tokens. INV3_EXTERNAL_CALL_IN_APPROVE appears in 28 tokens. Both require deep code analysis. Neither GoPlus, TokenSniffer, CertiK Skynet, nor De.Fi Scanner runs this analysis. See the Approve Verification documentation.

Reentrancy Analysis

ChainAware is the only automated token scanner that includes reentrancy detection. This study found 540 tokens with no reentrancy guard (INV_R2_NO_REENTRANCY_GUARD), 485 tokens using legacy ETH transfer patterns vulnerable to callback exploitation (INV_R6_ETH_TRANSFER_LEGACY), and 48 tokens with read-only reentrancy exposure (INV_R5_READONLY_REENTRANCY). According to the OWASP Smart Contract Top 10, reentrancy remains one of the most exploited vulnerability categories in DeFi. See the Reentrancy Verification documentation.

Proxy Analysis: 13.2% of Tokens Are Upgradeable Contracts

Token Audit detected proxy contracts in 1,865 tokens – 14.3% of all audited contracts. More importantly, it classifies each proxy by who controls the upgrade function, producing a six-tier risk assessment for the upgrade authority.

Tier	Upgrade Control	Tokens	% of Proxies	Risk
EOA-Controlled	Single private key	139	7.5%	🔴 Critical
Unknown Auth	Cannot be resolved	383	20.5%	🟠 High
Contract-Controlled	DAO / protocol governance	1,152	61.8%	🟡 Medium
Multisig-Controlled	Multiple required signers	29	1.6%	🟢 Low
Timelock-Controlled	Delayed on-chain execution	9	0.5%	🟢 Lowest
UUPS Locked / Renounced	Upgrade permanently disabled	153	8.2%	Immutable

Among all proxy findings, the 139 EOA-controlled proxies represent the most urgent concern. These tokens are upgradeable by a single private key – no multisig, no governance vote, no timelock delay. One transaction from one address can replace the entire contract implementation. BSC accounts for 75 of the 139 EOA-controlled proxies – 54% of the most dangerous proxy tier on less than a third of the token count. For how ChainAware classifies proxy types, see the Ownership Verification module documentation.

Risk Score Analysis: What the Numbers Say at Scale

Liquidity and Supply together drive 76% of all verdicts. The 2.8% driven by Approve and 1.8% by Reentrancy represent high-value findings that no competitor detects. Those 599 verdicts cover the sophisticated operators who invest in clean-looking code specifically to pass GoPlus and TokenSniffer while hiding more subtle attack vectors.

How Token Audit Compares to Existing Tools

Security Check	GoPlus	TokenSniffer	CertiK Skynet	Honeypot.is	ChainAware
Honeypot simulation
Mint capability				❌	+ hidden mint + cap quality
LP lock status			❌	❌	(V2 + V3 NFT positions)
Timelock absence check	❌	❌	❌	❌	Unique
Approve() call graph analysis	❌	❌	❌	❌	Unique
Transfer conservation invariant	❌	❌	❌	❌	Unique
Phantom balanceOf detection	❌	❌	❌	❌	Unique
Permit() correctness (EIP-2612)	❌	❌	❌	❌	Unique
Reentrancy analysis	❌	❌	❌	❌	Unique
Creator behavioral Trust Score	❌	❌	❌	❌	Unique
LP provider Trust Scores	❌	❌	❌	❌	Unique

GoPlus Security is the market standard, averaging 717 million monthly API calls in 2025. Its coverage is broad but rule-based rather than semantic. The approve transitive attack, phantom balance exploit, permit preload, and reentrancy vectors are all invisible to GoPlus’s current architecture. For how ChainAware fits into the broader DeFi security ecosystem, see our Rug Pull Detection Tools comparison and DeFi Compliance Tools guide.

The Behavioral Layer: What Code Analysis Cannot See

Code analysis answers one question: does this contract contain dangerous mechanisms? It cannot answer a more important one: does the person who deployed this contract intend to use those mechanisms maliciously?

This distinction matters because the most dangerous operators specifically invest in clean-looking code. A professional rug pull team in 2026 runs deep code analysis before deploying, checks their own contract against GoPlus, and removes every pattern that produces a red flag. They keep the mint function but make it look like a governance-controlled feature. They leave the LP unlocked but explain it as a treasury management decision. The code passes every automated check. Then, after accumulating enough liquidity, they execute.

ChainAware’s behavioral Trust Score system operates on a fundamentally different signal: the on-chain history of every wallet that deployed the contract and every wallet that provided liquidity. A deployer whose previous contracts ended in rug pulls carries that history regardless of how clean the new contract looks. An LP provider who has removed liquidity from multiple projects within 30 days of launch carries that behavioral signature regardless of how long they have held the current position.

These behavioral signals draw on ChainAware’s core fraud detection infrastructure – the same system that achieves 98% fraud prediction accuracy across 20 million+ wallet behavioral profiles. Combined with the code-level findings from Token Audit’s nine modules, the result is the only token security tool that catches both the technical vulnerability and the operator intent simultaneously. For the full behavioral intelligence methodology, see What Are Web3 Personas and the Fraud Detector documentation.

The Data Moat: Why Token Audit Cannot Be Replicated

Token Audit is built on three proprietary data assets accumulated over years of continuous operation. A competitor starting today cannot purchase these assets, compress the time required to build them, or replicate them from publicly available sources alone. Each one directly enables detection capabilities that require the asset to exist before the analysis can run – meaning the gap between ChainAware and any new entrant widens over time rather than narrowing.

20M+ Wallet Personas: The Behavioral Trust Score Foundation

Every Token Audit includes a creator behavioral Trust Score and LP provider Trust Scores – signals that no competing token scanner offers. These scores draw on ChainAware’s database of more than 20 million wallet behavioral profiles accumulated across 8 blockchains. Each profile represents a complete behavioral fingerprint: transaction history, timing patterns, counterparty networks, protocol diversity, AML exposure, and dozens of derived features trained against confirmed fraud outcomes. The result is 98% fraud prediction accuracy on held-out test data.

This persona depth is what makes the behavioral layer meaningful. A deployer whose previous contracts ended in rug pulls carries that history as a permanent behavioral signal – regardless of how clean the new contract code looks. Without the 20M+ persona database, the behavioral Trust Score would be a near-zero confidence interval. Building that database required years of continuous on-chain data collection and iterative retraining against real-world fraud cases. A new entrant cannot compress that timeline. Furthermore, the model retrains continuously on new confirmed fraud cases – meaning the behavioral edge compounds as ChainAware observes more fraud patterns than any competitor accumulating data from a cold start.

One Year of On-Chain Pair History: The Criminal Record Database

Token Audit’s creator Trust Score cross-references the token deployer’s wallet address against ChainAware’s database of confirmed rug pull and honeypot operators – a database built from more than a year of continuous monitoring of liquidity pair creation and removal events across PancakeSwap, Uniswap, and other major DEX venues. This database records which wallet addresses created pools that subsequently exhibited rug pull patterns, and which wallet addresses previously deployed honeypot token contracts.

This is the data asset that catches the serial scammer deploying a new token after previous campaigns. The rug puller of Q4 2025 is registered as a known criminal in ChainAware’s pair history database. When they deploy a new token in Q1 2026, Token Audit flags the creator wallet immediately – regardless of how clean the new contract code appears. No competitor runs this check because no competitor maintains a paired rug pull database cross-referenced against token deployer wallets. Building it retroactively is also impossible: identifying fraud outcomes requires the passage of time to observe liquidity removal patterns after the fact. The database is a one-year head start that cannot be bought or downloaded. For the data behind this detection layer, see our Rug Pull Tracker report.

Deep Code Analysis Infrastructure: The Semantic Engine Behind 127 Checks

The nine analysis modules that produce Token Audit’s unique findings – approve call graph analysis, transfer conservation invariants, phantom balance detection, permit correctness checking, and reentrancy analysis – all depend on a semantic code analysis infrastructure built specifically for EVM token analysis. It handles Solidity’s inheritance chains, proxy delegation patterns, assembly blocks within Solidity functions, and the non-standard token architectures (reflection, rebasing, ERC-4626 vault tokens) that cause false positives in naive static analysis tools.

Building this infrastructure required years of engineering investment. Every EVM edge case – from DELEGATECALL chains that must be traced across contract boundaries, to assembly-level balance manipulation that bypasses Solidity’s type system, to the layered transfer delegation patterns used by professional honeypot developers – required specific detection logic designed from first principles. The result is a scanner that runs 127 checks in a median of 11.3 seconds across any EVM-compatible contract. That combination of depth and speed is what enables the 9 unique findings in this study that no competitor detects. A new entrant replicating this infrastructure from scratch would need years of engineering time and a corpus of real fraud contracts to validate against – both of which ChainAware has already invested. For the competitive context, see our Forensic vs AI-Powered Blockchain Analysis guide.

Why the Moat Compounds

Each of these three assets improves as it grows. More wallet personas means better fraud prediction precision on creator behavioral scores. More pair history means more confirmed criminal operator wallets in the cross-reference database. More contracts analyzed means more edge cases handled correctly in the deep code analysis infrastructure. A competitor starting today with identical engineering resources would still need years to reach ChainAware’s current capability level – and by then, ChainAware’s data assets would be proportionally larger still. The advantage is a trajectory, not a snapshot.

What Does a CLEAN Token Look Like?

2,436 tokens in this dataset – 18.7% – received a CLEAN verdict. Understanding what they have in common is as instructive as understanding what HIGH RISK tokens share.

Notably, zero CLEAN tokens have an uncapped mint function. Every CLEAN token either has no mint capability at all, or has a mint function with an immutable, verifiable on-chain cap. This single characteristic is the strongest predictor of a clean verdict – more consistent than any other single check in the dataset.

Additionally, CLEAN tokens overwhelmingly have verified source code. Their LP is either locked in a recognized locker (PinkLock, UniCrypt, Team Finance), burned to a dead address, or the project has explicitly structured treasury management differently with transparent on-chain documentation. Their ownership model is either renounced, controlled by a multisig with public signers, or timelocked. The transfer function has no assembly in its call graph, no external calls, and emits exactly the events ERC-20 requires – nothing more, nothing less. For the complete CLEAN verdict criteria, see the Token Audit Verdict Methodology.

Implications for Investors, Platforms, and Builders

For Individual Investors

The core finding of this study is that market capitalization rank is not a security signal. Tokens in the CoinGecko top 10,000 are 55.2% high risk and 1% confirmed honeypot. Before committing capital to any token, check three things specifically: whether the LP is locked, whether the mint function has an enforceable cap, and whether the contract is upgradeable by a single EOA. ChainAware Token Audit checks all three – and 124 other things – in under 60 seconds, free, without requiring a wallet connection. For how to interpret Token Audit results, see the Token Audit Investor Guide.

For DeFi Platforms and DEX Aggregators

Platforms that surface token information currently rely almost entirely on GoPlus for token security data. This study demonstrates that GoPlus-equivalent analysis leaves substantial risk categories completely undetected. Embedding Token Audit results at the listing or interaction point gives users substantially more protection than any current alternative. The REST API and MCP integration return full structured results including per-finding boolean flags, per-module risk scores, and a human-readable verdict. For technical integration details, see the Token Audit API documentation and the MCP Integration guide.

For Token Builders

The 18.7% CLEAN rate in this study is not a verdict on intent – most high-risk findings reflect architectural patterns that developers adopted without understanding their security implications. Token Audit runs in full against any deployed contract, returning specific findings with remediation guidance for each. Running Token Audit costs nothing and takes 60 seconds. It identifies every architectural risk that investors, security researchers, and automated tools will find after deployment – and gives developers the opportunity to fix them first. For how to use Token Audit in a pre-deployment security review, see the Pre-Deployment Checklist.

Pausability: 5% of Tokens Can Freeze All Trading Right Now

This study found 648 tokens – 5.0% of all audited contracts – where the mint function continues operating even when the token is paused (INV_PA5_MINT_NOT_PAUSED). An admin can pause all investor transfers while continuing to mint new tokens into their own wallet – simultaneously trapping existing holders and diluting their positions.

The most sophisticated pausability vulnerability – INV_PA4_ASYMMETRIC_PAUSE – blocks sells (transferFrom) while allowing buys (transfer). Honeypot.is tests a sell by calling transfer – the same function that is allowed in the asymmetric pause scenario – so it returns CLEAN for a token that is functionally a honeypot. ChainAware detects the asymmetric pattern by analyzing whether the pause condition applies differently to transfer versus transferFrom.

The 22 tokens currently paused represent an immediate alert for any investor holding these tokens. Token Audit calls paused() directly on each pausable contract to determine whether the pause is currently active – those 22 tokens are actively frozen right now. See the Pausability Verification documentation.

Supply Analysis: Hidden Minting and Supply Manipulation at Scale

Finding INV_S1_HIDDEN_MINT appears in 822 tokens – 6.3% of the dataset. Hidden mint detects functions that inflate the total token supply through mechanisms not labeled as mint() or _mint(). Because they bypass the standard _mint internal function, simple checks that scan for mint selectors in the contract ABI will miss them entirely. ChainAware traces every function that modifies the total supply variable regardless of name. See the Supply Verification documentation.

Finding INV_S4_FAKE_BURN appears in 318 tokens – 2.4%. A fake burn transfers to address(0) but does not reduce totalSupply(). Tokens marketed as deflationary based on burn history may be inflating their apparent scarcity. Additionally, 216 tokens show deployer concentration at 100% of circulating supply (INV_S6_DEPLOYER_100PCT) – the optimal setup for a coordinated pump-and-dump.

Audit Performance: 15 Seconds Average, 98.4% Source Verified

The median audit completes in 11.3 seconds – well within the threshold for interactive use cases like a DEX listing flow or a wallet pre-transaction security check. Only 9 audits across the entire 12,998-audit dataset exceeded 120 seconds – representing 0.07% of cases and well within operational tolerances for any integration scenario. 98.4% of tokens in this dataset have verified source code. For unverified contracts, Token Audit operates in bytecode analysis mode – the Honeypot Pattern module, Liquidity module, Simulation module, and Behavioral Trust Score all operate on bytecode and on-chain state rather than source code. For details see the Unverified Contract Analysis documentation.

Conclusion: The Token Security Gap Is Real

This study set out to answer a simple question: how safe are the tokens that most investors actually hold? The answer – 55.2% high risk, 1% confirmed honeypot, 13.2% upgradeable proxy, 25.7% unlocked LP – is more alarming than most observers expected from the top 10,000 by market capitalization. These are not obscure tokens in forgotten DEX pools. Many appear in mainstream wallet apps, on regulated exchange listings, and in institutional portfolio allocations.

Furthermore, the findings that established tools miss are precisely the ones that matter most for sophisticated attacks. GoPlus, TokenSniffer, and CertiK Skynet catch the obvious patterns. Consequently, professional scam operators have adapted: they write clean-looking code that passes all three tools, then execute through vectors those tools cannot see. The approve transitive attack, the phantom balance exploit, the permit preload, the asymmetric pause – all of these appear in this dataset, and all of them are invisible to current market-standard scanners.

ChainAware Token Audit changes this equation. It brings institutional-grade deep code analysis to every token, automatically, for free, in under 60 seconds. Combined with simulation analysis, behavioral Trust Scores, and proxy upgrade authority classification, Token Audit produces a security profile that exceeds what any competing automated tool provides. According to FATF’s Virtual Assets Recommendations, real-time token screening is becoming a compliance requirement for virtual asset service providers globally. Token Audit is live today – test any contract free, no signup, no wallet connection. For enterprise integration, book a technical walkthrough below.

Frequently Asked Questions

What is a token audit?

A token audit is an automated or manual security review of a cryptocurrency token’s smart contract. A manual token audit performed by firms like CertiK or Hacken costs $5,000 to $150,000 and takes one to four weeks. ChainAware Token Audit performs automated analysis across 127 checks in under 60 seconds at no cost for individual queries.

How is ChainAware Token Audit different from GoPlus?

GoPlus Security runs approximately 717 million monthly API calls. Its detection is rule-based – it checks for known dangerous patterns at the interface level. ChainAware Token Audit adds semantic analysis via deep code analysis, which traces the complete execution paths of transfer(), approve(), and related functions to find vulnerabilities hidden deep in internal call chains. ChainAware also adds reentrancy detection, permit correctness analysis, supply consistency checking, and behavioral Trust Scores – none of which GoPlus offers.

What does HIGH RISK mean in practice?

HIGH RISK means the token contract contains one or more mechanisms that a malicious or compromised admin could use to harm investors – an uncapped mint function, unlocked LP, EOA-controlled proxy, or admin with no timelock. HIGH RISK does not mean the token is currently being exploited – it means the architectural risk exists and investors should evaluate it consciously before committing capital.

How does the simulation analysis work?

Token Audit’s simulation module forks the relevant blockchain at the current block using an Anvil instance, then executes real buy and sell transactions inside the fork. This catches dynamic honeypot behavior that static analysis cannot detect: tokens where sells revert, tokens where the effective sell tax differs from the declared sell tax, and tokens where token conservation fails. See Simulation Module documentation.

Which chains does Token Audit cover?

Token Audit currently supports Ethereum, BNB Smart Chain, Base, Polygon, and Arbitrum. Optimism support is in progress. The analysis architecture is chain-agnostic at the contract level: deep code analysis, ownership tracing, and supply verification work identically across EVM-compatible chains.

What does the creator behavioral Trust Score measure?

The creator Trust Score evaluates the on-chain behavioral history of the wallet that deployed the token contract. It draws on ChainAware’s database of 20 million+ wallet behavioral profiles to assess whether the deployer has patterns consistent with fraud operators – prior rug pulls, coordination with known scam wallet clusters, funding source characteristics, and behavioral sequences associated with professional exit scam operations. See Fraud Detector documentation.

Can Token Audit be used pre-deployment?

Token Audit requires a deployed mainnet contract address – it analyzes live on-chain state alongside contract code. For pre-deployment security review, ChainAware recommends running deep code analysis directly against the contract source, then running Token Audit immediately after mainnet deployment. According to the Smart Contract Weakness Classification Registry, the majority of token vulnerabilities are deterministic at the code level and identifiable through static analysis shortly after deployment.

Sources: Chainalysis Crypto Crime Report · OWASP Smart Contract Top 10 · Smart Contract Weakness Classification Registry · EIP-2612: Permit Extension for ERC-20

Related ChainAware Reading: Best Rug Pull Detection Tools 2026 · DeFi Compliance Tools Comparison · KYT and AML Guide for DeFi · Web3 Wallet Auditing Providers 2026 · What Are Web3 Personas · Prediction MCP for AI Agents · The Web3 Agentic Economy · Agent Trust Score: On-Chain Trust Scoring for ERC-8004

The post ChainAware Token Audit Launched – We Tested 10,000 CoinGecko Tokens. Here Are the Results. first appeared on ChainAware.ai.

A $3-5 trillion market is forming around one unsolved problem: how do you know whether to trust an AI agent before it touches your funds? Six distinct approaches have emerged in 2026 to answer that question. They carry similar names – trust scores, reputation scores, behavioral scores – but they answer fundamentally different questions, protect against different threat models, and leave very different blind spots.

Choosing the wrong approach does not mean you get a slightly worse score. It means the specific fraud pattern you face is exactly the one your chosen platform cannot detect. An operator running a Sybil farm of 50 agents will not be caught by a review-quality platform scoring each agent individually. A serial rug puller launching agents under a fresh wallet will not be caught by a platform that scores wallet age but ignores creation history. Understanding which approach catches which threat is the most important infrastructure decision in agentic commerce right now.

This guide maps every significant agent trust platform in 2026 – their methodology, their real strengths, their genuine blind spots, and the specific signals that separate them. It is written for three audiences: DeFi protocol builders integrating agents and choosing a trust gating system, agent creators who want to understand how their agents get scored across platforms, and investors evaluating the agent trust infrastructure market as a sector.

Table of Contents

1. Why the Approach Matters More Than the Score
2. The Four Questions Agent Trust Platforms Answer
3. Platform 1 – ERC-8004 Native Reputation Registry
4. Platform 2 – RNWY: Review Quality and Sybil Detection
5. Platform 3 – SkyeProfile: Multi-Attestation Wallet Trust
6. Platform 4 – AXIS T-Score: Runtime Performance Scoring
7. Platform 5 – DJD Agent Score: Wallet Activity Scoring
8. Platform 6 – ChainAware: Behavioral Fraud Intelligence
9. The Five Signals Only One Platform Provides
10. Head-to-Head Comparison Table
11. Decision Matrix: Which Platform for Which Use Case?
12. The White Space: Five Capabilities Nobody Has Built Yet
13. The Investor Lens: What Makes Agent Trust Infrastructure a Durable Market
14. Frequently Asked Questions

Why the Approach Matters More Than the Score

Every agent trust platform in 2026 returns a number. The number is not the product – the threat model behind it is. Two platforms can both return a score of 72 for the same agent and disagree completely about what that score means, because they measured entirely different things to compute it.

RNWY’s score of 72 tells you: the agent’s peer reviews show limited sybil activity and the reviewer wallets are moderately established. ChainAware’s score of 72 tells you something different: the owner wallet has a moderate fraud probability, the feeder address is unknown, and no criminal record signals are present. SkyeProfile’s assessment tells you something different again: the wallet passes certain solvency and governance checks but shows limited behavioral depth across attestation providers.

Each score is internally consistent. However, each one answers a different question about the same agent. Consequently, the correct question for any DeFi protocol builder, agent creator, or investor is not “which platform gives the highest scores?” It is “which platform’s threat model matches the risk I am actually trying to prevent?”

For context on how this same problem appears at the wallet intelligence layer, see our complete guide to Web3 Wallet Auditing Providers in 2026 – the same principle applies there, where raw data providers, descriptive profilers, and predictive intelligence systems each answer fundamentally different questions about the same wallet address.

The Four Questions Agent Trust Platforms Answer

Before comparing platforms, mapping the question each approach addresses clarifies the landscape considerably. Every platform in the 2026 agent trust market falls into one of four categories based on what it actually measures.

Question 1: Have other agents endorsed this agent?

This is the peer review / reputation registry approach. The ERC-8004 native system operates here. Additionally, RNWY’s core methodology operates here – with the significant enhancement of reviewing the quality of the reviewers rather than simply counting the reviews. The fundamental limitation of this approach is that endorsement and trustworthiness are not the same thing. Any operator who controls multiple agents can engineer endorsements between them at near-zero cost.

Question 2: Has this agent performed tasks well?

This is the runtime performance approach. AXIS T-Score operates exclusively in this category, measuring 11 behavioral dimensions of agent task execution – completion rate, instruction adherence, error recovery, security posture, and similar metrics. The limitation here is that runtime performance and financial trustworthiness are orthogonal. An agent that executes tasks reliably can still be controlled by a fraud operator using it as a front for financial extraction.

Question 3: What does the agent’s wallet history look like?

This is the wallet activity approach. DJD Agent Score operates here, scoring seven wallet dimensions including transaction history, partner diversity, and account age. SkyeProfile’s solvency layer also operates here. The limitation is that wallet history describes the agent wallet itself – which is frequently a fresh address created specifically for the agent, with minimal history by design. A fresh agent wallet with no history is not the same as a fraudulent one, but wallet-only scoring treats them identically.

Question 4: Who controls this agent, and what have they done on-chain?

This is the behavioral fraud intelligence approach. ChainAware operates here – scoring the owner wallet that controls the agent, the feeder address that funded the owner, and cross-referencing both against a database of confirmed rug pulls and honeypot token creations. The threat model this addresses is the one that matters most for autonomous financial execution: a sophisticated fraud operator registering a new agent identity to continue activities previously conducted under different wallet identities.

Each of these four approaches is internally valid. Furthermore, they are not mutually exclusive – DeFi protocols can layer multiple approaches. However, understanding which question each one answers is essential before choosing which to gate on.

Platform 1 – ERC-8004 Native Reputation Registry

What it is

The ERC-8004 standard includes a built-in Reputation Registry as an optional component of the agent identity specification. The registry provides a standard interface for posting and fetching feedback signals. Critically, the standard explicitly does not define a scoring algorithm – aggregation and scoring are intentionally delegated to third parties. The protocol is infrastructure. Every other platform in this comparison is a third-party scoring layer on top of it.

Methodology

Any wallet can submit a feedback signal to the Reputation Registry for any registered agent. The signal includes a rating and optional metadata. The registry stores it on-chain. Reading platforms aggregate these signals according to their own methodology – which means the “ERC-8004 reputation score” is not a single consistent number but rather different outputs from different aggregation strategies across different platforms reading the same underlying data.

Strengths

The registry is permissionless, transparent, and composable. Any smart contract can read it. Furthermore, on-chain storage means the feedback history is permanent and verifiable. For building a decentralised reputation system in principle, the architecture is sound.

Blind spots

The fundamental blind spot is that the registry cannot distinguish manufactured reviews from genuine ones without an external intelligence layer on top. An operator controlling 50 agents can give each one a 5-star review from the other 49 at a cost of a few dollars in gas. Additionally, the native registry provides no information about who controls the agent, no feeder analysis, no fraud prediction, and no criminal record check. It answers only “what have other agents said about this agent?” – which is the weakest possible trust signal in a system where agents can be created and coordinated freely.

Who it is for

The native registry is appropriate as an additional data layer for platforms that have already implemented stronger trust signals. It should not serve as a primary trust gate for any DeFi protocol permitting autonomous financial execution.

Platform 2 – RNWY: Review Quality and Sybil Detection

What it is

RNWY (rnwy.com) is the most established third-party agent trust platform operating on ERC-8004 in 2026. RNWY positions itself as the trust layer for an economy where participants might not be human, with 185K+ agents scored and every score showing its math – the same door for humans and AI alike. The platform is notable for its transparency: all scoring methodology is published, including exact signal weights.

Methodology

RNWY’s trust score uses six input signals combined with additive modifier stacking, logarithmic value scaling, buffer zones, and evaluator softening to produce a score out of 95 across five tiers. The six signals weight toward reviewer quality rather than raw review count.

RNWY’s sybil detection applies four signals with explicit weights: common funder (6×), inhuman velocity (5×), sweep pattern (3×), and score clustering (1×). The weighted score produces severity levels: Low (0-2), Moderate (3-9), Elevated (10-19), and Heavy (20+). This makes RNWY’s sybil detection notably rigorous – it specifically targets the coordinated-review attack that would compromise naive review counting.

Since v1.1.0 (April 2026), RNWY also returns an owner wallet score, commerce summary (provider jobs, counterparty count, commerce tenure), and transaction-backed review percentage in the API response. However, these are additional intelligence fields – they appear in the response but do not affect the tier calculation or the primary trust score. This is the critical distinction from ChainAware: RNWY surfaces the owner wallet score as informational context; it does not integrate it into the scoring formula.

RNWY also indexes 1.7 million commerce jobs across Olas, Virtuals ACP, and SATI – making it the most comprehensive commerce activity tracker in the agent ecosystem. Trust scores live on Base mainnet, meaning any smart contract can read an agent’s score, tier, and sybil severity mid-transaction without an API call or oracle fee. This on-chain accessibility is a significant technical advantage for DeFi protocols that want to gate at the smart contract level rather than the application layer.

Strengths

RNWY’s strengths are transparency, on-chain accessibility, and commerce job history depth. The published methodology with exact signal weights means any relying party can independently verify a score calculation. The on-chain trust oracle on Base enables smart contract-level gating. The 1.7M commerce job index provides genuine economic activity context that no other platform matches. Additionally, the sybil detection is genuinely sophisticated – the common funder signal (weighted 6×) specifically targets the attack pattern of one operator funding multiple reviewer wallets from a single source.

Blind spots

RNWY’s primary blind spot is the boundary it draws at the review layer. The owner wallet score is surfaced but does not affect the tier. Feeder address analysis does not exist. Prior token creation history – rug pulls, honeypots – is not queried. Farm detection operates only at the reviewer level, not at the fleet level. Consequently, a fresh wallet that has never received a review (no positive signals, no negative signals) scores the same as an established operator in RNWY’s primary score calculation – both lack review history. Furthermore, a serial rug puller who has never participated in the ERC-8004 review ecosystem will not trigger any RNWY detection signal, because their fraud history exists in token creation, not in agent reviews.

Who it is for

RNWY is the strongest choice for platforms where agent reputation is displayed to users (marketplaces, directories, leaderboards) and where the primary threat model is manufactured peer endorsements. It is a compelling addition to any trust stack as a review quality layer. However, it is not sufficient as a standalone gate for DeFi protocols where the primary threat is a fraud operator using agents as the execution vehicle for financial crimes.

Platform 3 – SkyeProfile: Multi-Attestation Wallet Trust

What it is

SkyeProfile (skyemeta.com) is a multi-attestation wallet trust profile that orchestrates nine specialised attestation providers and returns one unified signed profile per wallet. The system uses a dual-score model with Signal Depth (behavioral observability) and Risk Intensity (sybil and fraud risk) as independent axes, covering 150K+ agents across ERC-8004, Olas, Virtuals, and SATI registries.

Methodology

SkyeProfile works on a general contractor model – eight dimensions, eight independent providers, eight verifiable signatures. One API call returns ten independently verifiable attestations, each with a JWKS URI so relying parties can verify any dimension offline without trusting SkyeMeta itself. The dimensions span solvency (wallet holdings across 33 chains), governance participation, behavioral trust, identity, security posture, compliance, performance, and settlement track record.

Notably, SkyeProfile uses RNWY as its behavioral trust provider – RNWY maintains the dual-score model across 150K+ agents spanning twelve EVM chains and Solana within the SkyeProfile attestation framework. This means SkyeProfile inherits RNWY’s methodology for the behavioral dimension, including both RNWY’s strengths (sybil detection, review quality analysis) and RNWY’s blind spots (no feeder analysis, no criminal record check, owner wallet informational only).

Strengths

SkyeProfile’s primary strength is breadth and verifiability. By aggregating nine specialised providers and returning independently verifiable signatures, it gives relying parties a comprehensive wallet profile that no single provider can match across all dimensions. The cryptographic verifiability (ES256 or EdDSA signatures with JWKS-published keys) is technically rigorous and appropriate for high-stakes autonomous execution contexts. The 33-chain solvency layer is the most comprehensive wallet holdings analysis in the market.

Blind spots

SkyeProfile scores the agent wallet – the address registered with the ERC-8004 identity. Since it delegates behavioral trust to RNWY, it inherits RNWY’s blind spots on feeder analysis and criminal record checking. Furthermore, because SkyeProfile is built as a wallet profiling system rather than an agent-specific fraud intelligence system, it does not perform fleet-level farm detection or trust delegation from owner to agent wallet. The platform also scores 150K agents across multiple registries – which is valuable breadth, but means its ERC-8004 specific coverage is thinner than RNWY’s 185K ERC-8004-specific indexed agents.

Who it is for

SkyeProfile is strongest for use cases requiring verifiable, multi-dimensional wallet attestations – particularly in contexts where cryptographic proof of each assessment matters, such as compliance audit trails or high-stakes DeFi credit decisions. For the broader DeFi credit scoring context, see our DeFi Credit Score Platform comparison. SkyeProfile is not a standalone agent trust gate – it is a comprehensive wallet profiling layer that serves agent trust as one of several use cases.

Platform 4 – AXIS T-Score: Runtime Performance Scoring

What it is

AXIS T-Score (axistrust.io) operates in an entirely different category from every other platform in this comparison. While all other platforms score the agent’s identity and on-chain history, AXIS scores the agent’s runtime behavior – how well it performs tasks, follows instructions, and operates within defined guardrails during actual execution.

Methodology

AXIS measures 11 behavioral dimensions: task completion rate, instruction adherence, data handling, transparency, error recovery, consistency, scope compliance, resource efficiency, communication clarity, security posture, and audit trail quality. All of these metrics are off-chain – they measure what the agent does during task execution, not what its controlling wallet has done on-chain. Scores run from 0 to 1,000 across five tiers (T1-T5), using the same 0-1,000 scale as ChainAware’s Agent Trust Score but measuring completely different inputs.

Strengths

AXIS addresses a genuinely different problem: does this agent do what it claims to do? An agent that claims to be a compliance screener but routinely fails to flag sanctioned addresses will score low on AXIS – regardless of how trustworthy its owner wallet is. That quality assurance dimension is valuable and not addressed by any on-chain behavioral platform. For enterprise contexts where agents are deployed for specific task categories, AXIS provides the most rigorous evaluation of task quality available.

Blind spots

AXIS scores runtime performance, not financial trustworthiness. An agent can score T5 on AXIS (top-tier task execution) and be controlled by a serial rug puller who has stolen millions. The two assessments are orthogonal – they address completely different threat models. For DeFi protocols where the primary concern is financial fraud rather than task quality, AXIS provides no relevant signal. Additionally, AXIS is entirely off-chain, which means it has no chain coverage, no wallet analysis, and no on-chain verifiability. Scores cannot be read by smart contracts and cannot be cryptographically verified against on-chain data.

Who it is for

AXIS is most valuable for enterprise deployments where agents perform specific workflow tasks – research, content generation, data analysis – and where task quality rather than financial fraud is the primary concern. Layering AXIS with an on-chain identity trust system (ChainAware for fraud intelligence, RNWY for review quality) produces the most complete agent evaluation stack: you verify both who controls the agent and how well the agent performs.

Platform 5 – DJD Agent Score: Wallet Activity Scoring

What it is

DJD Agent Score is the smallest and most narrowly focused platform in this comparison. It returns a 0-100 behavioral trust score for any wallet, combining seven dimensions – transaction history, partner diversity, volume patterns, account age, balance stability, activity consistency, and USDC usage – with sybil detection and gaming velocity checks. Scores feed directly into the ERC-8004 Reputation Registry as off-chain attestations, and the service is monetised via x402 micropayments in USDC on Base.

Methodology and coverage

DJD scores the agent wallet address across those seven dimensions. The scoring approach is transparent and the seven dimensions are reasonable wallet activity signals. However, coverage is Base-only – the platform does not index agents on Ethereum mainnet, BSC, or Avalanche. Furthermore, like SkyeProfile’s solvency layer and the ERC-8004 native registry, DJD scores the agent wallet rather than the owner wallet. This means it faces the same fresh wallet problem: a newly created agent wallet with no transaction history will score near zero on all seven dimensions regardless of the owner’s reputation.

Strengths and limitations

DJD’s x402 integration is technically interesting – it demonstrates a viable micropayment-based business model for agent trust scoring that does not require API keys or subscription agreements. The seven-dimension wallet scoring is simple, auditable, and directly verifiable. However, the Base-only coverage and agent-wallet focus rather than owner-wallet focus significantly limit DJD’s utility as a primary trust gate. It is best understood as an early-stage product demonstrating one viable approach rather than a production-ready trust infrastructure system.

Platform 6 – ChainAware: Behavioral Fraud Intelligence

What it is

ChainAware’s Agent Trust Score approaches agent trust from the opposite direction of every other platform. Rather than starting from the agent and asking what signals the agent produces (reviews, task performance, wallet history), ChainAware starts from the human behind the agent and asks what that human has done on-chain across their entire history – including activities completely unrelated to the current agent registration.

This inversion is the foundation of every signal that differentiates ChainAware from the rest of the market. For a full technical explanation of the scoring formula, see the Agent Trust Score methodology page.

Core formula

The Agent Trust Score builds on the same Wallet Reputation Score formula used across ChainAware’s products:

ReputationScore = (1000/110) × (experience + 1) × (risk_capability + 1) × (1 − fraud_probability)
Maximum: 1,000

This formula runs separately on the owner wallet and the agent wallet. Furthermore, it runs on the feeder address when traceable. The results are then combined using trust delegation logic, farm detection modifiers, and criminal record hard caps to produce the final Agent Trust Score. The 0-1000 scale is consistent with the Wallet Reputation Score – meaning a protocol that already uses ChainAware’s wallet intelligence can compare agent trust and wallet trust on the same axis without recalibration.

Coverage and infrastructure

ChainAware indexes 240,000+ ERC-8004 agents across Ethereum mainnet, BSC, Base, and Avalanche – the widest chain coverage in the market for a predictive fraud intelligence approach. The underlying wallet persona database covers 20M+ addresses across 8 blockchains, trained on behavioral data accumulated over multiple years. The fraud prediction model achieves 98% accuracy on held-out test data, as documented in our AI-Powered Blockchain Analysis guide. Additionally, scores are available via the Prediction MCP server, meaning any Claude-based DeFi agent can query agent trust scores as a native tool call without custom API integration.

The Five Signals Only One Platform Provides

Five signals in the Agent Trust Score are not replicated by any other platform currently operating in the ERC-8004 agent trust market. Each one addresses a specific threat model that the other approaches structurally cannot reach.

Signal 1: Feeder address analysis

The feeder address is the wallet that funded the agent’s owner wallet. Tracing and scoring it is the single most distinctive capability in the ChainAware Agent Trust Score. No other platform – not RNWY, not SkyeProfile, not DJD – performs feeder analysis.

Why it matters: an experienced fraud operator rotates owner wallets between campaigns. Wallet A runs a rug pull, gets flagged, and is abandoned. Wallet B is freshly created and funded from Wallet A. Wallet B then registers 40 agents on ERC-8004. Every platform that scores only the agent or the agent’s direct owner wallet will see a clean Wallet B with no fraud history. ChainAware traces the funding path and scores Wallet A – the feeder – which carries the fraud record. Wallet B’s agents receive hard-capped scores regardless of how clean Wallet B’s own history appears.

ChainAware covers feeder analysis for approximately 38% of indexed agents – the ones with a traceable single-hop funding source. For agents where the feeder is a verified CEX withdrawal address (Binance, Coinbase, Kraken, OKX), the platform flags this as FEEDER_CEX_VERIFIED – a positive trust signal that implies the owner wallet was funded via a KYC’d exchange withdrawal. For agents where the feeder is unknown or obfuscated, the platform applies a penalty reflecting the information asymmetry.

Signal 2: Criminal record – rug pull history

ChainAware maintains a database built from one year of on-chain liquidity pair history. That database records which wallet addresses created pools that subsequently exhibited rug pull patterns – rapid liquidity removal after price appreciation, following the operational signature documented in our Rug Pull vs Pump and Dump guide.

Before computing the Agent Trust Score, ChainAware cross-references both the owner wallet and the feeder address against this database. A single confirmed rug pull in the owner’s history generates a hard cap on the Agent Trust Score – a ceiling no other signal can override. This is the signal that connects yesterday’s token fraud to today’s agent deployment. An operator who rugged three pools on PancakeSwap in Q4 2025 and registered 40 agents in Q1 2026 is caught by this check. No other agent trust platform makes that connection, because no other platform maintains a paired rug pull database and cross-references it against agent registry data.

Signal 3: Criminal record – honeypot token history

Separately from rug pull detection, ChainAware maintains token audit data identifying honeypot contracts – tokens with embedded code that prevents buyers from selling. The creator wallet for each identified honeypot token is recorded. Cross-referencing agent owner wallets against this database produces a second criminal record dimension: has the agent’s controller previously created trap tokens that extracted funds from retail investors?

Honeypot creation and rug pull creation are related but distinct fraud patterns. Some operators specialise in one or the other; some use both. Having both databases cross-referenced produces a more complete criminal record than either alone. Together with rug pull history, this gives ChainAware the only criminal record check available in the agent trust market. For more on how token auditing produces these signals, see our Token Audit methodology.

Signal 4: Trust delegation

Trust delegation is ChainAware’s mechanism for handling the fresh agent wallet problem without penalising legitimate new agents. Agent payment wallets are frequently created specifically for an agent deployment – they are fresh addresses with no transaction history. A scoring approach that treats wallet age as a primary negative signal would incorrectly assign low trust to every newly deployed agent from a legitimate operator.

ChainAware’s trust delegation sets a floor for the agent wallet’s effective score based on the owner wallet’s Reputation Score. A strong owner (Sovereign tier, 800+) partially transfers credibility to the fresh agent wallet, resulting in a significantly higher Agent Trust Score than the agent wallet alone would produce. A fraud-flagged owner, by contrast, cannot delegate any meaningful trust – the delegation factor collapses to near zero. This means fresh wallets from reputable operators score correctly high, and fresh wallets from fraud operators score correctly low – which is the right outcome for both cases.

Signal 5: Fleet-level farm detection

Every other platform in this comparison scores agents individually. ChainAware maintains an owner profile database – tracking how many agents each owner controls across all indexed chains and whether those agents were registered in the same block (indicating automated bulk registration). This fleet-level view enables detection of agent farms that individual agent scoring cannot surface.

An operator running a farm of 50 agents will have each individual agent score independently on RNWY, SkyeProfile, or DJD. Nothing in those individual scores reveals the coordinated nature of the fleet. ChainAware sees the fleet. Owners controlling anomalously large numbers of agents receive a suppression modifier that applies to every agent in their fleet – including agents that individually might score cleanly. This is the signal that catches the specific agentic commerce attack pattern identified in our Blockchain Data Providers guide: one operator manufacturing ecosystem depth through controlled agent populations.

Head-to-Head Comparison Table

The following table compares all six approaches across every dimension relevant to DeFi protocol builders and investors evaluating the space. Each row describes a specific capability, not a general category, to make the comparison as concrete as possible.

Decision Matrix: Which Platform for Which Use Case?

No single platform is the correct choice for every context. The right stack depends on what you are trying to prevent, what signals matter for your specific use case, and what integration constraints you are working within. The following matrix maps use cases to recommended platform combinations.

DeFi protocol gating autonomous financial execution

Primary: ChainAware Agent Trust Score – owner fraud probability, feeder analysis, criminal record check, and farm detection are all directly relevant to the threat model. Set tier thresholds based on transaction risk: Trusted (600+) for high-value operations, Provisional (400+) for lower-risk flows with monitoring.

Secondary: RNWY for reputation display – show the RNWY score in your protocol’s agent directory alongside the ChainAware score. They answer different questions and the combination is more informative than either alone.

Optional: SkyeProfile attestations if your compliance framework requires cryptographically verifiable attestations as audit evidence. For the compliance context, see our DeFi Compliance and AML guide.

Agent marketplace or directory

Primary: RNWY – the on-chain trust oracle on Base enables smart contract-level minimum score requirements for job listing. The commerce job history (1.7M jobs) is directly relevant to marketplace quality filtering. The transparent published methodology means marketplace users can understand exactly why an agent scores as it does.

Secondary: ChainAware Agent Trust Score – surface it as a fraud intelligence layer alongside RNWY’s reputation score. The two scores are complementary: RNWY tells users whether the agent’s reviews are genuine; ChainAware tells users whether the human behind the agent has a history of financial fraud.

Enterprise workflow agent deployment

Primary: AXIS T-Score – for enterprise agents performing specific workflow tasks (research, compliance screening, content generation), task quality assurance is the primary concern. AXIS is the only platform that evaluates whether an agent does what it claims to do.

Secondary: ChainAware if the agent has financial execution permissions. Task quality and financial trustworthiness are both relevant for agents with write permissions to financial systems.

Agent creator wanting to understand their score

Agent creators interact with multiple trust systems simultaneously. Your agents are scored by every platform a buyer chooses to query. Understanding all five is therefore more important for creators than for buyers. Specifically:

• RNWY score: ensure your agent has genuine reviews from established reviewer wallets. Avoid requesting reviews from wallets that bulk-review across many agents – they will be detected as sybil reviewers and suppress your score
• ChainAware score: your owner wallet’s history is the primary input. A wallet with 12+ months of diverse DeFi activity scores significantly higher than a fresh wallet. If your feeder is a CEX withdrawal, this is a positive signal that surfaces automatically
• SkyeProfile: ensure your owner wallet holds governance tokens and participates in established protocols – the solvency and governance dimensions reward breadth of DeFi participation
• AXIS: if you want T-Score evaluation, ensure your agent returns reliable, consistent outputs and maintains audit trail quality across repeated task executions

The White Space: Five Capabilities Nobody Has Built Yet

The current agent trust infrastructure market is six months old. Consequently, significant white space remains – capabilities that no platform currently provides but that the market will almost certainly require as agentic commerce scales. The following five gaps represent the next investment and product opportunities in this category.

Gap 1: Agent-to-agent trust propagation

No platform currently answers this question: if Agent A scores Sovereign and has completed 10,000 successful interactions with Agent B, does that interaction history update Agent B’s trust score? In human systems, ongoing positive relationships build trust over time. In agent systems, every score is computed from static inputs without accounting for the accumulated interaction history between specific agent pairs. Building trust propagation that flows through agent interaction graphs – raising Agent B’s score based on verified positive interactions with high-scoring agents – would fundamentally change how trust compounds in the agentic economy.

Gap 2: Cross-registry agent identity resolution

An operator may deploy agents across ERC-8004, Olas, Virtuals, and SATI simultaneously. Currently, each registry treats these as separate identities. No platform provides unified entity resolution – grouping agents across registries that share the same owner wallet into a single entity profile. This matters because fleet-level behavior visible at the entity level (100 agents across 4 registries controlled by one owner) is invisible at the per-registry level (25 agents on each).

Gap 3: MCP server trust scoring

Every agent trust platform scores the agent itself. None score the MCP servers the agent calls. An agent connecting to a malicious or compromised MCP server is a trusted agent performing untrusted actions. As the MCP ecosystem grows – Smithery already indexes thousands of MCP servers – the trust question extends naturally from “who is the agent?” to “what tools is the agent using?”

Gap 4: Trust score-based insurance underwriting

No DeFi insurance protocol currently uses agent trust scores as an underwriting input. A protocol granting autonomous execution access to a Sovereign-tier agent (800+) takes on less risk than one granting the same access to a Provisional-tier agent (400-599). Insurance premiums, coverage limits, and deductibles could all be parameterised on agent trust scores – creating a financial market that prices the residual risk after trust gating rather than treating all agent access as equally risky.

Gap 5: Dynamic trust scores updating in real time

Current trust scores are computed at query time from static inputs and cached. None update continuously as new on-chain events occur. An agent whose owner wallet executes a suspicious transaction pattern at 14:00 UTC will not have its trust score updated until the next scoring cycle. Real-time trust score streaming – where scores update within seconds of relevant on-chain events – would enable dynamic access control that responds to emerging fraud signals rather than lagging behind them.

The Investor Lens: What Makes Agent Trust Infrastructure a Durable Market

For investors evaluating the agent trust infrastructure category, several structural dynamics shape the market’s long-term economics.

The TAM compounds with agent adoption

Agent trust infrastructure is a derived demand market – its TAM scales directly with agentic commerce adoption. McKinsey’s $3-5 trillion agentic commerce estimate implies that every dollar of economic activity flowing through autonomous agents creates a corresponding demand for trust verification of those agents. As Morgan Stanley projects , nearly half of online shoppers will use AI shopping agents by 2030. Each one of those agents represents a trust decision for every protocol or merchant it interacts with.

Consequently, market growth in agent trust infrastructure is structurally tied to the overall growth of agentic AI – a market with multiple large tailwinds including regulatory pressure (Know Your Agent protocols emerging from the EU AI Act framework), enterprise adoption (agents handling financial workflows requiring documented risk controls), and protocol incentives (DeFi protocols facing liability exposure from agent-initiated fraud).

Data network effects favour early movers with behavioral databases

Agent trust platforms that rely on behavioral databases – rather than purely algorithmic or review-based scoring – accumulate a compounding data advantage. A platform with one year of on-chain pair history knows which wallets created rug pools. A platform with two years knows which wallets have repeat patterns across multiple fraud campaigns. That historical depth cannot be compressed – a competitor starting today cannot buy the historical database that an early mover has built through continuous operation.

This dynamic differentiates behavioral fraud intelligence platforms from review-quality platforms. RNWY’s review quality algorithm could theoretically be replicated by a well-resourced team in months. The underlying behavioral database and fraud prediction model trained on years of on-chain data cannot. For context on how machine learning model development timelines apply to this space, see our AI-Powered Blockchain Analysis guide.

Complementary rather than winner-takes-all

The four distinct approaches in the market address different threat models that do not fully substitute for each other. RNWY’s review quality signal and ChainAware’s behavioral fraud intelligence are complementary – a protocol using both is better protected than a protocol using either alone. This means the agent trust market is likely to support multiple sustainable businesses serving different parts of the trust stack, rather than converging to a single dominant platform.

The parallel is the credit rating market – Moody’s, S&P, and Fitch coexist because rating agencies with complementary methodologies provide more value to the market than a single monopoly. Agent trust infrastructure may evolve similarly, with different platforms serving different trust dimensions in a layered stack. For investors, this implies that both the review quality layer (RNWY) and the behavioral fraud intelligence layer (ChainAware) have independent market positions rather than competing for the same slot in every protocol’s integration.

Regulatory tailwinds

The EU AI Act , which takes full effect in August 2026, creates documentation and risk assessment requirements for high-risk AI systems. Autonomous agents with financial execution permissions are a clear candidate for high-risk classification under this framework. Protocols operating in EU-regulated markets will need demonstrable risk controls for agent interactions – a requirement that agent trust scoring infrastructure directly satisfies. Additionally, Know Your Agent (KYA) protocols are emerging as the agent-layer equivalent of KYC, creating a compliance-driven pull for trust verification infrastructure beyond pure product adoption.

Frequently Asked Questions

Can I use multiple agent trust platforms simultaneously?

Yes – and for high-value use cases, this is the recommended approach. RNWY and ChainAware answer different questions about the same agent. Using RNWY for review quality and ChainAware for owner fraud intelligence produces a more complete picture than either alone. The integration is straightforward: make two API calls per agent and combine the results in your access control logic. DeFi protocol builders can set independent thresholds for each score – for example, requiring RNWY tier 3+ (genuine review history) AND ChainAware Trusted tier (600+) for full autonomous execution access.

Which platform is best for displaying trust to end users?

RNWY is the strongest choice for trust display because its methodology is fully published and each score shows its math. End users can understand exactly why an agent scored as it did – which reviewer wallets were flagged, which sybil patterns were detected, what the address age contributed. Transparency builds user confidence. ChainAware’s score is complementary but its weights are private (to prevent gaming), making RNWY more appropriate for user-facing display where explainability matters.

How do the different score scales compare?

The score scales are not directly comparable across platforms. RNWY scores out of 95 (not 100 – their maximum is 95 due to scoring mechanics). ChainAware and AXIS both use 0-1,000. DJD uses 0-100. SkyeProfile uses two independent axes rather than a single number. Converting between scales requires understanding what each platform actually measures, which is why the comparison table above focuses on capabilities rather than score values. An agent scoring 72/95 on RNWY and 650/1,000 on ChainAware is not inconsistent – those numbers describe entirely different assessments.

Does RNWY’s owner wallet score compete with ChainAware?

Not meaningfully. RNWY’s v1.1.0 update added owner wallet score as an informational field in the API response – but explicitly does not affect tier calculation or the primary trust score. The field surfaces the owner wallet’s RNWY-defined score as context for relying parties who want to incorporate it into their own decision logic. ChainAware makes the owner wallet score the primary input to the Agent Trust Score formula, combines it with feeder analysis and criminal record data, and applies trust delegation. The two approaches share the observation that owner wallet matters – but diverge completely on how to score it and how much weight it should carry.

What is ERC-8183 and how does it relate to agent trust?

ERC-8183 is a commerce protocol that gives AI agents trustless commerce capabilities – handling escrow, state transitions, and evaluator attestation for agent-to-agent job markets. The spec is intentionally minimal – it handles the commerce mechanics but explicitly does not handle trust scoring, discovery, or fraud detection. RNWY has built a marketplace and trust scoring layer on top of ERC-8183. ChainAware’s Agent Trust Score is compatible with ERC-8183 job markets as a pre-interaction trust gate – protocol teams can require a minimum Agent Trust Score before an agent can claim a job or receive escrowed funds.

How often do trust scores update?

Update frequencies vary by platform. ChainAware’s fraud prediction model retrains daily – meaning the fraud probability feeding into owner wallet scores updates continuously as new on-chain patterns emerge. Scores for specific agents update when new relevant events are detected (new agent registrations, owner wallet activity, feeder transactions). RNWY scores update as new reviews are submitted to the ERC-8004 Reputation Registry and as sybil analysis runs on reviewer wallets. AXIS T-Score updates based on runtime task execution data. None of the current platforms offer real-time streaming score updates – that remains a white space capability described above.

Is the ChainAware Agent Trust Score relevant for non-ERC-8004 agents?

Partially. The owner wallet and feeder address scoring works for any wallet address, regardless of whether it is associated with an ERC-8004 registration. A protocol that receives agent-initiated transactions from wallets not registered on any standard identity registry can still query ChainAware’s Fraud Detection API for the controlling wallet’s behavioral intelligence. The ERC-8004-specific signals (farm detection, trust delegation from registry data) require an ERC-8004 registration to function. However, the owner fraud probability, feeder analysis, and criminal record check work on any wallet regardless of registry status. For protocols on chains not yet covered by ERC-8004 registries, this means ChainAware provides partial Agent Trust Score functionality even before full ERC-8004 adoption on those chains.

Where can I read ChainAware’s full scoring methodology?

The complete methodology – including the five scoring layers, all flag definitions, score tier descriptions, and the trust delegation formula – is documented at chainaware.ai/learn/agent-trust-score. The signal categories are published. The exact weights and model coefficients remain private to prevent gaming. The equivalent documentation for the underlying Wallet Reputation Score (which feeds into the Agent Trust Score formula) is at chainaware.ai/learn/for-individuals/wallet-auditor.

Further Reading

• Agent Trust Score – Complete Methodology – the five scoring layers, all flags, tier definitions, and trust delegation formula
• The First Step in Agentic Commerce Isn’t Integration. It’s Trust. – the companion article covering the trust gap in DeFi protocol agent integrations
• Web3 Wallet Auditing Providers in 2026 – the same three-layer framework applied to the wallet intelligence market
• Web3 Analytics Tools for Dapps: Complete Comparison – where agent trust scoring fits in the broader DeFi analytics stack
• AI-Powered Blockchain Analysis for Crypto Security – the machine learning methodology behind ChainAware’s 98% fraud detection accuracy
• Rug Pull vs Pump and Dump – the fraud patterns that generate ChainAware’s criminal record database
• DeFi Compliance: KYT and AML Guide 2026 – regulatory context for DeFi agent integration compliance
• DeFi Credit Score Platforms Compared – how agent trust scoring combines with borrower creditworthiness assessment
• Prediction MCP Setup Guide – add ChainAware behavioral intelligence including Agent Trust Score to any Claude agent
• 32 Ready-Made Agents – pre-built Claude agents including agent verification, fraud detection, and compliance screening

ChainAware.ai is the Web3 Agentic Growth Infrastructure – behavioral intelligence for DeFi protocols, AI agents, and individual crypto users. 20M+ wallet personas, 98% fraud detection accuracy, <100ms API latency across 8 blockchains. Try free at chainaware.ai.

The post The Agent Trust Infrastructure Race: Who Is Building the Trust Layer for Agentic Commerce? first appeared on ChainAware.ai.

Your DeFi protocol is ready to integrate AI agents. You have evaluated the frameworks, chosen your ERC-8004 registry, mapped the wallet flows, and written the integration spec. Yet one question remains unanswered in that spec – a question that determines whether your agentic integration scales safely or becomes a fraud vector the moment it hits production volume.

Who is actually behind the agent you are about to trust with your users’ funds?

Agentic commerce is accelerating at a pace that has outrun the trust infrastructure supporting it. McKinsey estimates the model could redirect $3-5 trillion in global financial flows by 2030 . Meanwhile, 78% of financial institutions already expect fraud to spike specifically because of AI agents operating autonomously in commercial systems. The gap between “agent integration complete” and “agent interaction verified” is where the next generation of DeFi fraud will live.

This guide covers the entire problem – from the structural gap in the ERC-8004 standard to the specific signals that distinguish a legitimate agent from a manufactured one, to the concrete integration pattern that closes the trust gap in under 100ms per transaction.

Table of Contents

1. What Agentic Commerce Actually Means for DeFi Protocols
2. The Integration Stack Has a Trust Gap
3. Why Voting-Based Agent Reputation Fails at Scale
4. Know Your Agent: The Three Questions That Matter
5. Signal 1 – The Owner Wallet: Scoring the Human Behind the Agent
6. Signal 2 – The Feeder Address: Who Funded the Controller?
7. Signal 3 – The Criminal Record: Rug Pulls, Honeypots, and Prior Fraud
8. Trust Delegation: How a Strong Owner Legitimises a Fresh Agent Wallet
9. Farm Detection: One Operator, Dozens of Agents
10. EIP-7702 Delegation: The Hidden Controller Problem
11. The Trust-Aware Agent Integration Pattern
12. The Compounding Risk of Getting This Wrong
13. How ChainAware Compares to Other Agent Trust Platforms
14. Frequently Asked Questions

What Agentic Commerce Actually Means for DeFi Protocols

Agentic commerce describes the shift from humans clicking “confirm” to AI agents executing transactions autonomously on behalf of users. In Web3, this shift is not a future scenario – it is happening now, at scale, across every DeFi protocol that accepts agent-initiated transactions.

Agents are managing DAO treasuries, executing lending strategies, routing liquidity, screening counterparties, and processing governance votes – all without a human in the approval loop for each action. The operational efficiency gains are real. Furthermore, the fraud surface that comes with them is equally real and far less discussed.

For DeFi protocol builders, the critical insight is this: if your protocol accepts transactions from external wallets today, you are already serving agent-initiated transactions. Agent wallets are indistinguishable from human wallets at the RPC layer. Therefore, you do not need to deliberately “integrate agents” to be exposed to the trust problem – you already are exposed, today, because any wallet can be controlled by an agent rather than directly by a human.

The agentic commerce numbers clarify the urgency. Morgan Stanley projects that nearly half of online shoppers will use AI shopping agents by 2030, accounting for approximately 25% of their total spending . In DeFi specifically, the transition is faster – AI is moving from the advisory layer (suggesting trades) to the execution layer (completing them). The distinction between advice and execution is the distinction between a bad recommendation and an empty wallet. Consequently, DeFi protocol builders face the urgency of solving this in 2026, not 2028.

Traditional fraud detection systems are structurally unfit for this environment. As detailed in our guide on AI-Powered Blockchain Analysis for Crypto Security, rule-based systems generate false positive rates of 30-70% – and they produce those false positives specifically on the rapid, sequential, cross-category transaction patterns that legitimate AI agents exhibit. Therefore, you need a different approach: one that evaluates the agent’s identity and the human behind it, rather than flagging agent behaviour as inherently suspicious.

The Integration Stack Has a Trust Gap

A typical agentic commerce integration in 2026 follows a well-established pattern. The agent framework (ElizaOS, Virtuals, a custom build) registers an identity on the ERC-8004 Identity Registry. Subsequently, that identity is referenced when the agent initiates interactions with DeFi protocols. The protocol’s smart contract processes the transaction. Funds move.

Every layer in that stack has tooling, documentation, and standards. Agent frameworks have deployment guides. ERC-8004 has a specification and a registry of 240,000+ agents across Ethereum, BSC, Base, and Avalanche . Smart contracts have audit firms. Yet the gap between the ERC-8004 registry lookup and the protocol interaction has no standard tooling – and it is precisely where trust decisions need to happen.

The ERC-8004 registry tells you four things about an agent: that it exists, which wallet controls it, which wallet receives its payments, and what URI points to its agent card JSON. Those four data points answer the question “does this agent have an identity?” They do not answer the question “should I trust this agent with autonomous execution?”

Specifically, the registry tells you nothing about:

• Whether the owner wallet has a history of creating rug pull pools or honeypot tokens
• Whether the owner was funded by a mixer, a sanctioned address, or a known fraud operator
• Whether this agent is one of 47 registered in the same block by the same operator running a Sybil farm
• Whether the wallet controlling this agent also controls the 46 agents that gave it positive reviews

This is the trust gap. Moreover, it is not an oversight in the ERC-8004 specification – the standard explicitly leaves scoring to third parties. As a DeFi protocol builder, you are therefore responsible for filling that gap in your own integration layer.

For context on how behavioral wallet intelligence fills similar gaps in fraud detection, see our complete guide to Web3 Wallet Auditing Providers in 2026. The same principle applies at the agent layer: raw identity data requires an intelligence layer on top before it becomes a trust signal.

Why Voting-Based Agent Reputation Fails at Scale

ERC-8004 includes a built-in Reputation Registry – a standard interface for agents to receive and query peer feedback. The design is intentionally open: any agent can leave a review, any protocol can read the scores, and the aggregation algorithm is left to third parties. On paper, this sounds like a reasonable decentralised trust mechanism. In practice, it is a manufactured-trust system waiting to be exploited.

The attack requires minimal technical sophistication. An operator deploys 50 agent wallets. Each wallet reviews every other wallet positively. All 50 accumulate reputation scores indistinguishable from agents with genuine peer endorsements. Total cost: gas fees for the review transactions, which on BSC or Base amounts to a few dollars. Total time: hours. Total manufactured trust: a full reputation history that any naive integration will treat as legitimate.

Furthermore, the problem compounds in agentic commerce contexts. When B2B agent networks operate where AI buyers negotiate directly with AI sellers in fractions of a second , the speed of manufactured-reputation exploitation is not limited by human review cycles. One fraudulent agent with a manufactured score can interact with thousands of protocol users autonomously before any human notices the pattern.

Voting-based reputation also has a specific structural blind spot: it cannot distinguish between an agent with 100 genuine endorsements and an agent whose owner simultaneously controls the 100 endorsing agents. Consequently, any trust system that reads only the Reputation Registry score is solving the wrong problem. The question is not “how many agents have endorsed this agent?” The correct question is “who controls this agent, and what have they done on-chain?”

This distinction drives the entire design of the ChainAware Agent Trust Score. Rather than reading the ERC-8004 Reputation Registry, we look behind the agent at the behavioral history of the wallets controlling it and funding its controller. The result is a trust signal that cannot be manufactured in hours and cannot be faked by a cluster of cooperating wallets.

Know Your Agent: The Three Questions That Matter

Know Your Agent (KYA) is emerging as the agent-layer equivalent of KYC. However, unlike KYC – which verifies identity documents and requires data collection – KYA for DeFi is necessarily on-chain behavioral. There are no passports in Web3. There is only transaction history, and that history is immutable, public, and available for scoring without touching any personal data.

A robust KYA check for any ERC-8004 agent answers exactly three questions. Together, these three questions generate a trust signal that is structurally difficult to fake and impossible to manufacture overnight.

Question 1: Who controls this agent?

Every ERC-8004 agent has an owner wallet – the address that holds the ERC-721 NFT representing the agent’s on-chain identity. This is the human or entity behind the agent. Scoring that wallet’s behavioral history is the foundation of any meaningful trust assessment.

Question 2: Who funded the controller?

The feeder address – the wallet that funded the owner – is the signal most agent trust platforms cannot reach. It is also the hardest signal to fake, because it requires either real capital from a legitimate source or exposure to traceable fraud infrastructure. An owner wallet can be freshly created and carefully aged. The funding source is immutable on-chain history.

Question 3: Has the controller done this before – in a bad way?

A year of on-chain pair history combined with token audit data produces a direct criminal record check for the agent controller. Has this wallet created honeypot tokens? Has it created liquidity pools and removed funds in rug pull patterns? Has the feeder funded previous rug pull operators? These questions have definitive on-chain answers – and no peer-review system can surface them.

The following three sections address each signal in depth, including how it feeds into the Agent Trust Score formula and what it means for your integration.

Signal 1 – The Owner Wallet: Scoring the Human Behind the Agent

The owner wallet is the single most important input to any agent trust score. Everything else – the agent wallet, the agent card, the reputation registry score – can be created fresh for a new fraud operation. The owner wallet’s behavioral history cannot.

ChainAware scores the owner wallet using the same three-pillar Reputation Score formula applied across 20M+ wallet personas:

ReputationScore = (1000/110) × (experience + 1) × (risk_capability + 1) × (1 − fraud_probability)
Maximum: 1,000

Each pillar captures a distinct dimension of the owner’s on-chain identity.

Experience

Experience measures how long and how actively the owner wallet has operated on-chain. A wallet with 18 months of diverse DeFi interactions – lending, trading, bridging, staking across multiple protocols – scores high on experience. Conversely, a wallet created three weeks ago that has done nothing but register agents scores near zero. Experience is hard to accelerate, because it is a function of time as much as activity. An operator cannot age a fresh wallet by transacting intensively for a week and matching the experience score of a genuinely established participant.

Risk Capability

Risk capability measures the behavioral breadth of the owner wallet. Does it interact with a range of DeFi protocols, or does it show narrow, mechanical patterns consistent with a purpose-built fraud wallet? Legitimate DeFi participants accumulate a diverse transaction graph over time – different counterparties, different protocol types, different token categories. Fraud wallets tend to exhibit concentrated patterns: high transaction frequency in a narrow activity type, often with timing patterns that indicate scripted rather than human behavior.

Fraud Probability

Fraud probability is ChainAware’s predictive AI model output – a score between 0.0 and 1.0 representing the likelihood that the owner wallet will engage in fraudulent behavior. This is not a blacklist check. Blacklists are reactive; they flag addresses after fraud has been confirmed. The ChainAware fraud model is predictive: it scores behavioral patterns against 20M+ wallet personas to estimate forward-looking risk, identifying likely fraud actors before they have generated a confirmed fraud record. For a detailed explanation of the machine learning methodology, see our AI-Powered Blockchain Analysis guide.

The Reputation Score applied to the owner wallet produces a single 0-1000 number that feeds into the Agent Trust Score formula as the primary input. A strong owner score (800+) indicates a Sovereign-tier controller with genuine on-chain history. A weak owner score (below 200) flags an Untrusted controller regardless of how clean the agent’s own wallet appears.

Signal 2 – The Feeder Address: Who Funded the Controller?

The feeder address is ChainAware’s most distinctive signal in the Agent Trust Score – and the signal that no competing agent trust platform currently reaches. RNWY surfaces the owner wallet but marks it as informational, non-scoring data. SkyeProfile performs partial operator wallet analysis. Neither traces the funding source of the controller.

ChainAware traces feeder addresses for approximately 38% of indexed agents. That 38% coverage rate reflects the on-chain reality: some owner wallets receive funds from obfuscated sources, some from multiple feeders that cannot be unambiguously attributed, and some from the native chain’s genesis or bridge infrastructure. When the feeder is traceable, the signal is highly informative.

Feeder categories and their trust implications

CEX withdrawal (Binance, Coinbase, Kraken, OKX, and others): A feeder address that is a verified CEX hot wallet implies that the owner wallet’s initial funding came from a centralized exchange withdrawal. CEX withdrawals imply the controller passed KYC somewhere upstream – not necessarily ChainAware’s KYC, but some identity verification process at deposit. This is the strongest positive feeder signal available. ChainAware flags this as FEEDER_CEX_VERIFIED and applies the maximum feeder factor in the scoring formula.

Known fraud operator or mixer: A feeder address that is a confirmed Tornado Cash wallet, ChipMixer output, or address previously flagged in ChainAware’s fraud database propagates that fraud signal directly to the agent score. An owner wallet funded by a mixer is not automatically fraudulent – there are legitimate privacy use cases – but combined with other risk signals it is a strong indicator of deliberate fund obfuscation. Mixers and confirmed fraud feeders apply a hard cap to the Agent Trust Score regardless of how clean the owner wallet’s own transaction history appears.

Unknown or obfuscated feeder: When the feeder cannot be determined, ChainAware applies a penalty to the feeder factor. Obfuscation is not neutral – the absence of a traceable funding source is itself a risk signal. Legitimate operators who funded their owner wallets via normal CEX withdrawals have nothing to hide and produce traceable feeder paths. Operators who deliberately route funds through multi-hop paths to obscure the source are doing so for a reason.

For compliance-oriented DeFi protocols, the feeder analysis also connects directly to AML obligations. Our guide on Blockchain Compliance for DeFi: KYT and AML in 2026 covers the regulatory context in detail. Notably, feeder address analysis extends the transaction monitoring horizon beyond the immediate counterparty – which is precisely what FATF’s Travel Rule guidance asks for in the context of virtual asset transfers.

Signal 3 – The Criminal Record: Rug Pulls, Honeypots, and Prior Fraud

This is the signal that makes the ChainAware Agent Trust Score genuinely unique – and the signal that matters most for DeFi protocol builders who have been operating in the space long enough to know that today’s agent creator is often yesterday’s rug pull operator wearing a fresh wallet.

ChainAware maintains a database built from one year of on-chain pair history and token audit data. Specifically, this database captures:

• Token contracts flagged as honeypots by ChainAware’s algorithmic analysis
• The creator wallet address for each honeypot token
• Liquidity pools where the creator removed funds in patterns consistent with rug pull execution
• The creator wallet address for each rug pull pool

Before computing the Agent Trust Score, ChainAware cross-references both the owner wallet and the feeder address against this database. Any match generates a hard cap on the final score – a ceiling that no other scoring signal can override.

The logic here is direct: a single confirmed rug pull or honeypot in an agent controller’s history is a disqualifying signal for autonomous execution trust. An operator who has previously stolen from retail investors through manufactured liquidity or tax-trap tokens is not a different actor simply because they have registered a new agent identity on ERC-8004. The on-chain history is permanent. The behavioral record cannot be expunged.

As we document in our guide to Rug Pull vs Pump and Dump: How Crypto Fraud Extracts Wealth from Retail Investors, approximately 95% of new pools on PancakeSwap end in rug pulls. Furthermore, the operators behind those pools are not typically first-time offenders – they are repeat actors who rotate wallets between campaigns. Connecting that historical fraud record to new agent registrations is what allows ChainAware to catch the serial fraudster who is simply moving from token launches to agent deployments as the market cycle shifts.

Feeder criminal record: the compounding signal

Criminal record analysis applies not only to the owner wallet but also to the feeder address. Consider the operational pattern of a sophisticated fraud operator:

1. Operator runs rug pull campaigns using Wallet A (primary fraud wallet, now flagged)
2. Operator creates fresh Wallet B with no fraud history
3. Wallet A funds Wallet B – the feeder relationship is recorded on-chain
4. Wallet B registers agents on ERC-8004, presenting a clean owner wallet history
5. Any platform that scores only the owner wallet (Wallet B) misses the connection entirely

ChainAware’s feeder analysis catches step 4. The funding source (Wallet A) has a confirmed rug pull history in our database. Therefore, Wallet B’s agents receive a hard cap score regardless of how clean Wallet B’s own transaction history appears. This is the operational pattern that makes the feeder signal irreplaceable – it is the signal sophisticated actors spend the most effort obscuring, precisely because it is the signal that most reliably connects new operations to old fraud records.

Trust Delegation: How a Strong Owner Legitimises a Fresh Agent Wallet

Agent wallets present a specific challenge for naive scoring approaches. These wallets are frequently created specifically for the agent – they are fresh addresses with no transaction history, no counterparty network, and no behavioral record. A scoring approach that treats wallet age and transaction history as primary inputs would therefore penalise every newly registered agent regardless of the owner’s reputation. That produces low scores for legitimate agents and renders the score less useful as a gate for agentic commerce integrations where new agents are continuously deployed.

ChainAware solves this problem with trust delegation. The owner wallet’s Reputation Score sets a floor for the agent wallet’s effective score. A strong owner partially transfers credibility to the fresh agent wallet. The exact delegation factor depends on feeder availability and the owner’s own fraud status.

# Trust delegation: owner lifts fresh agent wallet score
delegated_floor  = owner_score × delegation_factor

# Delegation factor varies by context:
# - Normal (feeder available, owner clean): 0.6
# - Feeder unknown (obfuscation signal):    0.3
# - Owner fraud-flagged:                    0.1

effective_wallet = max(wallet_score, delegated_floor)

This means a reputable developer deploying their first agent scores appropriately high – even with a fresh payment wallet – because the owner’s 18-month behavioral record delegates trust downward to the new agent wallet. A fraud-flagged owner, by contrast, cannot delegate any meaningful trust regardless of how the fresh agent wallet appears. The delegation factor collapses to near zero, and the agent score reflects the owner’s history rather than the wallet’s lack of it.

Trust delegation also captures the inverse correctly. If an agent wallet has a genuinely clean and established history (because the same operator has deployed agent wallets before), that score is used directly without needing the delegation floor. The formula takes the maximum of the two – the wallet’s own score and the delegated floor from the owner – ensuring that genuine wallet history is never penalised by the delegation mechanism.

This mechanism is unique to ChainAware among agent trust platforms operating on ERC-8004 in 2026. Competing platforms that surface the owner wallet as informational data but do not integrate it into their scoring formula cannot implement delegation – because delegation requires both the owner score and the wallet score to be computed on a comparable scale and combined algorithmically.

Farm Detection: One Operator, Dozens of Agents

Multi-agent orchestration is one of the defining architectural trends in agentic AI for 2026. Orchestrator agents coordinate specialised sub-agents working in parallel – a legitimate pattern that produces significant efficiency gains for complex workflows. However, the same architecture that enables powerful legitimate multi-agent systems also enables a specific attack pattern in agentic commerce: agent farming.

Agent farming is the practice of a single operator registering a large fleet of agents, typically in bulk during a narrow time window, with the goal of:

• Cross-endorsing each other to manufacture reputation scores
• Flooding agent marketplaces with controlled supply to manipulate pricing or availability
• Creating the appearance of ecosystem depth across multiple agent identities controlled by one bad actor
• Operating coordinated fraud campaigns across dozens of agent wallets that each individually appear to have limited exposure

ERC-8004 places no restrictions on how many agents a single owner can register. Consequently, a single wallet can register 500 agents in a single afternoon with no protocol-level friction. Individual agent scoring – which is what every competitor in this space does – is blind to the fleet-level pattern. Each agent scores independently; none of them individually triggers a threshold that reveals the fleet behavior.

ChainAware maintains an owner profile database that tracks agent fleet size per owner across all indexed chains. Owners controlling large numbers of agents receive a farm detection signal that suppresses the score for every agent in their fleet. Furthermore, the specific pattern of same-block registration – multiple agents minted in a single block – carries additional weight, because it indicates automated bulk registration rather than organic deployment over time.

The farm detection signal appears in the API response as the FARM_DETECTED flag. It does not expose the specific threshold that triggered the signal – sharing that threshold would tell operators exactly how many agents they can register before triggering detection. Instead, the flag communicates the category of signal without revealing the calibration.

From a DeFi protocol integration perspective, farm detection is the signal that turns individual agent trust scoring into a fleet-level intelligence system. Agents from the same owner share a trust destiny – if the owner’s fleet pattern is suspicious, every agent in that fleet is suspect regardless of how any individual agent scores in isolation.

EIP-7702 Delegation: The Hidden Controller Problem

EIP-7702 allows Externally Owned Accounts (EOAs) to delegate control to a secondary address for a single transaction or extended period. In the agent context, this means the wallet registered as the ERC-8004 agent owner may not be the wallet actually controlling the agent’s behavior – a secondary delegated address might be executing transactions on behalf of the nominal owner.

ChainAware detects EIP-7702 delegation for agent owner wallets. When detected, the scoring process adds the delegate address to the analysis and takes the lower of the two scores – owner and delegate – as the effective owner score feeding into the Agent Trust Score formula.

This matters because EIP-7702 delegation is a specific mechanism that sophisticated actors can use to obscure the real controlling entity behind an agent. The nominal owner wallet might have a strong reputation score built over many months. The delegate might be a fresh fraud wallet with no history. Without EIP-7702 analysis, the strong nominal owner score masks the fraudulent delegate’s risk profile. With it, the delegate’s low score pulls the effective owner score down to reflect the actual controlling entity.

Approximately 5% of indexed ERC-8004 agents have EIP-7702 delegated ownership, based on ChainAware’s current database. Agents with EIP-7702 delegation are flagged explicitly in the API response as EIP7702_DELEGATED – giving protocol builders the option to apply additional scrutiny to this category regardless of the final numerical score.

The Trust-Aware Agent Integration Pattern

A DeFi protocol that has addressed the trust gap adds one step between the ERC-8004 registry lookup and the transaction execution. That step takes under 100ms, requires one API call, and produces a structured output that the protocol’s access control layer can act on directly.

Agent initiates transaction
  ↓
Resolve agent_id → owner_address + agent_wallet (ERC-8004 registry)
  ↓
GET /erc8004/agent/{chain_id}/{agent_id}/trust-score
  ↓
Response:
  {
    "agent_trust_score": 882,
    "tier": "Sovereign",
    "flags": ["FEEDER_CEX_VERIFIED"]
  }
  ↓
score ≥ protocol_threshold → execute
score < protocol_threshold → reject or route to human review

The threshold is a protocol-level decision. Different use cases warrant different risk tolerances:

The ChainAware Agent Trust Score API integrates directly with the Prediction MCP server, meaning any Claude-based DeFi agent can call the scoring endpoint as a native MCP tool call without custom API integration code. For teams building on the MCP stack, see our Prediction MCP setup guide and our library of 32 ready-made agents that already include agent verification logic.

Additionally, the trust check does not add friction for legitimate agents. A reputable developer deploying their first agent – with a strong owner wallet history and a CEX-verified feeder – scores above 800 through trust delegation even with a brand-new agent payment wallet. The check identifies the fraudulent operator while leaving the legitimate one unrestricted. That asymmetry is the operational definition of a useful trust system.

The Compounding Risk of Getting This Wrong

Human-initiated fraud and agent-initiated fraud differ in one fundamental operational characteristic: velocity. A fraudulent human interacting with your protocol manually can execute perhaps dozens of interactions before detection. A fraudulent agent operating autonomously can execute thousands of interactions in the same period – at machine speed, without sleep, without rate-limit awareness unless you specifically implement it, and with the full behavioral sophistication of the AI model powering it.

Therefore, the cost of a single misidentified agent is not comparable to the cost of a single misidentified human user. The exposure scales with the agent’s operational capacity. A lending protocol that grants a fraudulent agent autonomous execution access for six hours faces losses that scale with the protocol’s TVL and the agent’s transaction rate – not with a single transaction amount.

Traditional fraud detection tools are particularly poorly suited to this environment for reasons we explore in detail in our DeFi Compliance and KYT guide. Rule-based systems flag agent behavior as suspicious because agents naturally exhibit the patterns those rules target: high velocity, cross-category activity, unusual timing distributions. Consequently, you end up blocking legitimate agents while missing sophisticated fraudulent ones that have been engineered to mimic human behavioral patterns.

The compounding risk calculation is straightforward. One fraudulent agent operating undetected for six hours at 100 transactions per minute generates 36,000 protocol interactions. If each interaction involves 0.1 ETH and the fraud extracts 50% of interaction value, that is 1,800 ETH in losses from a single agent integration oversight. The trust check that would have caught this agent costs one API call taking under 100ms. The return on that 100ms is measured in protocol TVL.

For protocols already implementing compliance infrastructure, the Agent Trust Score also extends the KYT monitoring timeline backward – connecting transaction monitoring at the agent level to the historical record of the human behind the agent. Our Web3 Analytics Tools comparison for 2026 covers how agent-level intelligence integrates with broader protocol analytics stacks.

The Five Agent Trust Score Tiers – What Each One Means for Your Protocol

The Agent Trust Score produces a single number between 0 and 1000, mapped to five tiers. Each tier has a distinct operational meaning for DeFi protocol builders – and a distinct set of recommended actions. Understanding what produces each tier helps protocol teams calibrate their threshold decisions correctly.

Tier 5 – Sovereign (800-1000)

Sovereign agents have an established owner wallet with strong on-chain history, a clean or CEX-verified feeder address, no criminal record signals, and no farm detection flags. Trust delegation produces a high effective wallet score even for fresh agent payment wallets. Sovereign-tier agents are suitable for the highest-risk autonomous operations – large-value lending, treasury management, governance participation with material financial consequences. Protocol teams can grant Sovereign agents the same execution permissions they would grant to established protocol participants.

Tier 4 – Trusted (600-799)

Trusted agents have a strong owner wallet, an available and generally clean feeder address, and no hard-cap signals from criminal record checks. The score may be below 800 because the owner wallet has moderate rather than extensive history, or because the agent wallet has minimal activity offset by partial trust delegation. Trusted agents are suitable for standard DeFi integrations – trading agents, yield optimisers, and automated compliance workflows – where the individual transaction risk is moderate and human monitoring is available as a backstop.

Tier 3 – Provisional (400-599)

Provisional agents show mixed signals. The owner wallet may have limited history, the feeder address may be unknown or unverified, or the agent wallet may be very fresh with insufficient trust delegation from the owner score to compensate. Provisional agents should not be granted unsupervised autonomous execution access for high-value operations. However, they are appropriate for lower-risk automated workflows with active monitoring – for example, read-only data queries, low-value token swaps, or agentic onboarding flows where individual transaction size is capped. DeFi protocols integrating Provisional agents should implement transaction volume limits and velocity monitoring as additional safeguards.

Tier 2 – Elevated Risk (200-399)

Elevated Risk agents have weak owner history, obfuscated feeder addresses, soft farm detection signals, or agent wallets that score poorly even after trust delegation. These agents should not be permitted autonomous financial execution. If a protocol needs to serve Elevated Risk agents – for example, in a permissionless DEX context – transaction size limits, velocity caps, and real-time monitoring should all be active. The FEEDER_UNKNOWN flag on an Elevated Risk agent is a particularly notable combination: it indicates both limited owner history and deliberate funding obfuscation, suggesting a higher likelihood of coordinated fraud activity.

Tier 1 – Untrusted (0-199)

Untrusted agents have active fraud signals, confirmed rug pull or honeypot history, confirmed farm detection, sanctioned address exposure, or blacklisted repeat offender status. These agents should not receive autonomous execution access under any circumstances. The score is not borderline – it reflects definitive fraud signals from immutable on-chain history. Untrusted agents attempting to access your protocol should be blocked at the access control layer before any transaction reaches the execution layer. Furthermore, DeFi teams running compliance programs may want to log Untrusted agent interaction attempts as part of their AML reporting, as these attempts represent potential fraud activity on record. For the full compliance context, see our MiCA Compliance for DeFi learn page.

How ChainAware Compares to Other Agent Trust Platforms in 2026

The agent trust scoring market emerged rapidly alongside ERC-8004’s mainnet launch in January 2026. Several platforms have moved quickly to stake positions in the space. Understanding the differentiation between them matters for DeFi protocol builders choosing integration partners.

RNWY is the most established competitor in the ERC-8004 space and uses a sophisticated review-quality analysis that detects coordinated fake review patterns. However, their core methodology solves fake reviews, not fake owners. ChainAware solves fake owners – a harder problem with higher-stakes implications for autonomous execution trust. Both signals are complementary; they are not substitutes for each other.

AXIS T-Score is entirely off-chain – it scores agent runtime performance across 11 behavioral dimensions rather than on-chain ownership identity. This makes it useful for evaluating how well an agent executes tasks, but irrelevant for trust decisions about the human behind the agent. For a protocol deciding whether to grant autonomous execution access, AXIS covers a different question than ChainAware does.

The feeder address, criminal record, and trust delegation signals are currently unique to ChainAware across all indexed agent trust platforms. Those signals require a database of over one year of on-chain pair history, a token audit data pipeline, and a predictive fraud model trained on 20M+ wallet personas – infrastructure that takes years to build and cannot be replicated quickly. Additionally, for more context on how ChainAware positions against broader analytics alternatives, see our Web3 Analytics Tools Comparison for DeFi Dapps in 2026.

The moat is the data, not the formula

ChainAware publishes the categories of signals that feed into the Agent Trust Score. However, the exact weights, thresholds, and model coefficients are not published – not because the methodology is proprietary for competitive reasons, but because publishing thresholds would allow bad actors to calibrate their behavior to stay just below each detection cap.

More importantly, the real moat is not the formula. The moat is the data. An operator who knows every weight and threshold in the Agent Trust Score formula still cannot change their on-chain history. A wallet that created a honeypot token in November 2025 cannot remove that event from the blockchain. A feeder address that funded rug pull operators throughout 2025 cannot alter its transaction graph. The formula can be known. The data cannot be changed. That asymmetry is what makes on-chain behavioral intelligence a durable trust infrastructure rather than a gameable reputation system.

Frequently Asked Questions

What chains does the Agent Trust Score cover?

ChainAware’s Agent Trust Score indexes ERC-8004 agents across Ethereum mainnet, BSC (BNB Chain), Base, and Avalanche C-Chain, with Mantle in progress. These five chains cover the majority of ERC-8004 registry activity. The owner wallet and feeder analysis draws on ChainAware’s broader behavioral intelligence database, which covers 8 blockchains total including Polygon, TON, TRON, and HAQQ.

How long does the Agent Trust Score API take to respond?

The Agent Trust Score API returns results in under 100ms for agents already in the ChainAware database. First-time scoring of a newly registered agent may take slightly longer as the owner and feeder addresses are resolved and scored. Pre-scoring of agents during indexing ensures that the vast majority of ERC-8004 agents in the registry return sub-100ms scores at query time.

Does the Agent Trust Score require any PII or KYC data?

No. The Agent Trust Score is derived entirely from public on-chain data. No personal information is collected, no identity verification is required, and no data is stored beyond what is already publicly available on the blockchain. This makes the score compatible with DeFi’s privacy-first ethos and compliant with GDPR and similar privacy regulations by design.

Can an agent improve its score over time?

Yes – through the owner wallet’s behavioral history, not through the agent wallet itself. As the owner wallet accumulates genuine on-chain experience, interacts with a broader range of protocols, and maintains a clean fraud probability score, the Reputation Score feeding into the Agent Trust Score improves. Trust delegation then carries that improved score to the agent wallet. However, criminal record signals (rug pull history, honeypot creation) are permanent hard caps – they do not improve over time because the underlying on-chain events are immutable.

What happens when an agent is transferred to a new owner?

ERC-8004 agents are ERC-721 NFTs and can be transferred between wallets. When ChainAware detects an ownership transfer, the Agent Trust Score recalculates using the new owner wallet’s behavioral history. This is intentional: the trust score tracks the current controlling entity, not the original registrant. Consequently, an agent cannot inherit a previous owner’s strong score after transfer – each new owner is scored from their own on-chain history.

How does Agent Trust Score integrate with the Prediction MCP?

The Agent Trust Score is available as a native tool through ChainAware’s Prediction MCP server. Any Claude-based agent can call agent_trust_score(chain_id, agent_id) as a natural language tool call, receiving the structured score and flags response without custom API integration code. For protocol teams building on the MCP stack, this means agent verification can be added to any existing MCP-connected workflow in minutes rather than days.

Is the Agent Trust Score different from the Wallet Reputation Score?

The Agent Trust Score uses the same 0-1000 scale and the same underlying Reputation Score formula as ChainAware’s Wallet Reputation Score. However, it applies that formula to multiple addresses simultaneously (owner wallet, agent wallet, feeder address) and combines them using trust delegation logic and fleet-level farm detection signals that do not exist in the standalone Wallet Reputation Score. The two scores are directly comparable on the same scale – a wallet Reputation Score of 750 and an Agent Trust Score of 750 represent the same trust tier.

How does ChainAware handle agents with no traceable feeder address?

When the feeder address cannot be determined – either because the owner wallet was funded through multi-hop paths that obscure the source, or through infrastructure (bridges, faucets) that does not produce an attributable single feeder – ChainAware applies a feeder-unknown penalty to the Agent Trust Score. This penalty reflects the information asymmetry: legitimate operators funded through normal CEX withdrawals produce traceable feeder paths; operators who route funds to obscure the source are doing so for a reason. The penalty is not a hard cap – a very strong owner wallet and clean criminal record can partially offset it. Nevertheless, unknown feeder remains a risk signal that appears in the API response as the FEEDER_UNKNOWN flag.

What does a DeFi credit scoring integration look like alongside Agent Trust Score?

For lending protocols specifically, Agent Trust Score and DeFi credit scoring serve complementary functions. The Agent Trust Score answers “should this agent be permitted to interact with my protocol at all?” – a gate decision. The DeFi credit score answers “given that this agent is permitted, what collateral ratio and interest rate tier should apply to its lending activity?” – a parameterisation decision. Running both checks in sequence gives lending protocols the most complete picture: a verified legitimate agent operating at its correct creditworthiness tier.

Further Reading

• Agent Trust Score – Complete Methodology – the full technical explanation of how the score is computed, including all five scoring layers and flag definitions
• Web3 Wallet Auditing Providers in 2026 – the complete landscape of wallet intelligence providers, from raw data to actionable predictions
• AI-Powered Blockchain Analysis for Crypto Security – how ChainAware’s fraud prediction model achieves 98% accuracy
• Rug Pull vs Pump and Dump – the fraud patterns that feed the Agent Trust Score criminal record database
• Blockchain Compliance for DeFi: KYT and AML Guide – regulatory context for DeFi protocol compliance in 2026
• DeFi Credit Score Platforms Compared – how to combine agent trust verification with borrower creditworthiness assessment
• Prediction MCP Setup Guide – add ChainAware behavioral intelligence to any Claude agent in minutes
• 32 Ready-Made Agents – pre-built Claude agents including agent verification, fraud detection, and compliance screening
• Web3 Analytics Tools for Dapps: Complete Comparison – where agent trust scoring fits in the broader DeFi analytics stack
• Blockchain Data Providers for AI Agents – the data infrastructure layer that feeds agent intelligence systems

ChainAware.ai is the Web3 Agentic Growth Infrastructure – behavioral intelligence for DeFi protocols, AI agents, and individual crypto users. 20M+ wallet personas, 98% fraud detection accuracy, <100ms API latency across 8 blockchains. Try free at chainaware.ai.

The post The First Step in Agentic Commerce Isn’t Integration. It’s Trust. first appeared on ChainAware.ai.

ChainAware was named in CB Insights’ AI Fraud Prevention Market Map alongside Chainalysis, Elliptic, and TRM Labs – and remains the only Web3 AI token across all 200+ companies in that list. The 32 sub-agents documented here are the operational engine behind that recognition: real, deployed tools that DeFi protocols, compliance teams, launchpads, DAOs, and AI agent developers use in production today. Every agent is open-source, MIT-licensed, and available at github.com/ChainAware/behavioral-prediction-mcp .

This article classifies all 32 agents into two functional categories – Fraud Tech and Growth Tech – and for each agent provides a precise description, concrete use case, and the specific trigger conditions that signal when a team needs it. Use this as your reference guide for selecting, combining, and deploying ChainAware’s agent suite.

In This Article

• Two Categories – Fraud Tech and Growth Tech
• The Complete Classification Table – All 32 Agents
• Fraud Tech Agents – 17 Agents, Complete Reference
• Growth Tech Agents – 15 Agents, Complete Reference
• How Agents Compose Into Pipelines
• Getting Started – Integration in Three Steps
• Frequently Asked Questions

Two Categories – Fraud Tech and Growth Tech

ChainAware’s 32 agents divide into two functional categories that reflect the platform’s core thesis: the same behavioral data that prevents fraud also drives growth. Both categories draw from the same underlying Prediction MCP tools and the same 20M+ wallet persona database. The distinction lies in what question each agent answers and what action it enables.

Fraud Tech agents answer: “Can we trust this wallet, contract, token, or transaction?” They protect protocols from losses, enforce AML compliance, prevent Sybil attacks, and screen counterparties before execution. Consequently, Fraud Tech agents operate primarily at the gate – before onboarding, before transactions, before token distributions, before listing decisions. Their outputs are verdicts: allow, block, flag, reject, or escalate.

Growth Tech agents answer: “Now that we know this wallet is legitimate, how do we convert it, retain it, and grow it?” They turn behavioral intelligence into personalized acquisition, onboarding, conversion, and retention decisions. Moreover, Growth Tech agents operate primarily post-gate – after a wallet passes initial screening, they determine how to engage it most effectively. Their outputs are recommendations: which product to surface, which message to send, which onboarding flow to show, which upsell to offer.

Furthermore, both categories share a fraud gate: every Growth Tech agent checks probabilityFraud before generating any recommendation and blocks output for high-risk wallets. This means the categories are not sequential stages but parallel layers – fraud protection runs continuously across every growth decision. For the foundational framework explaining why behavioral intelligence is essential for both fraud prevention and growth, see our Web3 Behavioral User Analytics guide .

The Complete Classification Table – All 32 Agents

The table below lists every agent with its category, primary MCP tool, supported networks, and core function. Agents are sorted by category, then by specificity – from broad-purpose agents to narrow specialists. Use this as your quick-reference lookup before reading the detailed descriptions that follow.

#	Agent	Category	Primary Tool	Networks	Core Function
1	fraud-detector	Fraud Tech	predictive_fraud	ETH BNB POLYGON TON BASE TRON HAQQ	Wallet fraud probability (98% accuracy) + 19 AML forensic flags
2	rug-pull-detector	Fraud Tech	predictive_rug_pull	ETH BNB BASE HAQQ	90.1% rug pull prediction – contract + deployer behavioral analysis
3	aml-scorer	Fraud Tech	predictive_fraud	ETH BNB POLYGON TON BASE TRON HAQQ	AML score (0-100) with full forensic flag breakdown
4	trust-scorer	Fraud Tech	predictive_fraud	ETH BNB POLYGON TON BASE TRON HAQQ	Trust score (0.00-1.00) = 1 − fraud probability. Composable building block
5	sybil-detector	Fraud Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	Batch Sybil detection – wallet farms, coordinated attacks, proxy voting fraud
6	governance-screener	Fraud Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	DAO voter tier (Core Contributor → Disqualified) + voting weight multiplier
7	counterparty-screener	Fraud Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	Pre-transaction Safe / Caution / Block verdict in a single API call
8	compliance-screener	Fraud Tech	Orchestrator	Multi-chain via sub-agents	MiCA-aligned PASS / EDD / REJECT with full documented evidence trail
9	transaction-monitor	Fraud Tech	predictive_behaviour + predictive_rug_pull	ETH BNB BASE HAQQ SOL + fallback	Real-time ALLOW / FLAG / HOLD / BLOCK for autonomous agent pipelines
10	token-launch-auditor	Fraud Tech	predictive_rug_pull + predictive_fraud	ETH BNB BASE HAQQ	Launchpad listing audit → APPROVED / CONDITIONAL / REJECTED + safety badge
11	airdrop-screener	Fraud Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	Batch airdrop eligibility – filters bots, ranks eligible wallets by reputation
12	rwa-investor-screener	Fraud Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	RWA investor suitability → QUALIFIED / CONDITIONAL / REFER_TO_KYC / DISQUALIFIED
13	gamefi-screener	Fraud Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	P2E bot farm and multi-account cheater detection + player tier classification
14	credit-scorer	Fraud Tech	credit_score	ETH	Crypto credit score (1-9) combining fraud probability + social graph analysis
15	lending-risk-assessor	Fraud Tech	predictive_behaviour + credit_score	ETH BNB BASE HAQQ SOL	Borrower risk grade (A-F) + recommended collateral ratio + interest rate tier
16	portfolio-risk-advisor	Fraud Tech	predictive_rug_pull + token_rank_single	ETH BNB BASE HAQQ	Portfolio rug pull scan → grade A-F + prioritized exit/reduce plan
17	agent-screener	Fraud Tech	predictive_fraud + predictive_behaviour + predictive_rug_pull	ETH BNB BASE HAQQ SOL + fallback	AI agent trust score (0-10) screening agent wallet + feeder wallet
18	wallet-auditor	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Complete 22-dimension Web3 Persona – fraud + behavioral + personalization
19	reputation-scorer	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Reputation score (0-1000) = experience × risk_capability × (1 − fraud)
20	wallet-ranker	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Global wallet rank from experience, total points, age, transaction count
21	whale-detector	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Whale tier (Mega / Whale / Emerging) + Active/Dormant status + domain
22	ltv-estimator	Growth Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	12-month revenue potential (USD range) from behavioral + risk signals
23	lead-scorer	Growth Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	Lead score (0-100) + Hot/Warm/Cold/Dead + recommended outreach angle
24	wallet-marketer	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Hyper-personalized marketing message (max 20 words) from on-chain signals
25	platform-greeter	Growth Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	Platform-specific welcome message (max 35 words) – different per platform
26	onboarding-router	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Onboarding flow decision – Beginner / Intermediate / Skip from real experience
27	defi-advisor	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Personalized DeFi product recommendations (3 tiers) by experience + risk
28	upsell-advisor	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Upgrade readiness (0-100) + next product + trigger event + conversion probability
29	cohort-analyzer	Growth Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	Batch behavioral cohort segmentation – 8 cohorts + per-cohort strategy
30	token-ranker	Growth Tech	token_rank_list	ETH BNB BASE SOL	Token discovery by community strength – AI / RWA / DeFi / DeFAI / DePIN
31	token-analyzer	Growth Tech	token_rank_single + predictive_fraud	ETH BNB BASE SOL	Single-token deep-dive: community rank + top holder profiles + fraud screening
32	marketing-director	Growth Tech	Orchestrator (7 specialist agents)	All networks via sub-agents	Full-cycle campaign orchestrator → complete Marketing Campaign Brief

Fraud Tech Agents – 17 Agents, Complete Reference

ChainAware’s Fraud Tech agents protect Web3 protocols from the full spectrum of on-chain threats: wallet fraud, rug pulls, money laundering, Sybil attacks, governance manipulation, P2E cheating, and fraudulent AI agents. Together, they cover every point in the protocol lifecycle where malicious actors attempt to extract value – from the moment a wallet first connects to the moment a transaction executes. According to FATF’s Virtual Assets Recommendations , the compliance requirements for crypto asset service providers now demand pre-execution risk assessment that legacy forensic tools were never designed to deliver. ChainAware’s Fraud Tech agents fill that gap with predictive behavioral intelligence rather than reactive forensic lookup.

Moreover, these agents share a critical structural advantage over traditional blockchain forensics: they analyze behavioral patterns across 20M+ wallet personas rather than matching against static blocklists. Professional fraud operators deliberately evade blocklist-based tools by using fresh wallets and clean contract code. They cannot, however, mask their behavioral fingerprint – the pattern of on-chain activity that identifies an operator regardless of which specific address they use today. This is why ChainAware achieves 98% fraud detection accuracy on ETH where forensic tools frequently miss sophisticated operators. For the complete technical comparison, see our Forensic vs AI-Powered Analytics guide .

1. chainaware-fraud-detector

The flagship fraud detection agent calls predictive_fraud on any wallet address and returns a fraud probability score, wallet status (Not Fraud / Fraud / New Address), OFAC sanctions check, and 19 AML forensic flags covering mixers, darknet transactions, phishing wallets, fake token creation, money laundering patterns, cybercrime associations, and more. Accuracy reaches 98% on ETH and 96% on BNB, backtested against CryptoScamDB – the largest publicly available database of documented crypto fraud incidents. Coverage spans 7 networks: ETH, BNB, POLYGON, TON, BASE, TRON, and HAQQ.

Use Case: A DeFi lending protocol screens every wallet requesting a loan before processing the application. The team integrates chainaware-fraud-detector into its onboarding API – each new wallet receives a fraud probability score and forensic flag check in under one second. Wallets scoring above 0.70 are automatically declined. Wallets between 0.40 and 0.70 route to enhanced due diligence. Wallets below 0.20 pass to the standard lending flow. The same agent works equally well for exchange KYC pre-screening, NFT allowlist vetting, and airdrop participant verification.

When Is It Required: Deploy chainaware-fraud-detector whenever a protocol accepts wallet connections from unknown participants – particularly before any value transfer, credit extension, or whitelist grant. It is specifically required when a protocol falls under MiCA, AML5D, or equivalent regulation that mandates pre-onboarding risk assessment. Additionally, it is required before running any Growth Tech agent on a wallet – the fraud gate in chainaware-wallet-marketer and chainaware-ltv-estimator calls this agent’s underlying tool before generating any recommendation. For the complete implementation methodology, see our Fraud Detector guide .

2. chainaware-rug-pull-detector

Analyzes smart contracts, liquidity pools, and token launches for rug pull risk before any capital is deployed. The agent runs predictive_rug_pull on the contract address and predictive_fraud on the deployer wallet, combining both into a unified verdict. Critically, the deployer fraud score can escalate the overall verdict by one tier – a contract scoring 0.35 (Medium risk) paired with a deployer scoring 0.72 (High risk) produces a combined High Risk verdict. This escalation catches the most dangerous category of rug pulls: professionally deployed clean contracts by operators with documented fraud histories on other wallets. Accuracy on the PancakeSwap V2 dataset reaches 90.1%, covering $569M in documented rug pull losses from weeks 1-20 of 2026. Networks supported: ETH, BNB, BASE, HAQQ.

Use Case: A DEX launchpad reviews 50 new token submissions per week. Without automated screening, each review requires a developer to manually inspect contract code and trace the deployer wallet – a process taking 30-60 minutes per token. With chainaware-rug-pull-detector, the launchpad runs all 50 contracts in batch mode and receives a ranked risk table in minutes. Contracts scoring above 0.80 are automatically rejected. Contracts between 0.50 and 0.80 require manual review with specific red flags already identified. Contracts below 0.20 proceed to standard listing.

When Is It Required: Use chainaware-rug-pull-detector before listing any token on a DEX, before depositing LP into any new pool, before investing in any IDO or pre-sale, and before any yield vault strategy deploys capital into a new protocol. It is specifically required for launchpad teams that need a standardized, reproducible audit process rather than ad hoc developer reviews. It pairs with chainaware-token-launch-auditor when a full public-facing audit report with a safety badge is needed. For the detailed comparison against GoPlus, Token Sniffer, and Honeypot.is, see our Rug Pull Detection Tools guide .

3. chainaware-aml-scorer

Calculates a structured AML score (0-100) using a two-branch logic that separates forensic compliance from probabilistic fraud risk. If any forensic flag is present – mixer usage, sanctioned entity association, stolen funds link, darknet transaction, ransomware wallet interaction – the AML score is 0 regardless of the fraud probability score. This hard-zero rule reflects regulatory reality: a forensic flag requires human review and escalation regardless of the overall risk probability. When forensics are clean, the AML score equals (1 − probabilityFraud) × 100, providing a continuous risk gradient for compliance tiering. The agent returns the complete forensic breakdown alongside the score, producing output that is audit-ready for regulatory review under MiCA and equivalent frameworks.

Use Case: A crypto exchange onboards 500 new wallets per day and must document AML screening decisions for regulatory reporting. Previously, the compliance team ran manual checks on wallets flagged by a basic blocklist – a process that missed sophisticated operators and created a documentation backlog. With chainaware-aml-scorer, every onboarding wallet receives an automated AML report in under one second. Wallets scoring 0 (forensic flag detected) escalate to the compliance team with the specific flags identified. Wallets scoring 71-100 receive automated approval documentation. Wallets in the 41-70 range trigger enhanced due diligence with a specific set of additional checks, creating a complete and auditable compliance trail for every onboarded wallet.

When Is It Required: Deploy chainaware-aml-scorer for any platform falling under AML/CFT regulatory requirements – exchanges, OTC desks, lending protocols, and any DeFi platform accepting significant TVL from institutional wallets. It is also required when chainaware-compliance-screener is the orchestrating agent, since compliance-screener calls aml-scorer as one component of its structured MiCA-aligned report. See our MiCA Compliance guide for the full regulatory compliance stack.

4. chainaware-trust-scorer

Returns a single trust score using one formula: Trust Score = 1 − fraud_probability. The output ranges from 0.00 (confirmed fraud) to 1.00 (zero fraud probability). Designed as a composable building block rather than a standalone product, trust-scorer feeds into other calculations across the agent suite: reputation score uses it as the base fraud penalty, AML score uses it as the clean-forensics branch, governance vote weighting multiplies by it, and marketing campaign gates use it as a minimum threshold before message generation. Covers 7 networks via predictive_fraud. Response time is sub-100ms by design, making it the fastest agent in the suite.

Use Case: A developer building a custom reputation system for their DeFi protocol needs a standardized trust signal to combine with their own on-chain activity metrics. Rather than building a fraud detection model from scratch, they integrate chainaware-trust-scorer as the fraud component and combine it with their own activity score. The resulting composite score inherits ChainAware’s 98% fraud accuracy while adding protocol-specific activity signals that ChainAware’s general model does not capture. The trust score’s mathematical cleanliness – it is simply the complement of fraud probability – makes it easy to incorporate into any scoring formula.

When Is It Required: Use chainaware-trust-scorer whenever a custom scoring formula needs a standardized, high-accuracy fraud component – governance vote weighting, airdrop allocation, lending collateral ratios, and marketing campaign eligibility gates all benefit from incorporating the trust score as a fraud signal. It is the recommended starting point for teams building composite scores rather than using a pre-built agent, since its output is mathematically clean and directly interpretable.

5. chainaware-sybil-detector

Batch-screens wallet lists for Sybil attacks, coordinated voting fraud, and wallet farm operations. Beyond individual wallet scoring, the agent applies four pattern detection rules across the full submitted set: a cluster flag triggers when 10%+ of wallets share experience scores within ±0.2 points and were created in the same approximate period – the signature of a coordinated wallet farm. A fraud concentration flag triggers when 20%+ of voters show fraud probability above 0.25. A new wallet surge flag triggers when 30%+ of wallets have experience below 1.5. A uniform risk profile flag triggers when 60%+ share identical behavioral categories, indicating coordination rather than organic community diversity. Each wallet is classified as ELIGIBLE, REVIEW, or EXCLUDE, and the cleaned voter list is ready for Snapshot or on-chain governance integration.

Use Case: A DAO preparing a governance vote on a $2M treasury allocation notices unusual activity: 400 new wallets registered in the 48 hours before the vote, all with minimal transaction history. Running chainaware-sybil-detector on the full voter list identifies 312 of those 400 wallets as part of a coordinated new-wallet cluster, disqualifying them from the vote. The attack is neutralized before it reaches quorum. The cleaned voter list shows genuine community support from 89 ELIGIBLE voters, and the vote proceeds with integrity intact.

When Is It Required: Run chainaware-sybil-detector before any governance vote controlling significant treasury funds, parameter changes, or upgrade authority. It is specifically required before Snapshot votes for DAOs with public token distribution, before on-chain governance proposals reaching quorum thresholds, and before any delegation validation process where vote weight can be amplified through coordinated proxy delegation. For the complete governance protection framework, see our Governance Screeners guide .

6. chainaware-governance-screener

DAO voter screening with four-tier classification and voting weight calculation. The agent assigns each wallet to one of five tiers: Core Contributor (experience ≥ 8, fraud ≤ 0.10, protocols ≥ 5 → 2.0× multiplier), Active Member (experience ≥ 5, fraud ≤ 0.25, protocols ≥ 2 → 1.5×), Participant (experience ≥ 2, fraud ≤ 0.40 → 1.0×), Observer (new address or experience < 2 with low fraud → 0.5×), and Disqualified (fraud gate fails → 0.0×). Within each tier, the multiplier adjusts downward for elevated fraud probability. Three governance models are supported: token-weighted, reputation-weighted (ChainAware reputation score as direct weight), and quadratic (multiplier applies to square root of token balance).

Use Case: A DeFi protocol wants to implement reputation-weighted governance to counteract plutocracy – the tendency of token-weighted systems to concentrate governance power in the largest holders regardless of protocol engagement. Using chainaware-governance-screener in reputation-weighted mode, every voter’s influence is determined by behavioral quality rather than token balance alone. A Core Contributor holding 1,000 tokens has more governance weight than a dormant whale holding 100,000 tokens but showing no protocol engagement. The result is governance that rewards genuine contributors rather than passive large holders.

When Is It Required: Deploy chainaware-governance-screener for any DAO that needs to validate voter quality before a proposal goes live. It is particularly required for protocols implementing reputation-weighted or quadratic voting models, for DAOs with public token distributions vulnerable to Sybil accumulation, and for any governance system where a single bad-faith actor could acquire enough voting power to pass a malicious proposal. It works alongside chainaware-sybil-detector – the Sybil detector identifies coordinated wallet farms, while governance-screener classifies remaining legitimate voters by quality tier.

7. chainaware-counterparty-screener

Pre-transaction safety agent optimized for minimum latency and maximum decisiveness. A single predictive_behaviour call retrieves both the fraud probability and the behavioral context needed for ambiguous cases – eliminating the two-call pattern that adds latency to pre-transaction flows. The verdict logic applies decisive rules first (confirmed fraud or forensic flag → immediate Block; fraud probability ≤ 0.15 → immediate Safe) and contextual rules only for the 0.16-0.70 range. Transaction-type context adjusts the risk assessment: approve actions receive a 1.3× risk multiplier, bridge and liquidity actions 1.2×, stake actions 0.9×. Compact mode returns a single line for autonomous agent pipelines.

Use Case: A DeFi aggregator routes user transactions across multiple protocols and counterparties. Before executing any multi-hop route, the aggregator’s AI agent calls chainaware-counterparty-screener on every intermediate counterparty address. A Block verdict causes the agent to find an alternative route avoiding the flagged address. A Caution verdict triggers additional monitoring for the transaction. A Safe verdict allows execution to proceed normally. The entire screening adds under 200ms to the routing decision – negligible for a user experience that already involves multi-second blockchain confirmation times.

When Is It Required: Use chainaware-counterparty-screener immediately before signing any transaction with an unknown counterparty – particularly token approvals (highest risk action type), LP deposits (contract risk), bridge transactions (irreversible cross-chain exposure), and high-value transfers. For autonomous AI agents executing transactions without human review, this agent provides the fraud gate that substitutes for human judgment. It pairs naturally with chainaware-transaction-monitor: counterparty-screener handles the pre-transaction check on specific addresses, while transaction-monitor handles real-time pipeline risk scoring across sender, receiver, and contract simultaneously.

8. chainaware-compliance-screener

The most comprehensive compliance agent in the suite – a MiCA-aligned orchestrator sequencing AML scoring, fraud detection, and transaction risk assessment into a single structured Compliance Report with a three-tier verdict: PASS, ENHANCED DUE DILIGENCE, or REJECT. Unlike the individual specialist agents, compliance-screener is specifically designed to produce documentation: every signal, every flag, every threshold applied is recorded in the output, creating an audit trail that compliance officers can present to regulators. The verdict structure mirrors MiCA’s layered compliance approach – PASS wallets proceed normally, EDD wallets receive additional checks before service, REJECT wallets are declined with specific reasons documented.

Use Case: A crypto asset service provider (CASP) operating under MiCA needs to document its compliance process for every customer onboarding. Manual KYC combined with blockchain forensics produces reports taking hours per customer and lacking standardization. With chainaware-compliance-screener, every onboarded wallet receives an automated, structured Compliance Report in under 5 seconds – covering sanctions screening, AML forensic flags, behavioral fraud risk, and transaction pattern analysis. The report format is consistent across all wallets, making regulatory reporting systematic rather than ad hoc. EDD cases are automatically flagged with the specific signals that triggered the enhanced review requirement.

When Is It Required: Deploy chainaware-compliance-screener for any platform regulated under MiCA, AML5D, FinCEN guidance, or equivalent frameworks requiring documented pre-onboarding risk assessment. It is specifically required when a compliance team needs to demonstrate to regulators that their screening process is systematic, documented, and applied consistently – not selectively or manually. The agent is also the right choice for institutional DeFi platforms serving accredited investors where documented compliance is a prerequisite for institutional capital access. For the complete regulatory compliance cost comparison, see our MiCA Compliance guide .

9. chainaware-transaction-monitor

Real-time transaction risk scoring designed for autonomous AI agent pipelines rather than human compliance review. The agent screens sender, receiver, and contract address simultaneously, computes a composite risk score (0-100) using weighted contributions from each address, applies action-type multipliers (approve 1.3×, bridge and liquidity 1.2×, stake 0.9×, unknown 1.1×), and returns a machine-actionable ALLOW / FLAG / HOLD / BLOCK signal. Override rules immediately produce a BLOCK regardless of composite score whenever sender or receiver carries confirmed fraud status or any AML forensic flag. Compact mode returns a single-line signal for mempool monitoring and high-frequency agent pipelines where sub-50ms response is required.

Use Case: A DeFi trading bot executes 200+ transactions per day across multiple protocols. Without transaction monitoring, the bot has no way to detect when it is being routed through a fraudulent intermediary or interacting with a compromised contract. With chainaware-transaction-monitor as a pre-execution hook, every transaction is screened in under 100ms before signing. BLOCK signals cause the bot to abort the transaction and find an alternative path. FLAG signals execute but generate a compliance log entry for review. Over a 30-day period, the monitoring prevents the bot from executing 14 transactions with BLOCK-level counterparties – including two interactions with wallet addresses later confirmed as hack-related by blockchain investigators.

When Is It Required: Deploy chainaware-transaction-monitor for any autonomous AI agent executing blockchain transactions without per-transaction human approval. This specifically includes DeFi trading bots, yield optimization agents, automated treasury management systems, and any AI agent operating under the emerging ERC-8004 standard for on-chain agent identity. It is also required for any protocol needing ongoing post-onboarding transaction screening – complementing chainaware-fraud-detector (which handles one-time onboarding checks) with continuous monitoring of user activity. For the complete integration guide, see our Transaction Monitoring guide .

10. chainaware-token-launch-auditor

Launchpad listing audit agent combining rug pull detection on the contract with full fraud and behavioral analysis on the deployer wallet. The output includes a composite Launch Safety Score, a public-facing safety badge suitable for embedding on listing pages, and specific conditions the launchpad should impose – mandatory LP lock periods, restricted admin key permissions, or vesting schedule requirements. The three-tier verdict (APPROVED, CONDITIONAL, REJECTED) gives launchpad teams a standardized decision framework they can communicate publicly to investors. CONDITIONAL listings include explicit conditions that, if met, convert the listing to APPROVED.

Use Case: An IDO launchpad receives a new project application for a DeFi token on BNB. The applying team has a polished website, a detailed whitepaper, and a professionally written smart contract that passes standard code review. However, chainaware-token-launch-auditor detects that the deployer wallet has previously deployed three tokens on ETH, all of which experienced LP withdrawal events within 72 hours of launch – a behavioral signature of serial rug pull operations. The contract score is 0.28 (Medium) but the deployer score is 0.81 (Critical), producing a REJECTED verdict. The launchpad declines the listing. Three weeks later, the same team launches the token on an unscreened DEX, where it rugs within 36 hours.

When Is It Required: Run chainaware-token-launch-auditor before approving any token listing on a launchpad or DEX maintaining listing standards. It is specifically required for platforms displaying a safety badge or endorsement alongside listed tokens – without auditor-backed evidence, any safety claim creates legal and reputational liability. The agent is also required for any accelerator or incubator program vetting projects before providing funding or platform access. It works as a pre-listing screening gate for token sale platforms where retail investors rely on the platform’s due diligence.

11. chainaware-airdrop-screener

Batch airdrop eligibility engine that filters fraud wallets, bots, and Sybil clusters from token distribution lists, then ranks eligible wallets by ChainAware’s reputation formula for merit-based allocation. Five disqualification rules apply in order: fraud probability above 0.70 → HIGH FRAUD disqualified; confirmed fraud status → CONFIRMED FRAUD disqualified; new address with fraud above 0.40 → SUSPICIOUS NEW disqualified; new address with zero experience and no categories → BOT/FRESH disqualified; any AML forensic flag → AML FLAG disqualified. Surviving wallets receive a reputation score calculated as (1000/110) × (experience + 1) × (risk_capability + 1) × (1 − fraud_probability) and are assigned allocation multipliers from 0.5× (Low Score) to 4× (Elite). When a token budget is provided, the agent calculates exact per-wallet token allocations ready to plug into a Merkle tree contract.

Use Case: A DeFi protocol distributes 10 million tokens across 5,000 wallet addresses collected through a six-week quest campaign. Without screening, ChainAware’s analysis of similar campaigns finds that approximately 84% of campaign participants are ghost wallets – addresses with zero real engagement that bot operators control mechanically. Running chainaware-airdrop-screener on the 5,000 addresses disqualifies 3,420 as bots, fraud, or suspicious new wallets. The remaining 1,580 eligible wallets are ranked by reputation score and receive allocations scaled from 0.5× to 4× of the base amount. The protocol distributes tokens to genuine community members, avoids immediate sell pressure from farming wallets, and creates a foundation of quality token holders.

When Is It Required: Run chainaware-airdrop-screener before every token distribution event – regardless of campaign size. It is specifically required for distributions above 100,000 USD equivalent where bot farming has high economic incentive, for any distribution including vesting where recipient quality affects long-term token price stability, and for governance token airdrops where recipient quality directly affects the quality of future governance participation. The agent pairs naturally with chainaware-sybil-detector (which identifies coordination patterns before disqualification) and chainaware-reputation-scorer (which provides the ranking formula for tiered allocations).

12. chainaware-rwa-investor-screener

Real World Asset investor suitability screening assessing three dimensions simultaneously: AML/fraud compliance (40% weight), investor sophistication via on-chain experience score (35%), and risk profile alignment against the RWA’s declared risk tier (25%). The composite Suitability Score (0-100) maps to four tiers: QUALIFIED (full access, standard caps), CONDITIONAL (reduced cap, enhanced monitoring), REFER_TO_KYC (on-chain profile insufficient, route to manual KYC), and DISQUALIFIED (fraud gate, AML flag, or confirmed fraud). Recommended investment caps are tied to experience level within each tier – a QUALIFIED Sophisticated investor has no cap, while a QUALIFIED Intermediate investor caps at $25,000. Three RWA risk tiers define minimum experience thresholds: conservative (≥ 2.0), moderate (≥ 4.0), aggressive (≥ 6.5).

Use Case: A tokenized real estate platform onboards investors for a $50M moderate-risk RWA offering. Traditional KYC takes 3-5 days per investor. The platform needs to process 2,000 investor applications in a two-week window before the offering closes. Chainaware-rwa-investor-screener processes all 2,000 wallets in batch mode in under 10 minutes, classifying 1,240 as QUALIFIED, 380 as CONDITIONAL, 210 as REFER_TO_KYC, and 170 as DISQUALIFIED. The 170 disqualified wallets are excluded immediately. The 1,620 QUALIFIED and CONDITIONAL wallets complete automated onboarding in minutes – dramatically reducing compliance cost and time-to-investment for legitimate investors.

When Is It Required: Deploy chainaware-rwa-investor-screener for any tokenized asset platform needing automated investor suitability assessment. It is specifically required when traditional KYC throughput is insufficient for the number of investors the platform needs to process, when the regulatory framework requires documented suitability assessment rather than just AML screening, and when the platform offers products across multiple risk tiers requiring different investor qualification standards. It complements chainaware-compliance-screener (which handles AML compliance) by adding the investor sophistication and product suitability dimensions that pure AML screening does not cover.

13. chainaware-gamefi-screener

Play-to-Earn bot farm and multi-account cheater detection for Web3 games. The agent screens wallets connecting to a P2E platform for bot signatures (coordinated transaction timing, uniform behavioral patterns, zero genuine game interaction history), multi-account cheating (same operator controlling multiple wallets extracting parallel rewards), and reward abuse patterns (wallets appearing across multiple P2E reward events in behavioral coordination). Legitimate players are classified into experience tiers for matchmaking and receive P2E reward eligibility scores scaling allocations by behavioral quality. The fraud gate disqualifies wallets above 0.70 fraud probability regardless of game-specific behavior.

Use Case: A P2E game launches a tournament with $100,000 in prize pool rewards. Within 48 hours, 40% of tournament participants are identified as bot farms – coordinated wallet clusters playing mechanically to extract rewards without genuine gameplay. Chainaware-gamefi-screener deployed at tournament registration identifies the bot wallets before they accumulate rewards. The disqualified wallets are excluded. Remaining players are classified into tiers from Beginner to Expert and receive reward multipliers (0.5× to 4×) scaled to their on-chain gaming experience. Prize pool distribution shifts from bot-dominated to skill-correlated, improving tournament integrity and the genuine player community’s experience.

When Is It Required: Run chainaware-gamefi-screener at every P2E tournament registration, every in-game reward event, and every NFT loot drop in a play-to-earn context. It is specifically required for any P2E game with real economic value at stake – when rewards are worth more than the cost of running bots, bot farms appear without exception. The agent is also required for scholarship programs in P2E games, where scholarship managers need to verify that scholar wallets are controlled by genuine individual players rather than farming operations controlling multiple scholarship slots simultaneously.

14. chainaware-credit-scorer

Returns a crypto credit score from 1 to 9 using ChainAware’s credit_score tool, combining fraud probability with social graph analysis of the wallet’s transaction network. Score 9 is Prime (highest creditworthiness, best lending terms). Score 1 is Very High Risk (decline lending). Currently supported on ETH only, where social graph data density is highest. The credit score is the simplest borrower signal in the suite – designed specifically as a composable building block that chainaware-lending-risk-assessor combines with experience score and risk appetite to produce a full Borrower Risk Grade.

Use Case: A DeFi lending protocol wants to offer differentiated interest rates based on borrower quality – lower rates for high-credit-score borrowers to attract and retain the best users, higher rates for lower-credit-score borrowers to compensate for elevated default risk. Chainaware-credit-scorer provides the credit signal driving the rate differentiation. Prime borrowers (score 9) receive the protocol’s best rate. High-Risk borrowers (score 1-2) are declined or required to over-collateralize at 200%+. The differentiation improves risk-adjusted revenue and creates a meaningful incentive for borrowers to maintain clean on-chain behavior over time.

When Is It Required: Use chainaware-credit-scorer as a component within chainaware-lending-risk-assessor for full borrower risk assessment, or standalone when a simple 1-9 credit rating is sufficient for the use case. It is specifically required for ETH-based lending protocols wanting a standardized credit signal compatible with the broader DeFi lending ecosystem. For multi-chain lending platforms, chainaware-lending-risk-assessor provides broader coverage by combining the credit score with behavioral signals from the full Prediction MCP toolset. See our Credit Score guide for the complete methodology.

15. chainaware-lending-risk-assessor

Full borrower risk assessment for DeFi lending protocols – combining fraud probability, on-chain experience score, risk appetite classification, and (on ETH) credit score into a Borrower Risk Grade from A to F with specific recommended collateral ratio and interest rate tier. Grade A borrowers (low fraud, high experience, appropriate risk profile) receive the best terms. Grade F borrowers are declined. The agent covers ETH, BNB, BASE, HAQQ, and SOLANA – enabling multi-chain lending platforms to apply consistent underwriting standards across all supported networks using behavioral signals rather than collateral value as the only risk proxy.

Use Case: A DeFi lending protocol currently applies a flat 150% collateralization ratio to every borrower regardless of on-chain history. This approach drives away high-quality borrowers who resent over-collateralization for loans they will clearly repay. With chainaware-lending-risk-assessor, the protocol offers Grade A borrowers 110% collateralization at the best rate, Grade B borrowers 130% at standard rates, and Grade C borrowers 160% at elevated rates. Grade D-F wallets are declined or required to provide significant over-collateral. Capital efficiency improves, quality borrower acquisition increases, and risk-adjusted returns improve across the loan book.

When Is It Required: Deploy chainaware-lending-risk-assessor for any DeFi lending or credit protocol wanting to move beyond collateral-only risk assessment. It is specifically required for undercollateralized or uncollateralized DeFi lending products, where behavioral risk signals are the primary protection against default. Additionally, it is required for any lending protocol seeking to compete on borrower experience by offering differentiated rates – flat-rate protocols cannot attract and retain the highest-quality borrowers who have better options elsewhere.

16. chainaware-portfolio-risk-advisor

Portfolio-level rug pull risk scan that evaluates every token in a submitted portfolio, aggregates risk into a Portfolio Risk Score (0-100) and grade (A-F), flags dangerous concentrations, and produces a prioritized exit/reduce rebalancing plan. The primary signal for each token is its rug pull probability from predictive_rug_pull. Supplementary community rank from token_rank_single enriches the risk assessment with holder quality data for the approximately 2,500-3,000 tokens covered by the pre-calculated index. Concentration flags alert when a single high-risk token represents more than 20% of portfolio value (Critical Concentration) or when multiple tokens share the same deployer (Cluster Risk).

Use Case: A DeFi investor holds 12 positions across ETH and BNB, total value $85,000. Three tokens have no community rank data and significant social media promotion – a combination warranting scrutiny. Running chainaware-portfolio-risk-advisor identifies two of those three tokens as High Risk (TRS 58 and 71), with deployer behavioral signatures consistent with previous rug pull operations. The agent produces a rebalancing plan: exit both High Risk positions immediately ($12,400 combined), reduce a Moderate Risk position to 5% of portfolio, and hold the remaining nine positions scoring Low Risk. The investor exits before the highest-risk position rugs two weeks later.

When Is It Required: Run chainaware-portfolio-risk-advisor before deploying significant new capital into any multi-token DeFi position, before any rebalancing decision in a portfolio containing tokens launched in the last 90 days, and as a regular monthly audit of any DeFi portfolio containing more than five positions. It is specifically required for protocols managing DAO treasuries or yield strategies on behalf of users, where portfolio risk is a fiduciary responsibility rather than a personal investment choice.

17. chainaware-agent-screener

The first dedicated AI agent trust scoring tool in the on-chain intelligence market. Screens two addresses simultaneously: the agent wallet (the address the autonomous agent uses to transact) and the feeder wallet (the address that funds the agent). The feeder wallet is typically the most revealing signal – a fraudulent feeder means the agent operates on behalf of a bad actor regardless of how clean the agent wallet appears. The output is a normalized Agent Trust Score from 0 to 10: 0 means confirmed or likely fraud, 1 means new address with insufficient data, and 2.0-10.0 is a normalized reputation score. When the agent wallet is a smart contract rather than an EOA, behavioral data is unavailable and the score is capped at 6.0 with a proxy calculation. This directly addresses the structural vulnerability in the ERC-8004 agent registry – 196,000+ registered agents with no behavioral trust signals attached to their on-chain identities.

Use Case: A DeFi protocol evaluating whether to accept automated interactions from third-party AI trading agents faces a core challenge: without agent trust scoring, the protocol cannot distinguish between a legitimate institutional trading bot and a fraudulent agent designed to manipulate protocol state. Running chainaware-agent-screener on each agent’s wallet and feeder wallet produces a trust score used as an access gate. Agents scoring 7.0+ receive full access. Agents scoring 4.0-6.9 receive limited access with lower transaction limits and no admin function access. Agents scoring below 4.0 or with Score 0 are blocked entirely. Score 1 (new feeder wallet) triggers a manual review before access is granted.

When Is It Required: Deploy chainaware-agent-screener whenever a protocol, DEX, lending platform, or DAO accepts or considers accepting automated interactions from third-party AI agents. As the agentic economy grows – with AI agents increasingly operating autonomously across DeFi, executing trades, managing positions, and participating in governance – the need for behavioral trust assessment of agents becomes as important as the need for behavioral trust assessment of human wallets. The agent is also required for ERC-8004 registry participants seeking to validate the trustworthiness of other registered agents before delegating tasks or sharing resources with them. For context on the growing agentic economy and its fraud implications, see our Web3 Agentic Economy guide .

Growth Tech Agents – 15 Agents, Complete Reference

ChainAware’s Growth Tech agents convert the same behavioral intelligence that prevents fraud into measurable protocol growth – higher conversion rates, better user retention, smarter acquisition spend, and more relevant product recommendations. The foundational insight driving this category is that 84% of wallets connecting to a typical DeFi protocol after a marketing campaign are ghost wallets – addresses with zero real engagement that farming bots and airdrop hunters control. Traditional Web3 growth tools cannot distinguish these ghost wallets from genuine users because they lack behavioral intelligence. Growth Tech agents solve this by treating each wallet’s on-chain history as a behavioral fingerprint that reveals its intentions, experience, risk appetite, and likely lifetime value – before the protocol spends a single dollar acquiring or engaging it.

Together, these 15 agents cover the complete user lifecycle: identifying high-value targets before acquisition (lead-scorer, ltv-estimator), personalizing the first moment of engagement (platform-greeter, onboarding-router), recommending the right products (defi-advisor, wallet-marketer), retaining users through their journey (upsell-advisor), and understanding the full user base through segmentation (cohort-analyzer, whale-detector). Furthermore, every Growth Tech agent runs a fraud gate internally – a wallet that fails the fraud check receives no marketing message, no personalized greeting, and no upsell recommendation. For the foundational framework on why behavioral intelligence outperforms demographic or web analytics approaches for Web3 growth, see our Web3 User Segmentation guide .

18. chainaware-wallet-auditor

The flagship intelligence agent delivers the complete 22-dimension Web3 Persona for any wallet address in under one second. A single predictive_behaviour call returns the full behavioral profile: fraud probability (98% accuracy), all 12 intention probabilities (Borrow, Lend, Trade, Gamble, NFT, Stake ETH, Stake Yield Farm, Leveraged Staking, Leveraged Staking ETH, Leveraged Lending, Leveraged Long ETH, Leveraged Long Game), experience score (0-10), risk capability (0-9), AML forensic flags, Wallet Rank, behavioral categories, protocol usage history, and ChainAware’s direct personalization recommendations. This is the broadest intelligence output in the suite – used when a protocol needs everything about a wallet rather than a specific signal. Coverage: ETH, BNB, BASE, HAQQ, SOLANA.

Use Case: A DeFi protocol’s product team wants to understand who is actually connecting to their platform before redesigning the UI. Using chainaware-wallet-auditor on a sample of 500 recent connecting wallets reveals that 62% have High Lend intention, 18% have High Trade intention, 11% are experienced DeFi power users with 8+ experience scores, and 9% are ghost wallets with zero meaningful history. This behavioral distribution tells the product team that their core user is a yield-seeking lender, not the active trader they assumed. The UI redesign prioritizes lending product visibility – a decision driven by behavioral data rather than assumption.

When Is It Required: Use chainaware-wallet-auditor when the use case requires the complete behavioral picture rather than a single signal – individual due diligence on high-value wallets, building a comprehensive user understanding before product decisions, and providing the full context that orchestrating agents like chainaware-marketing-director need to compose complete reports. The free Wallet Auditor at chainaware.ai/audit runs this agent for any address with no signup required – start there to understand the full output before integrating via API. See our Wallet Auditor guide for the complete usage guide.

19. chainaware-reputation-scorer

Calculates the deterministic ChainAware reputation score (0-1000) using the standard formula: (1000/110) × (experience + 1) × (risk_capability + 1) × (1 − fraud_probability). A score of 1,000 represents the theoretical maximum – experience 10, risk capability 9, fraud probability 0.00. In practice, scores above 750 represent Elite wallets: expert DeFi users with aggressive risk profiles and clean fraud histories. Scores below 125 indicate either ghost wallets with no history or high-fraud-probability addresses. The score is deterministic – given the same MCP inputs, the formula always produces the same output, making it auditable and reproducible for governance and allocation purposes. Coverage: ETH, BNB, BASE, HAQQ, SOLANA.

Use Case: A DAO wants to create a community leaderboard that ranks members by contribution quality rather than token holdings. Using chainaware-reputation-scorer on all community wallets produces a ranked list where active DeFi power users with long track records rise to the top, while passive token holders with minimal protocol engagement remain at the bottom. The leaderboard displays publicly on the DAO’s governance portal, creating a visible quality signal that incentivizes genuine participation over passive holding. Top-ranked wallets receive additional governance weight, early access to new protocol features, and community recognition – none of which require manual review to assign.

When Is It Required: Use chainaware-reputation-scorer when a standardized, comparable quality metric is needed across a large set of wallets – governance leaderboards, airdrop tier allocation (used internally by chainaware-airdrop-screener), lending collateral ratios, and marketing campaign quality gates all benefit from the single-number reputation score. It differs from chainaware-wallet-ranker (which ranks by total points and transaction count) in that the reputation formula explicitly penalizes fraud probability – a wallet with high activity but elevated fraud risk scores lower than a wallet with moderate activity and a clean history.

20. chainaware-wallet-ranker

Returns global wallet rank from experience score, total points, wallet age, and transaction count across the 20M+ wallet network. The rank provides a comparable quality metric across wallets from different blockchains through the unified behavioral scoring model – a wallet’s experience score on ETH is directly comparable to one on SOLANA. Batch mode produces a ranked leaderboard sorted by total points descending, identifying the highest-quality wallets in any submitted list. Unlike reputation-scorer (which uses a specific formula), wallet-ranker reflects ChainAware’s internal composite scoring of each wallet’s overall on-chain quality without the explicit fraud penalty component.

Use Case: A DeFi protocol wants to identify its top 50 users for a VIP program offering fee discounts and early feature access. Running chainaware-wallet-ranker on all 12,000 addresses that have ever interacted with the protocol produces a ranked leaderboard. The top 50 wallets by total points become VIP members. Because wallet rank reflects genuine on-chain quality rather than just protocol-specific activity, the VIP list includes wallets that are highly engaged across DeFi broadly – users most likely to promote the protocol within their wider DeFi networks and generate the most valuable word-of-mouth acquisition.

When Is It Required: Deploy chainaware-wallet-ranker for community leaderboards, VIP tier identification, governance weight calculation, and token holder quality assessment. It pairs naturally with chainaware-whale-detector – whale-detector identifies high-value wallets by behavioral depth, while wallet-ranker produces the specific numerical rank for ordering and comparison purposes. For the complete framework on wallet quality signals, see our Wallet Rank guide .

21. chainaware-whale-detector

Classifies wallets into four whale tiers – Mega Whale (experience ≥ 9, total points ≥ 5,000, active categories ≥ 3), Whale (experience ≥ 7.5 and total points ≥ 2,000, or experience ≥ 7 with high protocol diversity), Emerging Whale (experience ≥ 5 and total points ≥ 500, or experience ≥ 6 with high stake and trade intent), and Not a Whale. Each tier also receives an Active or Dormant classification based on forward-looking intent signals: Active whales have at least one High intent probability; Dormant whales have high experience but all-Low intent – they were once significant participants but are not currently engaged. Domain classification further identifies the wallet’s primary area: Trading Whale, DeFi Whale, NFT Whale, Multi-Chain Whale, Yield Whale, or Multi-Dimensional Whale. Fraud gate excludes wallets above 0.30 fraud probability from any whale classification.

Use Case: A DeFi protocol is launching a new advanced yield product designed for sophisticated users. The marketing team needs to identify which existing wallets in their user base qualify as genuine whales – and specifically which whales are currently active vs. dormant. Running chainaware-whale-detector on all 8,000 wallets that have interacted with the protocol in the last 90 days identifies 23 Mega Whales, 87 Whales, and 214 Emerging Whales. Within those groups, 68% are Active and 32% are Dormant. Active Mega Whales receive direct personal outreach for the new product launch. Dormant Whales receive a re-engagement campaign. Emerging Whales receive nurture content designed to accelerate their progression to the next tier.

When Is It Required: Run chainaware-whale-detector before any VIP program launch, before direct outreach campaigns targeting high-value users, before governance voting weight design (where whales warrant different treatment than retail participants), and as a regular audit of any protocol’s most valuable users to identify when whales go dormant and need re-engagement before they migrate to a competitor. The domain classification adds a targeting layer – a protocol launching an NFT-adjacent feature should specifically target NFT Whales, while a new yield vault should target Yield Whales and DeFi Whales.

22. chainaware-ltv-estimator

Estimates 12-month revenue potential for any wallet as a USD range using a seven-step model. Step one derives the annual transaction rate from experience level (Beginner → 5 tx/year, Expert → 700 tx/year). Step two applies an intent multiplier from forward-looking signals (3+ High intents → 1.25×, all Low → 0.65×). Step three calculates average transaction value from wallet balance × platform share (configurable, defaults to 15%). Step four applies the fee rate (configurable, defaults to 0.1%). Step five applies a category multiplier from activity breadth (1 category → 1.0×, 5+ categories → 1.75× cap). Step six applies a risk multiplier from risk profile (Conservative → 0.70×, Aggressive → 1.40×). Step seven applies a retention factor from fraud probability (0.00-0.09 → 0.95, 0.51-0.70 → 0.20). The final estimate applies ±25% to produce a range. Hard reject conditions return $0 with no range for confirmed fraud, fraud above 0.70, or any AML forensic flag.

Use Case: A DeFi protocol’s growth team plans a user acquisition campaign with a $200,000 budget. Before spending, they run chainaware-ltv-estimator on 10,000 target wallet addresses from a purchased marketing list. Results reveal that 6,200 wallets have estimated 12-month LTV below $10 (Dormant tier), 2,800 wallets have LTV in the $10-$100 range (Low tier), 800 wallets have LTV in the $100-$1,000 range (Medium tier), and 200 wallets have LTV above $1,000 (High tier). Rather than spending the $200,000 uniformly across all 10,000 addresses, the team concentrates 80% of the budget on the 1,000 Medium and High LTV wallets. Expected ROI improves dramatically compared to uniform distribution.

When Is It Required: Use chainaware-ltv-estimator before any acquisition campaign to prioritize high-value targets, before VIP tier assignment to identify which wallets generate the most protocol revenue, and before marketing budget allocation decisions where targeting the right wallets determines whether the campaign generates positive ROI. It works alongside chainaware-lead-scorer – lead-scorer measures conversion probability, while ltv-estimator measures revenue magnitude. Combining both gives a complete acquisition prioritization signal: high-lead-score × high-LTV wallets deserve the most aggressive outreach investment.

23. chainaware-lead-scorer

Sales lead qualification engine returning a lead score (0-100), tier (Hot/Warm/Cold/Dead), conversion probability, and recommended outreach angle for any wallet. The scoring model weights five components: experience (35%), intent strength (25%), activity breadth (20%), risk appetite (10%), and fraud penalty (up to −10). Product context doubles the weight of the matching intent signal – a staking product doubles Prob_Stake, a cross-chain bridge doubles Prob_Bridge – making the score product-specific rather than generic. Hot leads (75-100) warrant immediate personalized outreach. Dead leads (0 or fraud-disqualified) are excluded from all campaigns entirely, preventing budget waste on wallets that would never convert.

Use Case: A DeFi yield aggregator launching on BASE wants to identify which ETH-based DeFi users are most likely to bridge and adopt the new platform. The growth team runs chainaware-lead-scorer on 25,000 ETH wallet addresses that have interacted with competing yield products, with product context set to “cross-chain yield aggregator on BASE.” The scoring returns 340 Hot leads (score 75+, high Prob_Bridge and Prob_Stake intent), 2,800 Warm leads (score 50-74), 15,000 Cold leads (score 25-49), and 6,860 Dead leads (below 25 or fraud-disqualified). The team focuses personalized outreach on the 340 Hot leads and runs automated campaigns for the 2,800 Warm leads. Acquisition cost per converted user drops significantly compared to the previous campaign that treated all 25,000 addresses identically.

When Is It Required: Run chainaware-lead-scorer before any acquisition outreach campaign, before direct sales team prioritization, and before budget allocation across different wallet segments. It is specifically required when a protocol launches a new product or feature and wants to identify existing wallet holders most likely to adopt it based on behavioral signals – rather than guessing based on past protocol interactions alone. See our Behavioral Analytics guide for the complete acquisition framework.

24. chainaware-wallet-marketer

Generates a hyper-personalized marketing message of maximum 20 words for any wallet, derived directly from its on-chain behavioral signals – no generic crypto copy, no templated language. Signal priority determines the message angle: Prob_Stake High leads with staking yield opportunity; Prob_Trade High leads with trading execution quality; Prob_Bridge High leads with cross-chain capability; Prob_NFT_Buy High leads with NFT feature; DeFi Lender category leads with lending/yield rates; experience above 7.5 leads with advanced power user features; experience below 2.5 leads with simple beginner-friendly onboarding. The message mirrors what the wallet actually does on-chain, making it feel personal rather than promotional. Fraud gate blocks message generation entirely for high-fraud-probability wallets.

Use Case: A DEX wants to run a re-engagement campaign targeting 5,000 wallets that connected once but never executed a trade. Running chainaware-wallet-marketer in batch mode on all 5,000 addresses produces 5,000 distinct messages – each derived from that specific wallet’s behavioral signals. A wallet with High Prob_Stake and DeFi Lender category receives: “Your lending habits earn yield. Our single-click vault automates it. Start here.” A wallet with High Prob_Trade and Active Trader category receives: “You trade fast. Our zero-slippage routing finds better fills. Try one swap.” A beginner wallet with experience below 2 receives: “New to DeFi? Earn your first yield in under two minutes. Start here.” The personalized messages achieve 3-4× higher click-through rates than the generic campaign the DEX ran previously.

When Is It Required: Use chainaware-wallet-marketer for any outbound campaign where personalization improves conversion – which is essentially every outbound campaign. It is specifically required when a protocol has a segmented user base with significantly different behavioral profiles, when re-engaging dormant users where a generic message will be ignored, and when the campaign budget is large enough that even a 2× improvement in conversion rate generates meaningful additional revenue. For the complete personalization framework, see our Why Personalization Matters guide .

25. chainaware-platform-greeter

Contextual welcome message engine generating platform-specific in-app messages of maximum 35 words at wallet connection. The same wallet receives a completely different message on Aave than on 1inch or OpenSea – because what matters to a DeFi lender visiting a lending platform differs fundamentally from what matters when that same wallet visits a DEX or an NFT marketplace. Platform type detection maps the wallet’s dominant behavioral signals to the most relevant platform angle. Returning users with protocol history receive “welcome back” framing with specific references to their history. First-time visitors with strong intent alignment receive “you know X, here’s what we do for X” framing. Low-experience first-timers receive simplified educational framing. Tone is configurable across friendly, professional, and bold to match brand voice.

Use Case: A lending protocol integrates chainaware-platform-greeter into its wallet connection event. When a DeFi Lender wallet with experience 8 and existing Aave positions connects, it sees: “Your lending positions are working – ETH supply rate is up 0.4% since your last visit. Check your health factor before rates move.” When a High Prob_Trade wallet connects for the first time, it sees: “You trade – here you can also earn on idle assets between swaps.” When a low-experience wallet connects for the first time, it sees: “New here? Deposit any token and earn interest automatically. No minimums.” Three different wallets, three different messages, all generated automatically at connection with zero manual configuration per user segment.

When Is It Required: Deploy chainaware-platform-greeter for any DeFi platform with diverse user types – a protocol serving both experienced DeFi power users and first-time users needs different first-moment experiences for each segment. It is specifically required when conversion analytics show a significant percentage of connecting wallets leaving without taking any action – a sign that the current generic landing experience does not resonate with the behavioral diversity of the connecting wallet population. The agent adds under 200ms to the wallet connection flow, negligible for user experience purposes.

26. chainaware-onboarding-router

Routes each connecting wallet to the correct onboarding experience based on verifiable on-chain experience rather than self-reported surveys or assumed user segments. Experience 0-2.5 → Beginner Tutorial (full guided walkthrough – this wallet needs hand-holding through every step). Experience 2.6-6 → Intermediate Guide (condensed tips that skip the absolute basics while still orienting the user to platform-specific features). Experience 6.1-10 → Skip Onboarding (power user, straight to the product – tutorials waste their time and signal that the platform doesn’t understand them). Secondary signals refine the route: a wallet with experience 5.5 that already uses the platform’s specific protocol category can skip most tutorials even though its overall score is technically Intermediate. New Address always routes to Beginner regardless of other signals.

Use Case: A DeFi platform’s user research team discovers that 23% of users who complete the full onboarding tutorial are experienced DeFi power users who were frustrated by being forced through beginner content. These users have 3× higher churn rates in the first week compared to users correctly identified as power users who skipped onboarding. Integrating chainaware-onboarding-router eliminates the mis-routing: power users (experience 6.1+) go directly to the product, intermediate users see a condensed orientation, and genuine beginners receive the full tutorial. First-week churn drops 31% as power users stop abandoning the platform out of frustration with irrelevant onboarding content.

When Is It Required: Deploy chainaware-onboarding-router for any platform with a multi-step onboarding flow and a diverse user base that includes both experienced DeFi users and newcomers. It is specifically required when product analytics show high drop-off during onboarding – a symptom that the current fixed onboarding experience is poorly matched to the actual experience distribution of the connecting wallet population. The agent works best in combination with chainaware-platform-greeter (which personalizes the first moment before onboarding begins) and chainaware-defi-advisor (which provides product recommendations post-onboarding). For the complete onboarding conversion analysis, see our DeFi Onboarding guide .

27. chainaware-defi-advisor

Personalized DeFi product recommendation engine with three product tiers calibrated to wallet experience and risk appetite. Tier 1 Safe Harbor covers Beginner and Conservative wallets: simple staking, stablecoin lending, savings vaults, fixed-rate lending. Tier 2 Yield Builder covers Intermediate and Moderate wallets: liquid staking, blue-chip LP pools, variable rate lending, multi-asset vaults. Tier 3 Yield Maximizer covers Experienced and Aggressive wallets: leveraged yield farming, options vaults (DOVs), concentrated liquidity CLMM active management, cross-chain yield arbitrage, and veToken strategy stacking. Intent signals boost recommendations within the tier: Prob_Stake High prioritizes staking products first; Prob_Trade High prioritizes LP pools and active liquidity. Protocol history adds a further targeting layer: a wallet that already uses Aave receives Aave-compatible product recommendations over generic alternatives.

Use Case: A DeFi aggregator platform connects 500 different wallets per day across its product suite. Without personalization, every wallet sees the same “Featured Products” section – typically the highest-APY products, which are also the highest-risk. Conservative beginners see leveraged products they don’t understand, and aggressive experts see beginner staking options that bore them. Integrating chainaware-defi-advisor personalizes the product menu for each connecting wallet: beginners see stablecoin lending and simple staking; power users see advanced leveraged strategies and CLMM management tools. First-session product interaction rates increase 2.4× across all experience tiers because every user sees products calibrated to their level.

When Is It Required: Use chainaware-defi-advisor for any multi-product DeFi platform where the right product for one user is actively wrong for another. It is specifically required when conversion analytics show significant variance in product adoption rates by user experience level – a sign that current product placement is suboptimal for at least one segment. For platforms launching new products, the agent identifies which existing wallet segments are most aligned with the new product’s requirements before the launch, enabling targeted pre-launch outreach to the highest-probability adopters. See our DeFi Platform Use Cases guide .

28. chainaware-upsell-advisor

Identifies the optimal next product to offer an existing user and the precise moment to offer it. Upgrade readiness score (0-100) combines experience headroom toward the next product tier (40% weight), intent alignment with the target product (35%), and risk appetite fit (25%). Score 80-100 → offer now, conversion probability above 65%. Score 60-79 → offer at the next behavioral trigger event. Score 40-59 → nurture first, offer after 1-2 more sessions. Score below 40 → do not upsell yet – the risk is churn rather than conversion. Trigger events are behavioral rather than time-based: a wallet ready for a staking upgrade gets the offer the next time it stakes or claims rewards, not on a fixed weekly cadence. A “What NOT to do” recommendation identifies the single upsell approach most likely to cause churn for each specific wallet – for example, “Don’t pitch leveraged products – this is a Conservative wallet and the complexity will cause churn.”

Use Case: A DeFi lending platform has 3,000 active users on its basic lending tier. The product team wants to introduce an advanced leveraged yield farming product and identify which users are ready to upgrade now vs. which need nurturing first. Running chainaware-upsell-advisor on all 3,000 users with the new product as context identifies 180 users with readiness above 80 (offer now), 620 users at 60-79 (offer at next trigger), 1,400 users at 40-59 (nurture first), and 800 users below 40 (do not upsell). The 180 “offer now” users receive immediate personalized outreach with specific trigger messaging aligned to their dominant intent signal. Within four weeks, 67% of the “offer now” group has upgraded – without wasting outreach budget on the 800 users who were not ready and would have churned if pushed.

When Is It Required: Deploy chainaware-upsell-advisor whenever a protocol launches a new product tier and wants to maximize adoption among existing users. It is specifically required for protocols with a tiered product structure where pushing the wrong product too early causes churn, for platforms with subscription-based models where upgrade timing significantly affects revenue, and for any DeFi protocol where the most valuable users are those engaging with multiple product tiers simultaneously. The trigger event recommendation is especially valuable – it replaces time-based upsell campaigns (which push users at arbitrary moments) with behavior-triggered campaigns (which engage users at the exact moment their intent signals indicate readiness).

29. chainaware-cohort-analyzer

Batch behavioral cohort segmentation for Web3 analytics teams. Classifies every wallet in a submitted list into one of eight behavioral cohorts: Power DeFi User (experience ≥ 7, DeFi Lender or Active Trader dominant, protocols ≥ 5), NFT Collector (NFT Collector dominant, experience ≥ 3), Yield Farmer (Yield Farmer dominant or Prob_Stake High with experience ≥ 5), Multi-Chain Explorer (Bridge User dominant or bridge-heavy protocol history), Active Trader (Prob_Trade High with experience ≥ 4), Casual User (experience 2-4.9, no dominant pattern), Dormant/Inactive (experience ≥ 2 but all intent signals Low), and New/Fresh Wallet (new address with clean fraud signals). Fraud exclusions – bots, confirmed fraud, AML flags, suspicious new wallets – are separated from behavioral cohorts entirely. Each cohort receives a specific engagement strategy recommendation, and the full report includes audience quality score, per-cohort statistics, and a three-priority action plan.

Use Case: A DeFi protocol planning its Q3 marketing budget wants to allocate spend across different user segments rather than running one generic campaign. Chainaware-cohort-analyzer on their 15,000-wallet user base reveals: 890 Power DeFi Users (6%), 1,200 NFT Collectors (8%), 2,100 Yield Farmers (14%), 800 Multi-Chain Explorers (5%), 3,400 Casual Users (23%), 2,800 Dormant wallets (19%), 1,600 New wallets (11%), and 2,210 excluded bots and fraud (15%). The budget allocation becomes data-driven: 35% to Yield Farmer acquisition for the new vault product, 25% to Casual User conversion, 20% to Dormant re-engagement, and 20% to New wallet onboarding. Each cohort receives a distinct message strategy rather than a generic campaign blasted to all 15,000 addresses.

When Is It Required: Run chainaware-cohort-analyzer before any marketing budget planning cycle, before product launch targeting decisions, and as a quarterly audit of user base composition to detect shifts in behavioral distribution. It is specifically required before an airdrop (to ensure token distribution aligns with cohort quality rather than farming behavior), before a governance token launch (to understand which community members qualify for each allocation tier), and before any significant UI redesign (to ensure the redesign serves the actual behavioral distribution rather than an assumed user persona).

30. chainaware-token-ranker

Discovers and ranks tokens by the behavioral quality of their holder community across five categories – AI Token, RWA Token, DeFi Token, DeFAI Token, DePIN Token – on ETH, BNB, BASE, and SOLANA. Community rank scores the aggregate behavioral strength of all token holders: wallet age, transaction history, protocol diversity, and experience scores across the 20M+ wallet network. A token whose holders are predominantly experienced, long-tenured, multi-protocol DeFi users ranks higher than a token with the same market cap but predominantly fresh wallets with minimal history. This ranking reflects genuine community quality – not just trading volume or price momentum, which can be manufactured. Supports sort by community rank, normalized rank, or holder count; category filtering; pagination; and name-based token search.

Use Case: An institutional DeFi fund wants to allocate capital to the top three AI tokens by community quality rather than market cap. Running chainaware-token-ranker for AI Token category on ETH and BNB returns a ranked list showing which AI tokens have the strongest holder bases of experienced, legitimate DeFi participants – and which have significant proportions of fresh wallets and farming addresses in their holder distribution. The fund identifies two tokens where community quality is significantly stronger than their market cap rank suggests – potential value opportunities where genuine community strength has not yet been reflected in price. Both tokens are added to the portfolio after individual deep-dives using chainaware-token-analyzer.

When Is It Required: Use chainaware-token-ranker for token portfolio research and selection when community quality is a meaningful signal, for DEX teams curating featured token listings based on genuine community strength rather than trading volume alone, and for any platform wanting to surface high-quality tokens to users before market price discovery catches up to community quality. It works as the first step in a two-step research process: token-ranker identifies the best candidates from a category, then chainaware-token-analyzer deep-dives each candidate’s specific holder composition. See our Token Rank guide for detailed methodology.

31. chainaware-token-analyzer

Deep-dives into a single token’s community rank and top holder profiles – returning each top holder’s wallet age, total transaction count, total points, and global rank across the 20M+ wallet network. Optional fraud screening on the top holders via predictive_fraud identifies whether the token’s largest positions are held by legitimate experienced wallets or by coordinated fraud networks disguising their concentration. The holder quality assessment computes average wallet age, average transaction count, and average global rank across the top holders, producing a Verdict (2-3 sentences on whether these are genuine power users or manufactured holders). Token-ranker identifies which tokens have the strongest community quality in aggregate; token-analyzer validates whether specific tokens actually back that aggregate signal with genuine individual holders.

Use Case: A crypto exchange is evaluating whether to list a new DeFi token. The community rank from chainaware-token-ranker shows the token in the top 20% of its category – strong enough to consider. Chainaware-token-analyzer deep-dives the top 20 holders: 14 have average wallet age above 800 days, high transaction counts, and global ranks in the top 10% of the 20M+ wallet network. However, three of the top 20 holders share a funding source and show coordinated acquisition patterns – signals of artificial holder concentration. The fraud screening confirms two of those three have elevated fraud probability. The exchange requires the team to reduce concentration before listing. Six weeks later, the concentration issue is resolved, and the token lists and performs well due to its genuinely strong community foundation.

When Is It Required: Run chainaware-token-analyzer before listing any token on an exchange or DEX with listing standards, before making significant portfolio allocation to a token where holder quality affects the investment thesis, and before any governance vote giving token holders significant power – understanding whether those holders are genuine community members or coordinated operators directly affects the legitimacy of governance outcomes. It is also required as part of due diligence for institutional crypto fund investments where holder composition is a material factor in the investment case.

32. chainaware-marketing-director

The orchestrator agent – a senior marketing strategist that delegates to seven specialist agents and synthesizes their outputs into a complete Marketing Campaign Brief. In batch mode (multiple wallets), the agent runs six sequential phases: segmentation via chainaware-cohort-analyzer, lead scoring and whale detection on the highest-potential wallets, per-cohort message generation via chainaware-wallet-marketer, upsell opportunity identification via chainaware-upsell-advisor, onboarding routing for new wallets, and executive campaign brief synthesis. In single-wallet mode, it runs five specialist agents simultaneously and returns a complete Wallet Marketing Profile including fraud risk, whale tier, lead score, personalized outreach message, platform welcome message, upsell path, and recommended onboarding flow. The Marketing Director represents the highest-level abstraction in ChainAware’s agent architecture – demonstrating what coordinated multi-agent intelligence delivers that no single specialist agent can replicate independently. It requires a platform description as input, using that context to make every generated message feel native to the specific protocol.

Use Case: A DeFi lending protocol is planning a growth push targeting 200 existing wallets that have connected but never borrowed. The growth lead does not have time to run each specialist agent separately and synthesize results manually. Running chainaware-marketing-director with the 200 wallet addresses and the platform description as input produces a complete Campaign Brief in one pass: 23 Hot leads requiring immediate personal outreach; 8 Mega and Whale wallets identified for VIP treatment; per-cohort message templates for the 6 behavioral cohorts represented in the wallet list; 31 wallets with upgrade readiness above 80 ready for a borrowing product offer; 18 new wallets routed to beginner onboarding; and 14 excluded as fraud or bots. The entire brief – segmentation, prioritization, messages, execution sequence – is ready for the growth team to execute.

When Is It Required: Use chainaware-marketing-director when a campaign needs the output of multiple specialist agents and the team does not have the resources to run them separately and synthesize results. It is specifically the right choice for time-sensitive campaigns where speed matters, for small growth teams needing a complete brief rather than raw intelligence, and for any campaign spanning multiple wallet segments requiring different strategies simultaneously. The agent is also the best entry point for teams new to ChainAware’s agent suite – a single Marketing Director run demonstrates the full capability range of the underlying specialist agents in one unified output. For the complete campaign planning framework, see our Web3 Marketing guide .

How Agents Compose Into Pipelines

The most powerful applications of ChainAware’s 32 agents emerge not from individual deployment but from composing them into pipelines – where the output of one agent becomes the input of the next. Every agent’s documentation includes a composability section mapping its natural connections to adjacent agents. Three core pipelines demonstrate the composability principle and cover the most common production deployments.

The Compliance Pipeline

The compliance pipeline sequences four agents: trust-scorer → aml-scorer → compliance-screener → transaction-monitor. Trust-scorer provides the fast first gate at under 50ms – any wallet below 0.30 trust score is immediately routed to enhanced review. AML-scorer adds forensic verification for wallets that pass the trust gate, checking all 19 forensic flag categories and producing the documented AML score needed for regulatory reporting. Compliance-screener orchestrates both signals plus transaction pattern analysis into the final PASS / EDD / REJECT verdict with full documented evidence trail. Transaction-monitor handles ongoing screening post-onboarding, flagging any transaction that exceeds risk thresholds after a wallet has been onboarded and approved.

Together, the four agents cover the complete compliance lifecycle from pre-onboarding screening through ongoing monitoring – the full stack required for MiCA-compliant operation. According to FATF’s Virtual Assets Recommendations , this kind of continuous monitoring is increasingly required rather than optional for regulated crypto asset service providers. Furthermore, the documented output from each agent in the pipeline creates the audit trail that regulators require – not just a screening decision, but the specific signals and thresholds applied to produce it.

The Growth Pipeline

The growth pipeline sequences six agents: cohort-analyzer → lead-scorer → whale-detector → wallet-marketer → onboarding-router → upsell-advisor. Cohort-analyzer segments the full wallet list and identifies fraud exclusions, producing the audience map for the campaign. Lead-scorer then ranks the highest-conversion targets within the highest-value cohorts. Whale-detector surfaces the VIP wallets within those cohorts for personal outreach. Wallet-marketer generates per-wallet personalized messages for the identified hot leads and whale wallets. Onboarding-router assigns new wallets in the cohort analysis to the correct first-time experience. Upsell-advisor identifies existing users ready for product upgrades, completing the full lifecycle from acquisition through retention.

Notably, chainaware-marketing-director runs this exact pipeline automatically – making it the recommended entry point for teams deploying the growth pipeline for the first time. The Marketing Director adds the synthesis layer that converts six separate agent outputs into a single actionable Campaign Brief, eliminating the manual work of combining results across multiple specialist runs.

The Token Intelligence Pipeline

The token intelligence pipeline sequences three agents: token-ranker → token-analyzer → rug-pull-detector. Token-ranker identifies the strongest tokens in a target category by community quality across ETH, BNB, BASE, or SOLANA – producing a shortlist of high-potential candidates. Token-analyzer then deep-dives each shortlisted token’s specific holder composition, validating whether the aggregate community quality score reflects genuine individual holders or manufactured concentration. Rug-pull-detector screens the contract address and deployer wallet for the tokens that pass both previous stages – confirming that the project behind the strong community is not itself a fraud risk.

The three agents together provide the complete due diligence stack for token investment decisions, exchange listing evaluation, and governance token selection. Moreover, they address the three distinct questions that token evaluation requires: which tokens have the strongest communities (token-ranker), are those communities genuinely strong or manufactured (token-analyzer), and is the contract itself safe (rug-pull-detector). Each question requires a different tool, and combining all three produces a confidence level in a token that no single tool delivers alone. For the complete framework on how behavioral intelligence applies to token research, see our Token Rank guide .

The Agentic Economy Pipeline

The agentic economy pipeline sequences two Fraud Tech agents with the transaction-monitor: agent-screener → counterparty-screener → transaction-monitor. As AI agents increasingly operate autonomously across DeFi – executing trades, managing positions, and participating in governance on behalf of humans – the need for agent-specific trust assessment becomes as important as wallet trust assessment. Agent-screener validates the trust score of any third-party AI agent before it is granted access to a protocol or given permission to interact with user funds. Counterparty-screener validates each specific address the agent will interact with before execution. Transaction-monitor provides continuous real-time risk scoring for every transaction the agent executes once granted access.

This pipeline addresses the structural vulnerability in the current ERC-8004 ecosystem – 196,000+ registered agents with no behavioral trust signals. ChainAware’s agentic economy pipeline provides the trust infrastructure that the registry itself lacks, making it the foundational security layer for any protocol accepting autonomous AI agent interactions. For the complete analysis of how AI agents are reshaping Web3 operations, see our Web3 Agentic Economy guide .

Getting Started – Integration in Three Steps

All 32 agents are available as open-source Claude Code agent definitions at github.com/ChainAware/behavioral-prediction-mcp . Integration requires three steps and no blockchain expertise. According to Anthropic’s Model Context Protocol documentation , MCP is rapidly becoming the standard integration layer for AI agent tool access – making ChainAware’s MCP-native delivery compatible with any LLM infrastructure that supports the standard.

Step one – register the Prediction MCP server in your Claude Code environment:

claude mcp add --transport sse chainaware-behavioral-prediction \
  https://prediction.mcp.chainaware.ai/sse \
  --header "X-API-Key: YOUR_KEY"

Step two – clone the repository and copy all 32 agent definitions into your project:

git clone https://github.com/ChainAware/behavioral-prediction-mcp.git
cp -r behavioral-prediction-mcp/.claude/agents/ your-project/.claude/agents/

Step three – invoke any agent directly from Claude Code:

claude --agent chainaware-fraud-detector
# or trigger from within Claude Code:
@chainaware-wallet-auditor

API keys are available at chainaware.ai/pricing . The free Wallet Auditor at chainaware.ai/audit demonstrates the full behavioral intelligence output with no API key or signup required – start there to understand the complete output before building your integration. Additionally, the free Fraud Detector at chainaware.ai/fraud-detector and Rug Pull Detector at chainaware.ai/rug-pull-detector demonstrate the Fraud Tech agent outputs with no setup. For the complete developer integration guide covering Claude Desktop, Cursor, and custom MCP client setups, see our Prediction MCP guide .

Frequently Asked Questions

What is a ChainAware sub-agent?

A ChainAware sub-agent is a pre-built Claude Code agent definition – a markdown file containing a name, description, role definition, decision logic, output format specification, and MCP tool references. When placed in a Claude Code project’s .claude/agents/ directory, the agent becomes invocable by name from any Claude Code session in that project. The agent calls ChainAware’s Prediction MCP tools (predictive_fraud, predictive_behaviour, predictive_rug_pull, credit_score, token_rank_list, token_rank_single) with the appropriate parameters, interprets the response according to its decision logic, and returns a structured output in the format defined in the agent file. All 32 agents are open-source under the MIT license at github.com/ChainAware/behavioral-prediction-mcp .

How do the 32 agents relate to the ChainAware Prediction MCP?

The Prediction MCP is the intelligence layer – the SSE endpoint at prediction.mcp.chainaware.ai/sse that exposes ChainAware’s six prediction tools as MCP-callable functions. The 32 agents are the application layer – pre-built Claude Code agents that call those tools with the right parameters, apply decision logic to the results, and return structured outputs ready for human or automated action. Any developer can call the raw MCP tools directly via the REST API for custom integrations. The agents provide a head start – 32 production-ready agent definitions covering the most common use cases, tested and maintained by ChainAware’s team. For the complete MCP integration guide, see our Prediction MCP guide .

Which agent should I start with?

Start with chainaware-wallet-auditor for the broadest view of what ChainAware’s intelligence produces – it returns the complete 22-dimension Web3 Persona in one call, showing every signal that the specialist agents use individually. The free Wallet Auditor at chainaware.ai/audit runs this agent for any wallet address with no setup required. Once you understand the full output, select the specialist agent matching your primary use case: fraud prevention teams start with chainaware-fraud-detector; launchpad teams start with chainaware-rug-pull-detector; compliance teams start with chainaware-aml-scorer; growth teams start with chainaware-cohort-analyzer for their existing user base; teams evaluating AI agent trustworthiness start with chainaware-agent-screener.

Can I modify the agent definitions for my specific use case?

Yes – all 32 agent definition files are open-source under the MIT license. Fork the repository, modify any agent’s decision thresholds, output format, or tool selection, and deploy your customized version alongside the standard agents. Common customizations include adjusting fraud probability thresholds for specific risk tolerances, adding platform-specific context to message templates in chainaware-wallet-marketer and chainaware-platform-greeter, and modifying the governance tier classification thresholds in chainaware-governance-screener to match specific DAO requirements. The only component that is proprietary and cannot be modified is the underlying Prediction MCP server and its trained ML models – the intelligence that powers the tool calls. Agent definitions, decision logic, and output formats are all freely modifiable.

What is the difference between Fraud Tech and Growth Tech agents?

Fraud Tech agents answer whether a wallet, contract, or transaction can be trusted – they produce verdicts (block, flag, allow, reject, qualify). Growth Tech agents answer how to engage a wallet that has passed trust assessment – they produce recommendations (which product to surface, what message to send, which onboarding flow to show). Both categories draw from the same 20M+ wallet persona database and the same Prediction MCP tools. However, every Growth Tech agent runs a fraud gate before producing any recommendation – a wallet that fails the fraud check receives no marketing message, no personalized greeting, and no upsell recommendation. This means the categories are parallel layers rather than sequential stages: fraud protection runs continuously through every growth decision, ensuring that behavioral personalization never extends to wallets that ChainAware’s models identify as fraudulent operators.

How accurate are ChainAware’s fraud detection models?

ChainAware achieves 98% fraud detection accuracy on ETH and 96% on BNB, backtested against CryptoScamDB – the largest publicly available database of documented crypto fraud incidents. The rug pull detection model achieves 90.1% accuracy, backtested on the PancakeSwap V2 dataset covering $569M in documented rug pull losses from weeks 1-20 of 2026. These accuracy figures measure the model’s ability to correctly identify fraudulent wallets and contracts before they commit their recorded offense – not accuracy on post-incident classification. The distinction matters: ChainAware’s models are designed to predict fraud before it executes, which is structurally harder than forensic classification of known fraud incidents. For the complete accuracy methodology and comparison against forensic approaches, see our Forensic vs AI-Powered Analytics guide .

Are the agents available on all blockchains?

Coverage varies by agent and underlying MCP tool. The predictive_fraud tool – used by fraud-detector, aml-scorer, trust-scorer, and counterparty-screener – covers the broadest network set: ETH, BNB, POLYGON, TON, BASE, TRON, and HAQQ. The predictive_behaviour tool – used by wallet-auditor, reputation-scorer, whale-detector, and the growth agents – covers ETH, BNB, BASE, HAQQ, and SOLANA. The predictive_rug_pull tool covers ETH, BNB, BASE, and HAQQ. Agents supporting networks not covered by their primary tool include automatic fallback logic – for example, chainaware-airdrop-screener falls back to predictive_fraud for POLYGON, TON, and TRON wallets. The classification table in this article lists exact network coverage per agent for quick reference.

Why did ChainAware build on Claude specifically?

Claude’s tool use and structured output capabilities make it particularly well-suited for the deterministic decision logic that fraud detection and compliance agents require. An agent applying five disqualification rules in strict order – stopping at the first failure – needs a model that follows logical sequences reliably without hallucinating intermediate steps. Additionally, Claude Code’s native agent support (the .claude/agents/ directory standard) makes deployment frictionless for teams already using Claude Code. Agents requiring faster, cheaper inference (chainaware-trust-scorer, chainaware-wallet-ranker) use Claude Haiku 4.5. Agents requiring richer analytical reasoning (chainaware-wallet-auditor, chainaware-cohort-analyzer, chainaware-marketing-director) use Claude Sonnet 4.6. The model selection is specified in each agent’s frontmatter and can be changed by forking the agent definition file. ChainAware’s Prediction MCP tools are model-agnostic – GPT-4, Gemini, and any other MCP-compatible model can call them directly via the REST API.

How does ChainAware’s intelligence relate to the CB Insights market map?

ChainAware was named in CB Insights’ AI Fraud Prevention Market Map in June 2026 – placed in the On-Chain Intelligence subcategory alongside Chainalysis, Elliptic, TRM Labs, Crystal Intelligence, and Blockaid. CB Insights selected companies based on Mosaic health scores and equity funding recency, filtering out thousands of projects that did not meet the institutional bar. ChainAware’s position on the map validates the Fraud Tech agents (fraud-detector, aml-scorer, compliance-screener, rug-pull-detector, and transaction-monitor) specifically – these are the agents that deliver the on-chain intelligence capability CB Insights recognized. Beyond the map placement, ChainAware is the only company in the entire CB Insights list with a publicly traded token listed in CoinGecko’s AI category – a position that reflects the dual institutional and decentralized distribution model that the 32 agents are built to serve.

External Sources: ChainAware GitHub Repository · Anthropic Model Context Protocol · FATF Virtual Assets Recommendations · ERC-8004 Agent Registry · CB Insights AI Fraud Prevention Market Map

The post ChainAware.ai’s 32 Claude Sub-Agents – Fraud Tech and Growth Tech for the Agentic Economy first appeared on ChainAware.ai.

ChainAware.ai appears in the On-Chain Intelligence subcategory alongside Chainalysis, Elliptic, TRM Labs, Crystal Intelligence, and Blockaid. That placement represents meaningful institutional validation – CB Insights selects companies based on Mosaic health scores above 600 and equity funding recency since 2024, filtering out thousands of projects that do not meet the bar.

One additional data point makes ChainAware’s position unique across the entire 200-company map. ChainAware is the only Web3 AI token in the full list – and the only company in the On-Chain Intelligence category with a publicly traded token listed in CoinGecko’s AI category. Among 1,385 tokens in that category, ChainAware’s AWARE token is the single representative of on-chain intelligence and behavioral fraud detection.

This article explains what that combination means, why it matters for enterprise buyers, developers, and investors – and how ChainAware’s specific products produce outcomes that no other company on the map delivers.

What Is the CB Insights AI Fraud Prevention Market Map?

CB Insights is the institutional research and intelligence platform that tracks private company health scores, funding rounds, and competitive landscapes for 100,000+ technology companies. Its market maps represent the authoritative view of emerging technology categories – used by venture capital firms, corporate development teams, enterprise procurement departments, and regulatory bodies to identify leading vendors and benchmark competitive positioning.

The AI Fraud Prevention Market Map, published June 2, 2026, covers the companies building infrastructure to detect, prevent, and manage fraud in the AI era. That framing is deliberate and significant – it separates the legacy fraud prevention market (rules-based, human-reviewed, slow) from the emerging category of AI-native fraud prevention (predictive, automated, operating at agent speed).

Why This Map Exists Now

CB Insights publishes category maps when a market reaches sufficient maturity and investment volume to justify systematic mapping. The timing of the AI Fraud Prevention map reflects three converging forces that have made fraud prevention one of the most actively funded technology categories of 2026.

First, AI-generated fraud has scaled dramatically. Deepfake video scams, synthetic identity creation, and AI-powered phishing campaigns have collectively pushed AI-based fraud losses toward the $40 billion annual mark projected by industry analysts. Traditional fraud detection tools were built for human-speed fraud – they cannot detect AI-generated attacks operating at machine speed.

Second, the agentic economy has created entirely new fraud surfaces. AI agents transacting autonomously on behalf of humans do not carry passports, credit histories, or biometric signatures. Every identity and trust system built over the last 30 years assumes the actor is human. Agents need identity and trust infrastructure built specifically for how they operate – a gap that every major new crypto VC fund has identified as their primary investment thesis.

Third, stablecoin adoption has accelerated on-chain transaction volumes toward levels that require institutional-grade compliance infrastructure. According to CB Insights, stablecoin transaction volumes in 2025 grew to double-digit trillions – approaching Visa and Mastercard combined. That volume requires fraud detection, AML screening, and behavioral intelligence that scales with it.

CB Insights Map Structure

The map organizes 200+ companies into three primary sections, each with multiple subcategories:

• Agentic Trust Infrastructure – Agent observability and evaluation, Agent authentication and authorization (KYA), Agent runtime governance and oversight
• Digital Identity and Verifiable Credentials – Decentralized identity (DID), Passwordless authentication, Post-quantum identity, Know Your Customer (KYC), Biometric identity
• Fraud Detection and Prevention – Fraud orchestration and case management, Risk scoring and signals, AML compliance, AI-generated content detection, On-chain intelligence, Transaction monitoring, Bot detection, Graph analytics and network fraud, Account takeover (ATO) protection

ChainAware sits in the Fraud Detection and Prevention section, specifically in the On-Chain Intelligence subcategory – the most directly Web3-native category on the entire map.

The On-Chain Intelligence Category – Who Made the List

The On-Chain Intelligence subcategory contains eleven companies. Understanding each one – what they do, who they serve, and where they differentiate – establishes the competitive context in which ChainAware operates.

Chainalysis

Chainalysis is the dominant forensic intelligence platform for blockchain – built originally for law enforcement agencies including the FBI, DEA, and IRS. Its Know Your Transaction (KYT) product handles VASP compliance screening, and its investigation tools reconstruct transaction graphs across chains for evidence-grade fund flow analysis. Enterprise pricing ranges from $100,000 to $500,000 annually. Chainalysis is reactive by design: it traces where funds came from after transactions have occurred, which makes it essential for post-incident investigation but structurally unable to prevent fraud before execution. According to Chainalysis’s platform documentation , its clustering heuristics and entity attribution cover hundreds of major counterparties across multiple blockchains.

Elliptic

Elliptic serves a similar VASP compliance use case with a stronger European and institutional focus. Its blockchain analytics cover transaction monitoring, wallet screening, and sanctions compliance for exchanges, banks, and asset managers. Elliptic has expanded into DeFi protocol screening and NFT risk analysis – but remains fundamentally a forensic and compliance tool rather than a predictive intelligence platform.

TRM Labs

TRM Labs occupies the government and financial institution segment with the highest Mosaic score of any company in the On-Chain Intelligence category. Its platform serves FinCEN, OFAC, and major global banks – and has expanded into proactive threat intelligence that goes beyond pure reactive forensics. Spencer Bogart of Blockchain Capital invested in TRM Labs, citing the compliance infrastructure gap as one of the clearest institutional crypto needs.

Crystal Intelligence, Blockaid, and the Remaining Companies

Crystal Intelligence provides blockchain analytics and AML compliance with particular strength in European markets and cross-border transaction monitoring – covering 40+ blockchains. Blockaid approaches on-chain security from a different angle: transaction simulation and malicious dApp detection. Blockaid is now integrated into MetaMask, Coinbase Wallet, and Rainbow – but it protects at the transaction level rather than scoring the behavioral history of the parties behind transactions. Anchain.ai, CUBE AI, Merkle Science, NOTA BENE, and TestMachine occupy specialist positions serving government, institutional, and testing use cases across the category.

ChainAware.ai – The Behavioral Prediction Layer

ChainAware occupies a position in the On-Chain Intelligence category that no other company covers – behavioral prediction. While every other company answers “what has this wallet done or where did these funds come from?”, ChainAware answers “what will this wallet do next, and is this wallet likely to commit fraud before it acts?” That forward-looking prediction capability, combined with being the only Web3 AI token in the full 200-company CB Insights list, makes ChainAware uniquely positioned at the intersection of enterprise compliance and the decentralized token economy.

Why CB Insights Inclusion Matters for Enterprise Buyers

Enterprise procurement decisions for security and compliance infrastructure are significantly influenced by analyst validation. A security or compliance team evaluating on-chain intelligence vendors does not start with a Google search – they start with CB Insights, Gartner, Forrester, or IDC market maps. Inclusion in these maps is the difference between being considered and not being considered in enterprise vendor evaluations.

The Mosaic Score Gate

CB Insights selects companies based on its proprietary Mosaic score – a composite health measure incorporating funding recency, investor quality, web traffic, news sentiment, team quality, and patent activity. The AI Fraud Prevention map requires a Mosaic score above 600 and equity funding since 2024. Most projects in the blockchain space never appear on a CB Insights map because they fail either the Mosaic score threshold or the funding recency requirement. ChainAware’s inclusion confirms that its profile meets institutional investment standards – a signal that matters to the compliance officers, procurement teams, and CISOs who use CB Insights to shortlist vendors.

The Reference Check Effect

When a DeFi protocol’s compliance team receives a proposal from ChainAware, the first thing they do is verify the company’s credibility through third-party sources. The CB Insights listing now serves as that third-party validation – alongside CoinGecko’s AI category listing, the AWARE token on BSC, and ChainAware’s GitHub repository of open-source MIT-licensed agent definitions. Credibility signals compound. Each additional validation source reduces the friction of the enterprise sales cycle and increases the probability of converting enterprise interest into a signed API contract.

The CoinGecko AI Category – 1,385 Tokens, One Web3 AI Fraud Prevention Token

CoinGecko’s AI category currently lists 1,385 tokens – representing the full spectrum of AI-related blockchain projects, from Bittensor (decentralized AI compute) to Render (GPU network) to Virtuals Protocol (AI agent launchpad) to dozens of AI-themed meme coins. The category spans legitimate infrastructure projects, speculative tokens, and everything between.

Among these 1,385 tokens, ChainAware’s AWARE token is the only one building on-chain intelligence and behavioral fraud detection as its core product. None of the major forensic compliance companies – Chainalysis, Elliptic, TRM Labs, Crystal Intelligence – have tokens. None of Blockaid, Anchain.ai, Merkle Science, or NOTA BENE have tokens. They are pure SaaS companies with no token economy.

Why No Token Is the Default for Compliance Companies

Most on-chain intelligence companies avoid tokens for regulatory reasons – a tradeable token creates securities law complexity in most jurisdictions. Chainalysis, TRM Labs, and Elliptic have collectively raised over $1 billion in venture capital while deliberately remaining token-free. Their customers (banks, regulated exchanges, government agencies) cannot hold or use utility tokens as payment. ChainAware’s bifurcated model – enterprise API subscriptions for institutional clients plus the AWARE utility token for Web3 ecosystem participants – allows it to serve both audiences simultaneously without compromising either relationship.

The Unique Intersection

The combination of CB Insights validation and CoinGecko AI category listing creates a position that no competitor occupies. Companies on the CB Insights map without tokens serve institutional clients through SaaS contracts – their distribution is purely through enterprise sales cycles. Companies in the CoinGecko AI category without CB Insights validation are building token economies without institutional credibility. ChainAware sits at the intersection – credible enough for enterprise evaluation and token-native enough to participate in the decentralized economy it analyzes.

How ChainAware Differs From Every Other Company on the Map

Understanding ChainAware’s differentiation requires examining five dimensions where it diverges fundamentally from every other company in the On-Chain Intelligence category.

Dimension 1 – Prediction vs. Forensics

Every other company in the On-Chain Intelligence category is forensic – backward-looking by design. Chainalysis traces where funds came from. Elliptic reconstructs transaction graphs. TRM Labs identifies sanctioned counterparties. Crystal Intelligence monitors cross-border fund flows. All four describe the past. ChainAware predicts the future. Its behavioral ML models, trained on 20M+ wallet personas across 8 blockchains, produce probability scores for what a wallet will do next – not descriptions of what it has done. That prediction happens in milliseconds, before any transaction occurs, based on behavioral patterns that professional fraudsters cannot disguise by using clean contract code.

Dimension 2 – Fraud Tech and Growth Tech Combined

The CB Insights map treats fraud prevention as a purely defensive category – a cost center that organizations pay for to stay compliant and avoid losses. ChainAware reframes the category entirely by combining fraud prevention with growth intelligence in a single platform. ChainAware’s 20M+ wallet personas do not just tell a compliance team whether to block a wallet – they also tell a product team which content to show it, which features to surface, and which growth campaign to trigger. A wallet with high Lend intention and low fraud probability gets surfaced lending products automatically. A wallet with high fraud probability gets blocked before it enters the funnel. Both decisions come from the same behavioral intelligence layer. These same behavioral signals power ChainAware’s 32 ready-made Claude agents, which apply fraud screening and growth targeting directly inside AI agent workflows.

Dimension 3 – MCP-Native Delivery for AI Agents

AI agents need behavioral intelligence delivered in the format they can consume – structured predictions via the Model Context Protocol (MCP), not raw blockchain data that requires further analysis. According to Anthropic’s Model Context Protocol documentation , MCP is rapidly becoming the standard integration layer for AI agent tool access. ChainAware’s Prediction MCP delivers complete behavioral profiles – fraud probability, all 12 intention scores, experience level, risk appetite, AML status – in a single structured response that any AI agent can act on without blockchain expertise. For how this works in practice, see our Prediction MCP guide .

Dimension 4 – Token-Native Economic Model

ChainAware’s AWARE token creates an economic flywheel that enterprise-only SaaS competitors cannot replicate. Token holders who stake AWARE unlock higher API rate limits and premium intelligence tiers. Developers who build integrations with ChainAware’s API earn AWARE rewards. As the platform’s wallet persona dataset grows – currently at 20M+ profiles – the intelligence quality improves, increasing the value of AWARE access.

Dimension 5 – The Free Entry Point

Chainalysis charges $100,000 to $500,000 annually. TRM Labs requires enterprise negotiations. Elliptic does not publish pricing. ChainAware’s Wallet Auditor delivers the complete Web3 Persona for any address – free, no signup, in under one second. Any developer, compliance officer, or investor can experience the full depth of ChainAware’s behavioral intelligence without a sales conversation. For the complete dimension-by-dimension breakdown, see our Wallet Auditor guide .

Predictive Intelligence vs. Forensic Intelligence – The Critical Distinction

The most important conceptual distinction in the On-Chain Intelligence category is between forensic and predictive intelligence. Understanding this distinction explains why the entire category is funded heavily – and why ChainAware’s predictive position is structurally different from the forensic majority.

What Forensic Intelligence Does

Forensic intelligence analyzes the complete history of blockchain transactions to reconstruct fund flows, identify sanctioned counterparties, and attribute addresses to known entities. It answers: “Where did these funds come from, and who has touched them?” This capability is essential for post-incident investigation. However, forensic intelligence is structurally reactive – it requires the fraud to have already happened, or at minimum for the fraudulent address to already appear in its entity database. A professional operator using a fresh wallet that has never appeared in Chainalysis’s database is invisible to forensic tools until they commit their first recorded offense.

What Predictive Intelligence Does

Predictive intelligence analyzes behavioral patterns – not just transaction histories – to forecast what a wallet will do next and what the probability of fraud is before any transaction executes. ChainAware’s behavioral ML models train on 20M+ wallet personas – learning the behavioral signatures that distinguish legitimate DeFi users from professional fraud operators, Sybil wallets, airdrop farmers, and governance attackers. A professional fraudster can use clean contract code. They cannot mask their behavioral pattern across 20M+ training examples. The model detects the operator, not just the incident. For the complete technical comparison, see our Forensic vs AI-Powered Analytics guide .

The 98% Accuracy Benchmark

ChainAware backtested its fraud detection model on CryptoScamDB – the largest publicly available database of documented crypto fraud incidents – achieving 98% prediction accuracy. The model correctly identified fraudulent wallets before they committed their recorded offense in 98 out of every 100 cases in the test set. For compliance teams operating under MiCA or similar frameworks, that accuracy level dramatically reduces the manual review burden. For the complete MiCA compliance stack, see our MiCA Compliance at 1% of Chainalysis Cost guide .

ChainAware Rug Pull Detector – 90.1% Prediction Accuracy

Rug pulls represent the most damaging category of DeFi fraud by absolute dollar value. ChainAware’s Rug Pull Detector – trained specifically on PancakeSwap V2 data – achieves 90.1% prediction accuracy, identifying high-risk tokens before the rug pull occurs rather than after investors have lost funds.

The PancakeSwap V2 Dataset

ChainAware trained and validated its rug pull detection model on PancakeSwap V2 transaction data from weeks 1 through 20 of 2026 – covering $569 million in documented rug pull losses across thousands of token launches. This dataset is the largest and most recent rug pull training corpus available in the public domain for BNB Chain tokens. The training methodology uses behavioral signals from the contract deployer wallet and all LP providers – not contract code analysis. Professional rug pull operators know exactly which code patterns trigger existing contract scanners, and they code around them. Their behavioral history across 20M+ wallet personas reveals the signature of serial rug operators regardless of how clean their current contract appears.

Rug Pull Detector vs. Competing Tools

GoPlus, Token Sniffer, and Honeypot.is all analyze contract code – detecting known patterns of mint functions, blacklisting mechanisms, sell restrictions, and honeypot logic. These tools catch common scams that reuse known code patterns. They do not catch professional operators who deploy clean code specifically to evade code scanners. ChainAware’s Rug Pull Detector catches what code scanners miss – the experienced operator with a history of rugging who deploys a technically perfect contract but whose behavioral fingerprint across 20M+ personas identifies them as high risk. For the complete comparison, see our Best Web3 Rug Pull Detection Tools guide .

The Agentic Economy and Why It Needs a New Fraud Layer

The CB Insights AI Fraud Prevention Market Map was explicitly timed to coincide with the emergence of the agentic economy – the structural shift from human-operated financial systems to AI-agent-operated ones. Understanding this shift explains why the on-chain intelligence category is the fastest-growing by funding momentum in 2026.

Agents Are Not Humans

AI agents transacting on behalf of humans operate 24/7, across all time zones simultaneously, at machine speed, without the cognitive friction that slows human decision-making. An AI agent does not hesitate before a suspicious transaction – it executes at the speed of the LLM inference cycle. This eliminates the natural fraud prevention that human decision-making provides. Consequently, AI agents need external fraud intelligence to substitute for the human judgment they lack. ChainAware’s Prediction MCP delivers that intelligence in the format agents can consume – structured behavioral profiles via natural language queries, sub-second response, no blockchain expertise required. For integration details, see our 12 Blockchain Capabilities Any AI Agent Can Use .

Haun Ventures’ $1B Thesis – Word for Word

Katie Haun’s Haun Ventures $1 billion fund announcement, published May 4, 2026, contains the most precise description of ChainAware’s product from any institutional source: “Every supporting layer will need to be rearchitected for this world: fraud prevention, credit, insurance, identity, privacy, provenance, reputation, and verification all require native versions designed for how agents transact.” That sentence describes ChainAware’s product roadmap. Haun Ventures is not alone – Dragonfly Capital closed $650 million, a16z crypto closed $2.2 billion, ParaFi Capital raised $125 million – every major fund closing in 2026 has identified the same gap that ChainAware is building into.

The Market Signal – $6B+ in VC Funding Points at the Same Gap

The $6 billion+ deployed into crypto and Web3 infrastructure during the first five months of 2026 is the strongest institutional signal the sector has seen since 2021 – but with a fundamentally different thesis. The 2021 cycle was driven by speculation on token appreciation. The 2026 cycle is driven by infrastructure investment in the trust, compliance, and intelligence layers that the agentic economy requires.

The Fund Closing Timeline

Dragonfly Capital’s $650 million fourth fund closed February 17, 2026. ParaFi Capital’s $125 million raise closed in March 2026, focused on stablecoins, tokenization, and on-chain financial products. Haun Ventures announced $1 billion on May 4, 2026. a16z crypto’s $2.2 billion fifth fund announced May 5, 2026 – bringing its total crypto-focused assets to $9.8 billion. Blockchain Capital is actively raising $700 million. Paradigm’s rumored $1.5 billion includes an AI-plus-crypto thesis. Total confirmed capital: over $4.5 billion closed in the first five months of 2026, with another $2.2 billion in process. Every fund thesis identifies the same three investment areas: new financial infrastructure, new assets and markets, and the agentic economy.

ChainAware as Growth Tech – The Revenue Dimension of On-Chain Intelligence

The CB Insights map positions fraud prevention entirely as a defensive category. ChainAware’s growth tech layer reframes on-chain intelligence as a revenue-generating capability – where the same behavioral data that prevents fraud also drives conversion, retention, and user acquisition efficiency.

The 84% Ghost Wallet Problem

ChainAware’s analysis of 9,999 unique wallet addresses from a major Web3 marketing campaign found that 84% were ghost wallets: zero real engagement, zero meaningful transaction history, zero likelihood of converting into active protocol users. Every dollar spent acquiring ghost wallets is waste – the acquired “user” will never transact, never provide liquidity, never participate in governance, and never generate fee revenue. ChainAware’s growth intelligence layer converts this waste into signal. Before running a campaign, protocols can screen target wallet lists through the Fraud Detector and Wallet Auditor – removing ghost wallets, Sybil clusters, and airdrop farmers from the acquisition pool before spending budget on them. For the complete framework, see our DeFi Onboarding guide .

The 12 Intention Scores as Growth Signals

ChainAware’s 12 behavioral intention scores – Borrow, Lend, Trade, Gamble, NFT, Stake ETH, Stake Yield Farm, Leveraged Staking, Leveraged Staking ETH, Leveraged Lending, Leveraged Long ETH, Leveraged Long Game – are not just risk signals. They are growth signals that tell a protocol exactly which products to surface to each connecting wallet. A wallet with High Lend intention should see lending products featured first. A wallet with Low Experience should see simplified onboarding. Neither wallet needs to self-identify their interests – the behavioral history already tells the protocol everything it needs to know. For the complete growth deployment architecture, see our User Segmentation guide .

Full Competitive Landscape – CB Insights Map Breakdown

The full CB Insights AI Fraud Prevention Market Map covers 200+ companies across six major sections. Understanding the complete map reveals where ChainAware’s behavioral intelligence layer fits within the broader fraud prevention ecosystem – and which categories represent potential integration partners rather than competitors.

Agentic Trust Infrastructure – A Partnership Category

The Agentic Trust Infrastructure section covers agent observability and evaluation (Arize, LangChain, Patronus AI), agent authentication and authorization (xAembit, Arcade, AuthMind, Skyfire), and agent runtime governance (Ciphero, HUMAN, Witness AI). ChainAware’s Prediction MCP is a natural integration layer for all three subcategories – adding on-chain behavioral fraud detection to agent monitoring, authentication, and governance workflows that these platforms currently lack.

Digital Identity – Complementary, Not Competing

The Digital Identity section covers decentralized identity (DID), passwordless authentication, post-quantum identity, KYC, and biometric identity. Companies like Humanity Protocol, Billions, Self, and zkMe provide proof-of-personhood and verifiable credentials – confirming that a wallet is controlled by a unique human. DID systems answer “is this wallet controlled by a unique person?” ChainAware answers “is this person’s behavior consistent with fraud – and what will they do next?” These questions are complementary, not overlapping. For how ChainAware integrates with DID systems, see our Blockchain Compliance guide .

AML Compliance – The Enterprise Complement

The AML compliance subcategory includes Amlyze, Comply Advantage, Fiverity, Hawk AI, Natech, and Sphinx – all providing transaction monitoring and AML reporting for regulated financial institutions. ChainAware’s AML screening and behavioral fraud detection complement these platforms rather than replacing them. Enterprise AML systems provide regulatory reporting, case management, and audit trails. ChainAware provides the pre-execution risk signal that determines which transactions require closer AML review. For the complete DeFi compliance stack, see our DeFi Compliance Tools guide .

How to Use ChainAware’s Intelligence Products Today

All three ChainAware intelligence products are available without signup, without wallet connection, and without a sales conversation. The free tier delivers the complete product – not a limited preview.

Rug Pull Detector

Navigate to chainaware.ai/rug-pull-detector . Paste any ERC-20 or BEP-20 token contract address. The detector returns a rug pull probability score, a breakdown of the risk factors identified, and a behavioral assessment of the contract deployer and LP providers. Results are available in under 3 seconds. No account required. Use it before buying any new token – especially on BNB Smart Chain where the $569 million PancakeSwap V2 dataset gives the model its highest accuracy. For the full methodology behind this scoring, see the Rug Pull Detector learn page.

Fraud Detector

Navigate to chainaware.ai/fraud-detector . Paste any wallet address. The detector returns fraud probability (98% accuracy), AML status, OFAC screening result, and a behavioral summary. Covers ETH, BNB, BASE, POLYGON, TON, TRON, HAQQ, and SOL. Results are available in under 1 second. No account required. Use it to screen wallets before approving DeFi protocol interactions and to verify team wallet addresses published by new token projects. Full scoring methodology is documented on the Fraud Detector learn page.

Wallet Auditor

Navigate to chainaware.ai/audit . Paste any wallet address. The Wallet Auditor returns the complete 22-dimension Web3 Persona: fraud probability, all 12 intention scores, experience level, risk appetite, AML status, OFAC screening, Wallet Rank, wallet age, transaction count, and balance. For the complete guide, see our Wallet Auditor guide . The full 22-dimension methodology is also documented on the Wallet Auditor learn page.

API Access and Prediction MCP

For teams integrating ChainAware intelligence at scale, the REST API provides full access to all intelligence products at volume. The Prediction MCP server at prediction.mcp.chainaware.ai/sse delivers complete behavioral profiles to any MCP-compatible AI agent in under 1 second. API documentation is available at swagger.chainaware.ai. For setup instructions and the full tool list, see the Prediction MCP learn guide.

Frequently Asked Questions

What is the CB Insights AI Fraud Prevention Market Map?

The CB Insights AI Fraud Prevention Market Map, published June 2, 2026, identifies 200+ companies building identity, trust, and fraud prevention infrastructure for the AI era. CB Insights selects companies based on Mosaic health scores above 600 and equity funding since 2024. ChainAware appears in the On-Chain Intelligence subcategory – alongside Chainalysis, Elliptic, TRM Labs, Crystal Intelligence, and Blockaid – as the only Web3 AI token in the full list.

Why is ChainAware the only Web3 AI token in the CB Insights list?

Most on-chain intelligence companies – Chainalysis, Elliptic, TRM Labs, Crystal Intelligence, Blockaid – are pure SaaS businesses with no publicly traded token. They serve regulated institutional clients who cannot hold utility tokens, and they avoid tokens for regulatory complexity reasons. ChainAware’s bifurcated model – enterprise API subscriptions for institutional clients plus the AWARE utility token for Web3 ecosystem participants – allows it to appear in both institutional and decentralized discovery channels simultaneously.

How does ChainAware’s 90.1% rug pull accuracy compare to other tools?

GoPlus, Token Sniffer, and Honeypot.is analyze contract code – they do not publish accuracy statistics because they report risk flags rather than probability scores. ChainAware’s 90.1% accuracy is a backtested performance metric on the PancakeSwap V2 dataset covering $569 million in documented rug pulls from weeks 1 through 20 of 2026. The key distinction is that ChainAware’s model analyzes behavioral history of the contract deployer and LP providers – catching professional operators who deploy clean code to evade code scanners. For detailed methodology, see our Rug Pull Detection guide .

What is the difference between ChainAware and Chainalysis?

Chainalysis is a forensic compliance platform designed for law enforcement and regulated exchanges – it traces where funds came from after transactions have occurred, with enterprise pricing from $100,000 to $500,000 annually. ChainAware is a predictive behavioral intelligence platform designed for DeFi protocols, AI agents, and compliance teams – it predicts fraud before transactions execute, with a free tier and accessible API pricing. The two are complementary: Chainalysis provides post-incident forensics; ChainAware provides pre-execution fraud prevention. For the complete cost comparison, see our MiCA Compliance at 1% of Chainalysis Cost guide .

How does the Prediction MCP work for AI agents?

ChainAware’s Prediction MCP server is accessible at prediction.mcp.chainaware.ai/sse. Any MCP-compatible AI agent – Claude, GPT, or any other LLM – can connect to the MCP and query behavioral profiles via natural language. The agent sends a query such as “What is the fraud risk and behavioral profile of 0x2f71…?” and receives a structured response containing fraud probability, all 12 intention probabilities, experience level, risk appetite, AML status, and Wallet Rank – all pre-computed, in under one second. According to Anthropic’s MCP documentation , MCP is becoming the standard for AI agent tool access. For the integration guide, see our Prediction MCP guide .

Can ChainAware detect governance attacks before they execute?

Yes – governance attack detection is one of ChainAware’s most differentiated capabilities. DAO governance attacks typically use Sybil wallet clusters – coordinated addresses that each hold small token amounts and vote together to achieve disproportionate governance influence. ChainAware’s behavioral model detects these clusters by identifying wallets that share funding sources, exhibit synchronized transaction timing, and demonstrate consistent co-voting behavior across multiple governance proposals. For the complete governance attack detection framework, see our Web3 Governance Screeners guide .

How does ChainAware’s behavioral intelligence help with MiCA compliance?

MiCA (Markets in Crypto-Assets Regulation) requires crypto asset service providers operating in the EU to implement transaction monitoring, AML screening, and customer risk assessment. ChainAware’s Fraud Detector and AML screening cover the pre-execution risk assessment requirement – delivering 98% accurate fraud probability and real-time AML/OFAC screening for every wallet interacting with a MiCA-covered service. According to FATF’s Virtual Assets Recommendations , transaction monitoring requirements increasingly mandate real-time screening capabilities. For the complete implementation guide, see our DeFi Compliance Tools guide .

What makes ChainAware’s position in CoinGecko’s AI category strategically valuable?

CoinGecko’s AI category receives millions of views monthly from users specifically searching for AI-related blockchain investments and infrastructure. Being the only on-chain intelligence and behavioral fraud detection project among 1,385 tokens creates a discovery advantage that pure enterprise SaaS competitors cannot replicate. A developer researching AI-native blockchain tools who browses the CoinGecko AI category finds ChainAware as the only fraud intelligence and behavioral scoring option – without competition from Chainalysis, Elliptic, or TRM Labs who have no token presence. The combination of institutional validation from CB Insights and retail discovery via CoinGecko creates a dual-channel visibility that no competitor in either ecosystem can match.

External Sources: CB Insights AI Fraud Prevention Market Map · CoinGecko AI Category · Anthropic Model Context Protocol · FATF Virtual Assets Recommendations · Chainalysis Platform

The post ChainAware.ai Named in CB Insights AI Fraud Prevention Market Map – The Only Web3 AI Token in the List first appeared on ChainAware.ai.

103,695 separate rug pull events. $1,947,176,810 in liquidity removed by contract creators. $1,377,788,426 added before removal. The difference – $569,388,384 – flowed directly out of retail wallets and into the pockets of fraudulent actors operating industrial-scale extraction infrastructure on one of the world’s largest decentralized exchanges. Every single week. Quietly. Without ceremony.

This is the rug pull industry. Not a bug. An industry.

Today, ChainAware.ai publishes this data for the first time – and simultaneously launches Rug Pull Detector V3, our most advanced version yet, with 90.1% prediction accuracy achieved by combining behavioral analysis of contract creators with full smart contract inspection. This is the complete picture: what the data shows, why nobody talks about it, how the extraction machinery works, and how to stop it.

In This Guide

• The $569M Dataset: What We Measured and How
• Week-by-Week Breakdown: 20 Weeks of Retail Extraction
• The Industry Silence Problem: Why Nobody Talks About Rug Pulls
• How Rug Pulls Work: The Mechanics of Liquidity Extraction
• Beyond Basic Rug Pulls: The More Complex Extraction Methods We Did Not Count
• Rug Pull Detector V3: From 68% to 90.1% Prediction Power
• How the V3 Algorithm Works: Behavioral + Smart Contract Analysis
• Algorithm Verification and Accuracy Methodology
• Who Uses Rug Pull Detector: Retail Investors, Businesses, and AI Agents
• Projection: How Many Rug Pulls in the Next 20 Weeks?
• The Complete Protection Stack for DApps and Retail Investors
• Frequently Asked Questions

The $569M Dataset: What We Measured and How

ChainAware analyzed every liquidity event on PancakeSwap V2 across weeks 1 through 20 of 2026. The methodology is deliberately conservative. We measured only the most basic, unambiguous form of rug pull: a contract creator adds liquidity to a pool, then removes more than they added. The difference between liquidity added and liquidity removed – when removal exceeds addition – constitutes the rug pull value we report.

This definition intentionally excludes more sophisticated extraction methods. Complex multi-step schemes involving LP token transfers, associated wallet sell-offs, and unlocked token dumps are not included in these numbers. The $569M figure represents the floor – the minimum provably fraudulent extraction we could measure with mathematical certainty from on-chain data alone.

The full dataset covers:

• Total rug pull events detected: 103,695
• Total liquidity added by creators (Mints): $1,377,788,426
• Total liquidity removed by creators (Burns): $1,947,176,810
• Net extraction (Burns minus Mints): $569,388,384
• Period: Week 1 through Week 20, 2026
• Exchange: PancakeSwap V2 (BNB Chain)

PancakeSwap V2 on BNB Chain is one of the highest-volume decentralized exchanges in the world. It is also, by our measurement, one of the largest venues for systematic retail investor fraud. The combination of low gas fees, high token creation velocity, and large retail liquidity makes BNB Chain the preferred operating environment for industrial-scale rug pull operations.

For context on just how large this ecosystem of fraud is: the Bybit hack in February 2025 – which generated enormous industry coverage, emergency response coordination, and weeks of Twitter discussion – extracted $1.46 billion. Our 20-week PancakeSwap V2 measurement represents 39% of that headline-grabbing figure. On one DEX. In one 20-week window. With virtually zero media coverage.

Week-by-Week Breakdown: 20 Weeks of Retail Extraction

The weekly data reveals patterns that any serious analyst of DeFi fraud should study carefully. Rug pull activity is not random noise. It follows detectable rhythms tied to market sentiment, BNB price movements, and the operational cadence of the fraud factories running these schemes.

What the Weekly Patterns Tell Us

Four distinct patterns emerge from careful analysis of this 20-week dataset. Each pattern carries significant implications for how rug pull operations are structured and how retail investors can protect themselves.

Pattern 1 – Early surge, then stabilization: Weeks 1 through 5 represent the highest-intensity period, with Week 4 peaking at $53.4M in a single week. This early-year surge correlates with January-February market optimism, when retail capital flows into DeFi most aggressively following holiday periods. Fraud operators know this and deploy capital accordingly.

Pattern 2 – Persistent baseline fraud: Even in quieter weeks (W8, W17, W18, W19), fraud never drops to zero. The range of $12.6M to $16.7M represents what we call the baseline rug pull floor – the irreducible minimum level of extraction that persists regardless of market conditions. These weeks demonstrate that rug pulls are not opportunistic responses to bull markets. They are a continuous, professionally operated business.

Pattern 3 – Pool creation velocity diverging from fraud events: Week 2 shows the highest pool creation (24,145 total pools) but does not produce the highest fraud value. Week 4 shows far fewer pools (17,984) but delivers the peak fraud extraction. This divergence suggests that fraud operators optimize for pool value, not pool volume – they are creating fewer but more lucrative pools rather than flooding the market with low-value scam tokens.

Pattern 4 – Week 20 resurgence: After a six-week decline from W14 through W19, Week 20 spikes back to $39.9M – the second-highest single-week figure in the dataset. This resurgence suggests cyclical fraud campaigns that compress activity during low-sentiment periods and re-accelerate when retail interest returns. The Week 20 data point is a leading indicator, not an anomaly.

The Industry Silence Problem: Why Nobody Talks About Rug Pulls

Here is a question worth sitting with: Why does a $1.46B exchange hack generate hundreds of articles, emergency response calls, and weeks of industry-wide discussion – while $569M in retail losses across 20 weeks generates almost no coverage at all?

The answer is structural. Exchange hacks are dramatic, concentrated, and attributable. They happen to institutions – Bybit, Binance, Euler Finance – that have PR teams, legal counsel, and market presence. Rug pulls happen to anonymous retail investors, dispersed across thousands of small transactions, with no single victim large enough to command media attention and no single perpetrator identifiable enough to pursue.

$53.4M stolen from a major exchange in a single transaction is a crisis. $53.4M stolen from 6,000 retail investors across thousands of micro-transactions in a single week is business as usual. The victims are too distributed to organize. The fraud is too normalized to shock. The perpetrators are too anonymous to name.

This structural invisibility benefits the rug pull industry enormously. Unlike hacks – which trigger security audits, insurance claims, regulatory investigations, and protocol upgrades – rug pulls operate in a consequence-free zone. The operators who ran this week’s $28M extraction will run next week’s extraction with identical infrastructure. Nothing stops them. Nobody is watching. The industry is busy discussing the latest Layer 2 throughput numbers.

The Chainalysis 2025 Crypto Crime Report documents $17 billion in total crypto scam losses – a figure that includes rug pulls, phishing, fake investments, and other fraud categories. Rug pulls constitute one of the largest subcategories within this total, yet they receive proportionally far less analytical attention than flash loan exploits, bridge hacks, and protocol vulnerabilities.

Part of the problem is that rug pulls do not fit the traditional Web3 security narrative. Security firms make revenue from smart contract audits, not from behavioral fraud detection. Media outlets generate traffic from dramatic hacks, not from steady-state extraction statistics. Influencers amplify projects that pay them, not warnings that might upset project teams with marketing budgets. The incentive structure of the crypto information ecosystem systematically underweights the most consistent form of retail harm.

ChainAware exists, in part, to change this. Publishing this data is the first step. Building tools that let retail investors and DApps verify tokens and pools before committing capital is the second. For a broader perspective on the comparison between rug pulls and pump-and-dump schemes and how each extracts value from retail investors, our dedicated guide covers the full mechanics of both fraud types.

How Rug Pulls Work: The Mechanics of Liquidity Extraction

Understanding rug pull mechanics is the foundation of avoiding them. The basic liquidity rug pull – the type we measured in this dataset – follows a repeatable, five-step operational sequence that any investor can learn to recognize.

Step 1: Token and Pool Creation

A fraudulent actor deploys a new ERC-20 or BEP-20 token contract. On BNB Chain, this costs a fraction of a dollar and takes minutes. The token contract typically includes mint functions (allowing the creator to generate unlimited supply), hidden transfer restrictions (preventing buyers from selling), and ownership functions that are often not renounced. The pool is created on PancakeSwap V2 by pairing the new token with BNB or USDT, establishing an initial price.

Step 2: Liquidity Seeding

The creator deposits initial liquidity – real BNB or USDT – into the pool alongside the worthless token they created. This establishes a tradeable pair and makes the token appear legitimate on DEX aggregators and portfolio trackers. The liquidity seed is the bait. It represents the money the fraudster is temporarily risking to attract retail capital.

Step 3: Marketing and Social Momentum

Telegram groups are created. Twitter accounts post about the new token. Sometimes influencers are paid to promote it. Bots generate artificial trading volume to make the token appear active. Retail investors see a token with real liquidity, active trading, and social proof – and buy in. Each purchase increases the price and deepens the liquidity pool with real capital.

Step 4: Liquidity Removal

Once sufficient retail capital has entered the pool, the creator burns their LP tokens – withdrawing all liquidity from the pool. This single transaction extracts all the BNB or USDT that retail investors deposited when they bought the token. The token price drops to zero instantly. Holders are left with worthless tokens that cannot be sold. The creator walks away with the full liquidity amount, minus their initial seed deposit. The difference is pure profit extracted from retail investors.

Step 5: Repeat

Professional rug pull operators do not run one scheme. They run dozens simultaneously, across multiple wallets, with industrialized tooling that automates contract deployment, social media posting, and liquidity management. The 103,695 rug pull events in our dataset represent the output of a mature industry with operational infrastructure comparable to a sophisticated affiliate marketing operation – except the product being sold is fraud.

Beyond Basic Rug Pulls: The More Complex Extraction Methods We Did Not Count

Our $569M figure covers only the most conservative, mathematically verifiable form of rug pull: direct liquidity removal exceeding direct liquidity addition, by the same contract creator. This definition was chosen deliberately – it produces numbers that cannot be disputed, because they are derived purely from on-chain transaction data with no inferential assumptions.

However, professional rug pull operators frequently use more sophisticated extraction methods that our current measurement excludes. Understanding these methods matters for two reasons: first, because they represent additional losses not captured in the $569M figure; second, because ChainAware’s Rug Pull Detector V3 is being extended to detect these more complex patterns in future iterations.

LP Token Transfer Rug Pulls

Instead of burning LP tokens directly, the creator transfers them to a secondary wallet before burning. This adds one intermediary step that breaks the direct creator-to-burn attribution our basic methodology requires. The economic outcome is identical – retail liquidity is extracted – but the attribution chain is one step longer. Detecting this pattern requires tracking LP token ownership across wallets, not just monitoring mint/burn events on the original creator address.

Unlocked Token Sell-Offs

Some rug pulls never involve liquidity removal at all. Instead, the creator holds a large pre-minted supply of unlocked tokens and sells them gradually into the market as retail buyers push the price up. This is the “slow rug” – a controlled sell-off that extracts value over days or weeks rather than in a single transaction. Detecting this requires monitoring creator wallet sell behavior relative to price action, not liquidity pool events.

Associated Party Extraction

Sophisticated operators fund multiple secondary wallets that receive token allocations at launch and sell into retail buying pressure. The creator’s primary wallet never transacts after the initial liquidity seed – only associated wallets do. Connecting these wallets to the creator requires graph analysis of funding transactions, not just monitoring of the deployer address.

Honeypot Contracts

A honeypot is a contract that allows buying but blocks selling. Transfer restrictions embedded in the contract – hidden from Etherscan views unless you know where to look – prevent token holders from executing sell transactions. Buyers accumulate tokens they cannot sell, while the creator sells their pre-minted allocation freely. GoPlus Security detected 67,241 honeypot tokens in Q4 2024 alone – a figure that underscores the scale of this specific fraud variant. For a full comparison of rug pull detection tools that cover honeypot analysis, see our guide to best Web3 rug pull detection tools in 2026.

The conservative $569M figure – our confirmed minimum – would be substantially higher if all these additional extraction methods were included. ChainAware’s V3 algorithm already incorporates smart contract analysis that can detect honeypot patterns and some forms of unlocked token sell-off risk. Future iterations will extend this coverage further.

Rug Pull Detector V3: From 68% to 90.1% Prediction Power

The previous version of ChainAware’s Rug Pull Detector operated at approximately 68% prediction accuracy. For retail investors, that accuracy level is better than nothing – significantly better than the zero-analysis approach most investors use. However, 68% means that roughly one in three high-risk pools would pass the detector without a warning, and some legitimate pools would trigger false positives.

Our customers asked directly: can we do better? The answer is V3 – and the answer is yes.

ChainAware Rug Pull Detector V3 achieves 90.1% prediction accuracy. This jump from 68% to 90.1% represents a 32.5% relative improvement in prediction power – the largest single-version upgrade in the detector’s history. The improvement comes from a fundamental architecture change: V2 relied exclusively on behavioral analysis of contract creators. V3 combines behavioral analysis with full smart contract inspection.

Why the V2 Accuracy Gap Existed

Behavioral analysis alone – examining the on-chain history of the wallet that deployed a contract – is a powerful signal, but it has a ceiling. Experienced fraud operators know that behavioral signals can be spoofed. They create fresh wallets with clean histories. They fund deployer wallets through legitimate channels. They space out deployments to avoid clustering signals that behavioral models flag.

A behavioral model trained purely on creator wallet history will inevitably miss sophisticated operators who invest in maintaining clean deployer identities. This is the category that the 32% gap in V2 accuracy primarily represented. The false negatives were concentrated in professional, well-organized fraud operations – precisely the operators responsible for the largest individual rug pull events.

What V3 Adds: Smart Contract Analysis

Smart contract analysis reads the code of the contract itself – not just the history of the wallet that deployed it. Regardless of how clean a deployer wallet’s history looks, a contract with hidden mint functions, owner-only transfer restrictions, or unchecked liquidity lock mechanisms will trigger V3’s contract analysis layer.

This combination closes the gap that sophisticated fraud operators had exploited in V2. A fraudster who maintains a clean wallet but deploys a honeypot contract now triggers the smart contract analysis layer even when the behavioral analysis layer returns a clean signal. Conversely, a wallet with minor behavioral flags but a fully transparent, auditable contract receives a more accurate risk assessment that prevents false positives on legitimate projects.

The 90.1% accuracy figure represents the combined performance of both layers together – it is the V3 ensemble model’s prediction power, not either layer in isolation. The algorithm remains under active development. We expect accuracy to continue improving as the training dataset expands and additional smart contract analysis patterns are incorporated.

How the V3 Algorithm Works: Behavioral + Smart Contract Analysis

V3 runs two parallel analysis pipelines that produce independent risk scores, then combines them through an ensemble model trained on verified historical rug pull events. Both pipelines run in real time – the full analysis completes in under two seconds for any pool or token address submitted to the detector.

Pipeline 1: Creator Behavioral Analysis

The behavioral analysis pipeline examines the complete on-chain history of the wallet that deployed the contract. ChainAware’s 20M+ wallet persona database, trained across 8 blockchains, provides the foundation for this analysis. The pipeline evaluates multiple behavioral dimensions that collectively predict fraudulent intent:

• Deployment history: How many contracts has this wallet deployed? What is the historical fate of those contracts – did their pools maintain liquidity or were they rugged?
• Funding provenance: Where did the capital to seed liquidity originate? Wallets funded from known mixer outputs, fresh exchange withdrawals, or clusters of associated addresses receive elevated risk scores.
• Creator feeder analysis: Wallets that funded the deployer are also examined. A deployer wallet with a clean history but funded by a wallet with prior rug pull associations triggers a feeder-chain risk signal.
• Temporal patterns: How quickly after token deployment was liquidity removed in prior contracts from this wallet or associated wallets? Short hold periods are a strong predictor of rug pull intent.
• Wallet age and diversity: Fresh wallets with minimal on-chain history and a single purpose (token deployment and liquidity management) score significantly higher than wallets with years of diverse on-chain activity.

This behavioral layer is powered by the same predictive intelligence that drives ChainAware’s broader wallet analysis capabilities. For context on how the underlying wallet behavioral analysis works across other use cases, our guide to using the ChainAware Wallet Auditor covers the full 9-parameter profile in detail. The full methodology behind this deployer-level scoring is also documented on the Rug Pull Detector learn page.

Pipeline 2: Smart Contract Analysis

The smart contract analysis pipeline inspects the deployed contract code directly. For verified contracts (where source code is published), the analysis performs AST (Abstract Syntax Tree) parsing – examining the structural logic of the contract to identify dangerous patterns. For unverified contracts, bytecode inspection is used to detect characteristic opcode sequences associated with honeypot restrictions and hidden mint functions.

The contract analysis examines specific risk patterns:

• Hidden transfer restrictions: Functions that block selling by non-owner addresses, often disguised within complex conditional logic that is not obvious from casual code review.
• Owner-privileged mint functions: Unrestricted mint capabilities controlled by the deployer allow infinite token supply expansion after retail investors have bought in.
• Ownership renouncement status: Contracts that have not renounced ownership retain the ability to modify transfer restrictions, fee structures, and other parameters after launch.
• Liquidity lock verification: Whether LP tokens are locked – and in what contract, with what unlock conditions – is a critical signal. Unlocked LP tokens in the deployer’s wallet represent immediate rug pull risk.
• Fee manipulation functions: Contracts with owner-callable functions to increase buy/sell taxes after launch can effectively trap investors by making selling economically unviable.

For additional context on how AI-powered smart contract analysis compares to traditional audit approaches, our guide to AI-powered blockchain analysis and machine learning for crypto security covers the broader landscape of ML-based fraud detection.

The Ensemble Model

Outputs from both pipelines feed into an ensemble model that produces a single composite risk score between 0 and 100. Scores above 75 trigger a high-risk warning. Scores between 50 and 75 generate a medium-risk flag with specific risk factors highlighted. Scores below 50 return a lower-risk assessment – though not a guarantee of legitimacy, since the algorithm continues to develop and some novel fraud patterns may not yet be captured.

The ensemble model is trained on a labeled dataset of confirmed rug pull events – including events from the 103,695 cases in our PancakeSwap V2 analysis – and updated continuously as new rug pull events are confirmed. This continuous retraining is what allows the algorithm to adapt to evolving fraud operator tactics rather than becoming outdated as the fraud industry develops new evasion methods.

Algorithm Verification and Accuracy Methodology

The 90.1% accuracy figure requires explanation of its methodology, because prediction accuracy claims in the crypto security space are frequently made without rigorous verification frameworks. ChainAware’s accuracy is measured against historical confirmed rug pull events using a held-out test set – contracts and pools that were not included in the training data but whose eventual rug pull status is now known.

Full verification methodology, including the test set composition, evaluation metrics, false positive and false negative rates by pool type, and comparison to V2 baseline performance, is published at chainaware.ai/resources/rugpull-verification ↗.

Three important caveats about the 90.1% figure deserve explicit statement:

Caveat 1 – The algorithm is in active development. 90.1% is the current measured performance. We expect this number to improve. We also acknowledge that novel fraud patterns not yet present in our training data could temporarily reduce real-world performance below this benchmark.

Caveat 2 – False negatives exist. Roughly 9.9% of rug pull events will not be flagged by V3. These are concentrated in the most sophisticated fraud operations – those that maintain clean creator wallets and deploy contracts that pass automated inspection. Human review of tokenomics, social channels, and team identity remains important for high-value investment decisions.

Caveat 3 – False positives also exist. Some legitimate projects will receive elevated risk scores, particularly those deployed by newer wallets without extensive on-chain history or those using novel contract architectures that match patterns we associate with fraud. V3 is designed to flag risk, not to definitively declare fraud – the final investment decision always rests with the human investor.

For comparison with how other detection tools benchmark their accuracy, the broader context of the forensic versus AI-powered blockchain analysis framework explains why predictive accuracy figures differ fundamentally from forensic identification rates.

Who Uses Rug Pull Detector: Retail Investors, Businesses, and AI Agents

ChainAware’s Rug Pull Detector V3 serves three distinct user categories, each with different integration paths and use case requirements.

Retail Investors: Free Web Tool

Individual investors access the Rug Pull Detector at no cost through the web interface at chainaware.ai/rugpull. No account creation is required. Submit any pool address or token contract address on supported chains, and V3 returns a complete risk analysis within seconds. The tool handles both liquidity pool addresses (where additional LP-specific checks run) and regular token contract addresses.

For retail investors who want to go beyond rug pull risk and understand the complete behavioral profile of any wallet – including their own – the ChainAware Wallet Auditor provides a 9-parameter profile covering experience, risk willingness, intentions, AML status, and Wallet Rank. Both tools are free and require no signup. The full profile methodology is documented on the Wallet Auditor learn page.

DApps and Businesses: API Subscription

Businesses that need to check new pools or existing tokens at scale – DEX aggregators, portfolio trackers, launchpads, DeFi protocols – can subscribe to the Rug Pull Detector API. The API provides the same V3 analysis through a programmatic interface, enabling integration into existing DApp infrastructure without requiring users to manually submit addresses.

For DApps that want to screen wallet connections in real time – catching bad actors at the moment of wallet connection rather than at the moment of investment – the broader ChainAware suite integrates via Google Tag Manager pixel with zero code changes. See the complete Transaction Monitoring Agent guide for how automatic wallet screening works in a live DApp environment.

DApps that want to understand not just fraud risk but the full behavioral profile of their user base – their intentions, experience levels, and conversion likelihood – can combine the Rug Pull Detector API with ChainAware’s Web3 Behavioral User Analytics for a complete picture of who is using their platform and which users represent fraud risk versus conversion opportunity.

AI Agents: X402 Micropayment Protocol

AI agents operating autonomously in DeFi environments – executing trades, managing portfolios, or conducting due diligence on behalf of human principals – can access Rug Pull Detector V3 through the X402 micropayment protocol. This enables agents to pay per analysis in real time without requiring pre-approved API keys or subscription agreements.

An agent evaluating whether to provide liquidity to a new pool, or whether to purchase a newly launched token as part of a portfolio strategy, can query V3 and receive a risk assessment as part of its decision-making pipeline. This integration pattern – AI agents using on-chain behavioral intelligence to make better decisions – is the core use case that ChainAware’s MCP integration guide covers in detail, and the full setup process is documented on the Prediction MCP learn page. For the complete framework of how AI agents are replacing human functions in Web3, our guide to the Web3 agentic economy provides essential context.

Projection: How Many Rug Pulls in the Next 20 Weeks?

Based on the 20-week dataset, what does the next 20-week period look like? This is not a rhetorical question. The data provides enough signal to make a structured projection.

The Baseline Projection

Averaging the 20 weeks of data gives a mean weekly rug pull extraction of approximately $28.5M (total $569.4M divided by 20 weeks). If the next 20 weeks perform at the historical mean, the projected extraction would be approximately $570M – virtually identical to the first 20-week period.

However, averages obscure the variance that makes this projection more nuanced. Week 17 produced just $12.6M. Week 4 produced $53.4M. The range is wide – and what drives the range matters for any forward projection.

Bull Market Scenario

If BNB and the broader crypto market experience a significant price rally in the next 20 weeks, retail capital inflows to PancakeSwap V2 will increase. More retail capital entering DEX pools means more capital available for extraction. In a bull market scenario, weekly extraction figures could return to the W3-W5 range ($38M-$53M per week), pushing the 20-week total toward $800M-$900M.

Bear Market Scenario

Sustained market decline reduces retail participation in speculative DeFi activity. The W16-W19 pattern – four consecutive low weeks averaging $15M – represents what a low-sentiment environment looks like. In a bear market scenario, the next 20-week total could fall to $250M-$350M.

The Conservative Certainty

Regardless of market direction, one conclusion is near-certain: rug pulls will continue to extract hundreds of millions of dollars from retail investors in the next 20 weeks. The fraud infrastructure exists, is profitable, and faces no meaningful deterrent. The floor established by the W17-W19 data ($12.6M-$15.1M per week) implies a minimum 20-week extraction of approximately $250M under the most pessimistic scenario.

The only variable the industry controls is how many investors check tokens and pools before buying – and how accurate those checks are. That is the market for Rug Pull Detector V3.

The Complete Protection Stack for DApps and Retail Investors

Rug pull detection is one layer of a complete Web3 fraud protection stack. Understanding where it sits relative to other security tools helps both retail investors and DApp teams build comprehensive protection rather than relying on any single tool.

For Retail Investors: The Three-Check Protocol

Before committing capital to any new token or pool, a thorough retail investor runs three checks. First, they use ChainAware’s Rug Pull Detector to assess the pool or token’s fraud risk – covering both creator behavior and contract analysis. Second, if the rug pull check flags concerns, they use the Fraud Detector to drill into the specific wallets associated with the token deployment. Third, before sending funds to any individual wallet address, they use the Wallet Auditor to assess the receiving address’s behavioral profile and AML status.

This three-check approach takes less than five minutes per investment decision and provides protection against the most common forms of DeFi fraud. It will not catch every sophisticated attack – nothing will – but it filters out the vast majority of the 103,695 rug pull events in our dataset, which are predominantly straightforward enough to be detected by V3’s behavioral and contract analysis.

For a complete understanding of the crypto security landscape and how different tools protect against different threat vectors, the guide to crypto wallet security in 2026 covers hardware wallets, behavioral intelligence, and fraud prevention in a single comprehensive framework.

For DApps: Pre-Connection Screening

DApps face a different version of the rug pull problem: they are not the ones buying potentially fraudulent tokens, but they are being used as distribution channels by users who may have funded their activity through fraud proceeds. A DApp that allows a rug pull operator to use its interface for withdrawals or swaps becomes part of the fraud infrastructure – and faces potential compliance exposure under FATF guidelines and MiCA regulations.

ChainAware’s DApp protection layer screens connecting wallets at the moment of wallet connection – before any transaction is submitted. Wallets associated with known rug pull contracts, flagged behavioral patterns, or AML-positive addresses are identified at the connection stage and can be blocked, flagged for review, or routed to restricted functionality automatically. The full architecture of this pre-connection screening is covered in our guide to Web3 fraud detection for DApps in 2026.

For DApps operating under MiCA requirements, ChainAware’s compliance layer provides 70-75% MiCA coverage at approximately 1% of the cost of traditional compliance solutions from Chainalysis or Elliptic. The complete comparison of DeFi compliance tools and costs is available in our DeFi compliance tools comparison for 2026.

The AML Layer: Complementary, Not Overlapping

AML screening and rug pull detection address different risk vectors. AML screening asks: is this wallet associated with known illicit activity – sanctions lists, mixer usage, dark web transactions? Rug pull detection asks: is this contract creator likely to extract liquidity before investors can exit?

These questions are complementary. A wallet can be AML-clean but behaviorally likely to rug – a fresh wallet with no prior illicit associations but a clear deployment pattern matching rug pull operators. Conversely, a wallet can trigger AML flags without being involved in rug pulls – a legitimate DeFi user who passed through a mixer for privacy reasons, for instance.

Running both checks provides the most comprehensive protection. For the complete technical architecture of AML and KYT compliance for DeFi, our guide to blockchain compliance, KYT, and AML for DeFi in 2026 covers the regulatory obligations and implementation options for protocols of all sizes. Additionally, for protocols evaluating predictive AI versus traditional rule-based approaches for compliance, our analysis of predictive AI for crypto KYC, AML, and transaction monitoring provides a direct comparison.

PancakeSwap V2 Specifically: Why BNB Chain Is Ground Zero for Rug Pulls

PancakeSwap V2 on BNB Chain represents an ideal operational environment for rug pull schemes. Understanding why helps investors calibrate their risk exposure appropriately when interacting with BNB Chain DeFi specifically.

BNB Chain gas fees are among the lowest of any major EVM-compatible chain. Deploying a token contract, creating a PancakeSwap V2 pool, and seeding initial liquidity can cost less than $5 in total gas fees. This near-zero barrier to entry means that fraud operators can deploy hundreds of rug pull setups simultaneously at negligible marginal cost. A single successful extraction – even of a few thousand dollars – covers the cost of dozens of attempted schemes.

PancakeSwap V2 also benefits from extremely high organic retail traffic. Hundreds of thousands of retail investors use PancakeSwap V2 daily, creating a large pool of potential victims for each fraudulent token. The PancakeSwap interface itself presents tokens without any fraud risk warnings – it is a neutral trading interface, not a security layer. The responsibility for fraud detection falls entirely on the investor.

Token aggregators like DexTools and DexScreener surface newly created pools with real-time price charts, trading volume, and holder counts – all of which can be manipulated through bot trading and wash volume. A fresh pool with manufactured trading activity looks identical to a legitimate new project on these interfaces. Retail investors using aggregators as their primary research tool are working with data that fraud operators have specifically optimized to deceive.

CertiK’s 2025 security data placed BNB Chain consistently among the top chains for exit scam and rug pull activity, a position it has held across multiple annual reporting periods. The combination of low costs, high traffic, and minimal native protection makes BNB Chain the most active venue for the rug pull industry globally.

This is not a criticism of BNB Chain or PancakeSwap as infrastructure. Both are legitimate, high-quality platforms. The fraud problem is an emergent consequence of their success – high retail traffic and low costs that serve legitimate users equally serve fraudulent ones.

What the Rug Pull Industry Looks Like at Scale

$569M across 20 weeks. 103,695 individual events. These numbers only make sense at the scale of an organized industry, not at the scale of individual bad actors. Consider the operational requirements to produce this output.

At an average of 5,185 rug pull events per week, operators must be deploying thousands of token contracts weekly. Each contract requires wallet funding, deployment, pool creation, liquidity seeding, marketing (to attract retail buyers), and eventually liquidity removal. The automation required to manage this volume is sophisticated – these are not manual operations but scripted, bot-driven pipelines that handle the entire lifecycle with minimal human intervention.

The diversity of fraud values also tells a story. The same week that produced the maximum fraud value ($53.4M in W4) contained individual rug pulls ranging from a few hundred dollars to millions. This range reflects an ecosystem with multiple tiers: small-scale operators running low-value schemes alongside professional operations running high-value targeted campaigns. The industry has a hierarchy, with the most sophisticated operators at the top extracting the most per event while the smallest operators run volume plays at low margins.

Understanding rug pulls as an industry – not as isolated frauds – changes how we think about protection. Blacklists of known bad actors are largely ineffective against industrial-scale operations that create fresh wallets continuously. Reactive forensics – identifying fraud after it happens – provide no protection to the retail investors who lost money. Only predictive, behavioral approaches that identify fraud operators before they extract value offer meaningful protection at this scale.

This is the core thesis behind ChainAware’s approach. For context on how predictive AI compares to forensic analytics in practical terms, and why the shift from reactive to predictive is the fundamental security challenge facing Web3 in 2026, our comparison of forensic versus AI-powered blockchain analysis provides the complete framework.

The Role of Token Holder Quality in Identifying Rug Pull Risk

One additional signal that Rug Pull Detector V3 incorporates – particularly for tokens that have already launched and have a holder base – is token holder quality analysis. ChainAware’s Token Rank system assigns every token a rank based on the median Wallet Rank of its holders. Legitimate tokens with real communities tend to attract wallets with diverse, experienced behavioral profiles. Rug pull setups, which attract retail speculators and bots, produce distinctive holder quality signatures.

A token where the holder base consists predominantly of fresh wallets with no prior DeFi history, created in the days immediately preceding the token launch, is a warning sign that the holder base was manufactured rather than organically acquired. Bot-driven trading activity similarly produces holder clusters with synchronized creation dates and homogeneous transaction histories – patterns that stand out clearly in a holder quality analysis.

For investors conducting due diligence on tokens with existing holder bases, ChainAware’s Token Rank guide covers the complete methodology for assessing holder quality as part of investment due diligence. Combined with Rug Pull Detector V3’s contract and creator analysis, holder quality analysis provides a third independent signal layer – and convergent warnings across all three layers are among the strongest indicators of fraudulent intent available in the DeFi ecosystem today. This deployer- and holder-level scoring is the same methodology behind ChainAware’s specialised ready-made agents, which combine token risk with holder and deployer screening in a single call.

P2P Transactions: The Rug Pull Risk Outside DEX Pools

The $569M we measured operates entirely within the DEX pool context – tokens and liquidity pools on PancakeSwap V2. However, rug pull risk also exists in peer-to-peer payment contexts that do not involve DEX pools at all.

Approximately 50% of on-chain transaction volume consists of direct peer-to-peer transfers – one wallet sending assets directly to another, with no DApp interface in the flow. This volume includes legitimate payments, OTC trades, escrow arrangements, and investment contributions to projects that have not yet launched on a DEX. It also includes fraud: investors sending funds to project team wallets that subsequently disappear, or OTC trades where the receiving party does not fulfill their side of the arrangement.

For the 50% of transactions that happen wallet-to-wallet, the protection question is not “is this pool safe?” but “is this wallet safe?” ChainAware’s free Wallet Auditor addresses exactly this use case – providing a complete behavioral profile and fraud risk assessment for any wallet address before you send irreversible on-chain funds to it. The Wallet Auditor complete guide walks through every parameter the audit covers and how to interpret the results for P2P transaction due diligence.

What Comes Next: ChainAware’s Rug Pull Research Roadmap

This dataset – 103,695 rug pulls and $569M in confirmed extraction – is the foundation of an ongoing research program. Publishing this data is the beginning, not the conclusion.

The next phases of ChainAware’s rug pull research and product development address several open questions that this initial dataset raises but does not answer.

Expanding to Complex Rug Pull Detection

As noted throughout this article, the $569M figure covers only basic liquidity extraction. Adding LP token transfer rug pulls, unlocked token sell-offs, and associated wallet extraction to the detection methodology will require additional algorithmic development – but the training data now exists in our confirmed dataset. V3.1 and subsequent versions will incrementally expand coverage to these more complex patterns.

Multi-Chain Expansion

This dataset covers PancakeSwap V2 on BNB Chain. ChainAware already operates across 8 blockchains – ETH, BNB, BASE, POLYGON, SOL, TON, TRON, and HAQQ. Expanding the rug pull dataset to include Ethereum DEXes (Uniswap V2/V3), Solana meme token launchpads, and BASE chain activity will produce a comprehensive multi-chain picture of rug pull extraction that does not currently exist anywhere in the industry.

Solana’s pump.fun – where an estimated 99% of tokens launched end in loss events for buyers – is a particularly important next target for this analysis framework. The structural characteristics of pump.fun token launches share significant overlap with PancakeSwap V2 rug pulls, but the technical analysis requires adaptation to Solana’s account-based architecture and SPL token standard.

Continuing Algorithm Improvement

The 90.1% accuracy of V3 is a milestone, not a ceiling. The algorithm team continues iterating on both the behavioral and smart contract analysis pipelines. Each confirmed rug pull event from our ongoing monitoring adds to the training dataset, and each fraud industry adaptation that our current model misses provides labeled false negative examples for the next training cycle.

The target is 95%+ prediction accuracy. Reaching that threshold requires solving the core challenge of sophisticated operator evasion – fraud operators who invest significantly in maintaining clean behavioral profiles and deploying contracts that pass automated inspection. ChainAware’s research team is actively working on graph-based analysis of funding networks and deployment clustering to address this remaining gap.

Rug Pull Red Flags: What to Look for Before Investing in Any New Token

While automated tools like V3 do the heavy lifting, investors who understand the underlying red flags make better decisions – both by interpreting V3’s output intelligently and by performing manual checks that complement automated analysis. The following red flags are drawn directly from our analysis of the 103,695 rug pull events in the dataset and from ChainAware’s behavioral research across 20M+ wallet profiles.

Red Flag 1: Unrenounced Contract Ownership

Contract ownership allows the deployer to call privileged functions – modifying transfer fees, adding transfer restrictions, minting additional supply, and pausing trading. A legitimate project with long-term intentions almost always renounces ownership or transfers it to a time-locked multisig. A contract where the deployer retains full ownership indefinitely is structurally set up for the owner to modify conditions after retail investors have bought in.

Checking ownership renouncement takes 30 seconds on BscScan or Etherscan – search the contract address, look for the “Contract” tab, and check whether owner() returns the zero address (renounced) or an active wallet address (retained). This single check eliminates a significant fraction of the most blatant rug pull setups.

Red Flag 2: Unlocked Liquidity Pool Tokens

When a creator seeds liquidity into a PancakeSwap V2 pool, they receive LP (Liquidity Provider) tokens representing their share of the pool. Legitimate projects lock these LP tokens in a time-lock contract – preventing liquidity removal for a defined period (commonly 6-24 months). Unlocked LP tokens held in the deployer’s wallet can be redeemed for the underlying liquidity at any moment, making rug pull execution a one-transaction event.

LP lock verification can be performed manually on platforms like Mudra or Team Finance, which maintain records of locked LP tokens on BNB Chain. The absence of a lock is not automatically fraudulent – some legitimate projects rely on other mechanisms – but it is a significant red flag that requires additional due diligence before committing capital. V3’s smart contract analysis flags unlocked LP positions automatically.

Red Flag 3: Fresh Deployer Wallet with No History

Professional rug pull operators often maintain separate deployer wallets for each scheme – fresh addresses with no prior on-chain history. This is a deliberate evasion tactic: behavioral analysis tools that look at deployer history return a clean result, because there is no history to analyze. V3 handles this by examining the funding source of the deployer wallet (where the ETH or BNB originated from), not just the deployer wallet itself. However, a fresh deployer wallet with no history should still independently raise an investor’s suspicion level, particularly when combined with other red flags.

Red Flag 4: Suspiciously High Maximum Transaction or Wallet Limits

Many rug pull contracts include maximum transaction size or maximum wallet balance limits that restrict how much any individual buyer can purchase. These limits are presented as anti-whale measures – preventing any single buyer from acquiring too large a share. In practice, they often serve a different purpose: ensuring that no single buyer has enough exposure to justify the gas cost of legal action, and ensuring that the token requires extended time to accumulate retail capital before the rug can be executed. Combined with gradual price increases driven by bot activity, these limits produce a slow accumulation of widely distributed retail capital that is then extracted in a single pull event.

Red Flag 5: Concentrated Token Allocation

Token distribution matters enormously. If the top 10 wallets hold more than 50% of the total supply, and those wallets are connected to the deployer or hold unlocked tokens, they represent a latent supply overhang that can be sold into any retail buying pressure. ChainAware’s Token Rank analysis reveals the quality and concentration of holder distribution for any token with an existing holder base – a concentrated, low-quality holder base is one of the strongest predictors of eventual rug pull or dump events. The complete methodology is explained in the Token Rank guide.

Red Flag 6: Anonymous Team With No Verifiable Presence

Full anonymity is the norm in crypto, not the exception – many legitimate projects have anonymous teams. However, the combination of full anonymity with the absence of any verifiable prior project history, a project launched in the previous week with no audit, and an aggressive social media presence focused entirely on price expectations is a signature pattern of rug pull operations. Legitimate anonymous projects typically have verifiable GitHub commit histories, prior community involvement in legitimate projects, and at minimum a third-party security audit. The absence of all three simultaneously is a meaningful red flag.

Red Flag 7: Extremely High Early Price Performance

A token that gains 500%-2,000% in its first 24-48 hours of trading is an appealing investment narrative. It is also the characteristic signature of a pump phase that precedes a rug pull. Organic price discovery – driven by genuine retail demand for a real product – produces growth curves that are steep but not parabolic. Parabolic early gains are typically produced by bot-driven wash trading that manufactures artificial volume and price momentum to attract FOMO-driven retail buyers. The parabolic chart is the marketing material for the rug. When the price is high enough to make liquidity removal maximally profitable, the rug executes.

How Rug Pull Operators Evade Detection: An Adversarial Analysis

Understanding how sophisticated fraud operators attempt to evade detection is as important as understanding the detection methods themselves. The 9.9% of rug pull events that V3 does not catch are concentrated in operations that specifically invest in evasion. Publishing this analysis serves retail investors by explaining what the highest-risk events look like, and serves the research community by documenting the evasion tactics that future algorithm versions must address.

Wallet Aging and History Manufacturing

The most sophisticated operators maintain aged deployer wallets – addresses that have months or years of legitimate-looking transaction history before being used for fraud. These wallets interact with legitimate DeFi protocols, make small token purchases across diverse projects, and accumulate behavioral signals that behavioral analysis models associate with legitimate users. When such a wallet eventually deploys a fraudulent contract, the behavioral layer returns a low-risk signal. Only the smart contract analysis layer can catch fraud from these operators – and only if the contract itself contains detectable risk patterns.

Countering this tactic requires tracking the clustering of aged wallets that share funding sources or that appear in coordinated deployment patterns – even when each individual wallet’s history appears clean. This graph-based analysis is part of ChainAware’s ongoing algorithm development roadmap.

Contract Code Obfuscation

Professional fraud operators increasingly deploy contracts with obfuscated code – transfer restrictions hidden within complex modifier chains, mint functions embedded in proxy contract architectures, and ownership retention disguised as multi-sig governance mechanisms that are controlled entirely by the deployer. These obfuscation patterns specifically target the limitations of automated smart contract analysis tools.

V3’s bytecode inspection – which operates on compiled contract code rather than requiring readable source code – provides partial protection against source code obfuscation. However, novel obfuscation patterns that have not yet appeared in the training dataset represent genuine blind spots. Regular model updates and the continuous addition of newly confirmed rug pull events to the training dataset are the primary mechanism for closing these gaps as they are discovered.

Delayed Execution

Some operators run long-duration schemes – maintaining active development activity, social media presence, and liquidity for weeks or months before executing the rug pull. These delayed execution schemes are specifically designed to outlast the attention spans of initial investors who may have checked V3 at launch and seen a clean result. By the time the rug executes, many initial investors have already forgotten the risk assessment they saw weeks ago and have added to their position based on the apparent ongoing legitimacy of the project.

Protecting against delayed execution requires ongoing monitoring rather than a single point-in-time check. ChainAware’s Transaction Monitoring Agent, which continuously rescreens connecting wallets on every DApp visit, provides this ongoing monitoring layer for DApps. For individual retail investors, re-running V3 checks periodically on held positions – particularly when planning to add to an existing position – is the equivalent individual protection behavior.

Legitimate-Looking Audits

A concerning development in the rug pull industry is the emergence of fraudulent security audits – certificates from obscure or non-existent “audit firms” that create the appearance of third-party verification without the substance. A token that displays an “audited” badge from an unrecognizable firm – or from a legitimate-sounding name that does not correspond to any established security company – provides no real protection against rug pull risk.

Legitimate audits from established firms like CertiK, Trail of Bits, Consensys Diligence, or OpenZeppelin are verifiable on the auditing firm’s own website and are associated with published audit reports. An audit badge that cannot be verified against the auditing firm’s own published report list should be treated as fabricated. V3’s smart contract analysis provides an independent assessment that does not rely on audit claims – it examines the code directly, regardless of what audit certificate the project displays.

The Market Infrastructure Gap: Why DeFi Needs Better Fraud Data

The $569M figure in this report exists not just as a fraud statistic but as a market infrastructure data point. DeFi cannot mature into a mainstream financial system while retail investors lose hundreds of millions of dollars per quarter to a fraud mechanism that could be largely prevented with better tooling and better data.

Traditional financial markets have infrastructure specifically designed to protect retail investors from comparable fraud: prospectus requirements, securities registration, market maker oversight, exchange listing standards, and regulatory enforcement. These mechanisms are not perfect – traditional markets have their own fraud problems – but they represent decades of accumulated institutional knowledge about how to structure market access to minimize retail harm.

DeFi has none of this infrastructure by design – permissionless access is a core feature, not a bug. The trade-off is that permissionless access enables both the extraordinary innovation of the DeFi ecosystem and the extraordinary fraud of the rug pull industry. Building protective infrastructure that preserves permissionlessness while reducing retail harm requires data-driven tools that operate at the protocol layer – not regulatory gatekeeping that would undermine DeFi’s fundamental architecture.

ChainAware’s approach – behavioral AI that flags risk without blocking access, tools that inform investor decisions without requiring permission from any central authority – represents one model for how this protective infrastructure can be built. Publishing the $569M dataset is part of the broader argument that this infrastructure is urgently needed and that the data to build it exists.

For DApp teams specifically, building fraud resistance into their platform architecture is becoming a competitive differentiator. Users who have been burned by rug pulls – and statistically, a significant fraction of active DeFi users have been – actively seek platforms with visible security measures. A DApp that visibly screens connecting wallets, displays behavioral security ratings for tokens listed on its interface, and provides transparent fraud risk data builds trust with the retail user base that DeFi needs to grow beyond its current audience.

The DeFi onboarding problem – why 90% of connected wallets never transact – is partly a product problem, partly a UX problem, and significantly a trust problem. Users who don’t trust that their capital is safe don’t commit capital. Solving fraud at the infrastructure level directly addresses the trust component of the conversion problem. For the complete analysis of why the 90% non-transacting wallet problem is DeFi’s most critical growth challenge, and how behavioral intelligence addresses it, see our comprehensive guide on DeFi onboarding and why connected wallets don’t transact.

Building that trust requires two things simultaneously: making fraud harder to execute and making fraud more visible when it does execute. V3 contributes to making fraud harder to execute – at 90.1% prediction accuracy, it prevents a significant fraction of rug pull investments before they happen. Publishing this dataset contributes to making fraud more visible – giving the industry, regulators, and researchers the empirical foundation to understand and respond to the true scale of the problem.

Together, these two contributions represent ChainAware’s core thesis: that Web3 grows faster and serves users better when behavioral intelligence is embedded into the infrastructure of every DApp interaction. The $569M figure is not just a warning – it is the business case for why predictive fraud detection is one of the most important infrastructure investments any Web3 platform can make in 2026. For the complete framework of how behavioral intelligence powers Web3 growth beyond security, the Web3 agentic economy guide covers the full stack from fraud prevention through personalized growth. The full 19-category forensic methodology behind the Fraud Detector is documented on the Fraud Detector learn page.

Frequently Asked Questions

What exactly is a rug pull?

In the context of this dataset, a rug pull is defined as a liquidity event where the contract creator of a token pool removes more liquidity than they originally added. A creator deposits funds (Mint event), retail investors buy the token and add value to the pool, and then the creator withdraws all liquidity (Burn event). The difference between what they removed and what they added – when removal exceeds addition – is the rug pull value. The token price drops to zero instantly upon liquidity removal, leaving holders with worthless assets. For a full comparison of rug pulls versus pump-and-dump schemes, see our dedicated guide on rug pull vs pump and dump.

How does ChainAware Rug Pull Detector V3 work?

V3 runs two parallel analysis pipelines. The first examines the behavioral history of the contract creator’s wallet – their deployment history, funding sources, prior rug pull associations, and wallet age. The second inspects the smart contract code itself for dangerous patterns: hidden transfer restrictions, uncapped mint functions, unrenounced ownership, and unlocked LP tokens. Both pipelines produce independent risk scores that are combined through an ensemble model trained on 103,695+ confirmed rug pull events. The combined model achieves 90.1% prediction accuracy on the held-out test set.

Is the Rug Pull Detector free?

Yes. The web interface at chainaware.ai/rugpull is completely free for retail investors, with no account creation required. Businesses that need API access for automated, high-volume token screening can subscribe to the API at chainaware.ai/subscribe. AI agents can access the tool through the X402 micropayment protocol, paying per analysis without requiring a subscription.

What is the difference between V2 and V3?

V2 relied exclusively on behavioral analysis of contract creator wallets, achieving approximately 68% prediction accuracy. V3 adds a full smart contract analysis layer – inspecting contract code for dangerous patterns regardless of the deployer wallet’s history. This combination closes the gap that sophisticated fraud operators exploited in V2 by maintaining clean deployer wallet histories. The combined V3 model achieves 90.1% prediction accuracy – a 32.5% relative improvement over V2.

Does V3 work on tokens that have not yet launched?

Yes. V3 can analyze any deployed smart contract – including contracts that have been deployed but have not yet attracted liquidity. Smart contract analysis runs regardless of whether there is an active trading pool. Creator behavioral analysis also runs at any point after contract deployment. For pre-launch tokens, smart contract analysis is particularly valuable because it does not require any trading history to return a risk assessment.

Can a rug pull still happen even if V3 gives a low risk score?

Yes. V3 achieves 90.1% accuracy, meaning approximately 9.9% of rug pull events will not be flagged. These false negatives are concentrated in the most sophisticated fraud operations – those that invest in maintaining clean deployer profiles and deploy contracts that pass automated inspection. No automated tool can guarantee 100% detection. V3 should be used as a risk filter, not as a guarantee, and combined with human judgment on tokenomics, team identity, and project fundamentals for high-value investment decisions.

Why does $569M in losses generate almost no media coverage?

Rug pull losses are distributed across hundreds of thousands of small transactions affecting individual retail investors, rather than concentrated in a single event affecting an institution. Distributed losses are structurally invisible to media coverage, which requires a single dramatic event with a named victim and a named perpetrator. The victims of rug pulls are anonymous retail investors with no media presence. The perpetrators are anonymous wallet operators with no attributable identity. The structural invisibility of rug pull losses is one of the primary reasons this form of fraud operates at scale without triggering the industry-wide response that dramatic exchange hacks generate.

What chains does ChainAware support for rug pull detection?

ChainAware currently supports 8 blockchains across its full product suite: Ethereum (ETH), BNB Smart Chain (BNB), Base (BASE), Polygon (POLYGON), Solana (SOL), TON, TRON, and HAQQ. Rug Pull Detector V3 is optimized for BNB Chain and Ethereum in its current version, with Solana and Base coverage expanding in upcoming releases. For the complete multi-chain capability overview, ChainAware’s complete product guide covers which tools are available on which chains.

How can AI agents use Rug Pull Detector V3?

AI agents access Rug Pull Detector V3 through ChainAware’s X402 micropayment protocol – paying per analysis in real time without requiring pre-approved API keys. Agents can also access the full suite of ChainAware tools through the Model Context Protocol (MCP) server at prediction.mcp.chainaware.ai/sse. The complete integration guide for AI agents is available at our Prediction MCP guide, and the GitHub repository at github.com/ChainAware/behavioral-prediction-mcp provides open-source agent definitions that any AI framework can deploy directly.

The post $569M+ in Rug Pulls on PancakeSwap V2 in 20 Weeks – Rug Pull Detector V3 Launched With 90.1% Accuracy first appeared on ChainAware.ai.

Web3 lost $4 billion to fraud and hacks in 2025. Remarkably, 57.8% of those losses came not from smart contract vulnerabilities but from the wallets and systems operating around the code. Consequently, every DeFi founder eventually searches for the same thing: a fraud detection tool that actually works for their DApp. However, most of what they find was built for someone else entirely.

Chainalysis, Elliptic, TRM Labs, Hypernative, and GoPlus are all serious platforms. Nevertheless, each one was architecturally designed for wallet providers and centralized exchanges – not for DApps. Furthermore, DApps face a completely different threat model that demands a completely different solution. This guide explains that distinction, maps the full competitive landscape, and shows precisely why behavioral wallet screening at connection is the correct approach for DApps in 2026.

In This Guide

• The Two-Segment Split That Most Analyses Miss
• Segment 1 – Wallet Providers and CEXs: Why Simulation Is Essential
• Segment 2 – DApps: Why Simulation Is the Wrong Answer
• The Major Providers – Who Serves Which Segment
• ChainAware – Purpose-Built for DApps
• P2P Payments – The Other 50% of On-Chain Volume
• MiCA Compliance for DeFi in 2026
• Complete Provider Comparison – DApp Lens
• Frequently Asked Questions

The Two-Segment Split That Most Analyses Miss

Before evaluating any fraud detection tool, DApp teams must first answer one question: which customer was this tool actually built for? Every provider solves a real problem. The critical issue is that those problems belong to structurally different customers facing structurally different threats.

The split comes down to a single architectural fact. Wallet providers and CEXs interact with arbitrary external smart contracts written by unknown third parties. DApps interact exclusively with their own contracts – contracts they wrote, audited, and trust completely. That one difference changes everything about which fraud detection approach is technically correct. For a broader view of how wallet behavioral intelligence sits within the full Web3 security stack, see our Web3 Trust Verification Systems guide.

Segment 1 – Wallet Providers and CEXs: Why Simulation Is Essential

Wallet providers – MetaMask, Coinbase Wallet, Phantom, Trust Wallet – face a threat that DApps simply do not encounter. Every user transaction could involve an arbitrary external smart contract that the wallet has never seen before. That contract might be a drain contract, a phishing approval, a honeypot, or a malicious NFT mint designed to steal assets the moment the user signs.

Transaction simulation is therefore essential in this segment. Before a user signs anything, the wallet must simulate what the transaction actually does – which tokens move, which approvals are granted to third parties, and which external contracts get called recursively. Without simulation, the user has no way to know what they are agreeing to. The threat lives inside the contract code itself. For the definitive breakdown of how crypto AML differs from transaction monitoring at the structural level, see our Crypto AML vs Transaction Monitoring guide.

CEXs and crypto banks face a related but distinct version of this problem. They process high volumes of transactions spanning diverse token types, cross-chain flows, and mixing services. Their compliance obligation is regulatory: they must demonstrate to authorities that they screen for sanctions exposure, money laundering, and illicit fund flows. This drives demand for forensic fund-flow tools. Chainalysis Reactor, Elliptic’s Holistic Screening, and TRM Labs’ Forensics platform all serve this specific need.

Importantly, this segment is already well-served. Multiple mature providers compete on chain coverage, threat type breadth, and API latency. The transaction simulation problem has Hypernative, GoPlus, and Pocket Universe. The forensic fund-flow problem has Chainalysis, Elliptic, and TRM Labs. These are serious, well-funded platforms with deep expertise in their specific domain. However, none of them was built for DApps.

Segment 2 – DApps: Why Simulation Is the Wrong Answer

DApps face a completely different problem – and almost every fraud detection vendor has not been designed for it. Uniswap’s team wrote the Uniswap contract. Aave’s team wrote the Aave contract. Therefore, simulating “what will this contract do?” answers a question DApp teams have already answered themselves during development and auditing.

The only unknown variable for a DApp is the wallet connecting to it. The threat model shifts entirely:

Wallet connects to your DApp
        ↓
Is this wallet trustworthy and high-quality?
        ↓
Bad wallet  → ban immediately - before any transaction starts
Good wallet → allow + personalize the experience
Unknown     → flag + monitor on every return visit

The logic that follows is precise and important. If you already know a wallet is fraudulent, AML-flagged, sanctioned, or Sybil – then simulating its transaction on your own smart contract tells you nothing useful. Your contract executes exactly as designed. Simulation is a downstream catch. Wallet behavioral scoring at connection is upstream prevention. Upstream always wins in DeFi because blockchain transactions are irreversible: by the time a transaction is being simulated, the damage window is already open.

Moreover, selling a DApp on transaction simulation means selling them a solution to a problem they do not have. Their smart contract is trusted – they audited it. Their concern is entirely the wallets connecting to it. This fundamental mismatch explains why the most prominent fraud detection providers, despite their genuine capabilities, are structurally misaligned with the DApp use case. For a full comparison of how DeFi compliance tools stack up for DApp-specific needs, see our DeFi Compliance Tools Comparison.

The Major Providers – Who Serves Which Segment

Understanding which segment each provider actually serves cuts through the marketing noise quickly. Most providers claim broad applicability. However, examining their core architecture reveals their true target customer immediately.

Chainalysis – Law Enforcement and Enterprise VASPs

Chainalysis is the dominant blockchain intelligence platform, trusted by 1,500+ institutions including the FBI, IRS, and DOJ. It has helped freeze and recover $34B+ in stolen funds. Core products include Reactor (forensic visual fund flow mapping), KYT (Know Your Transaction – AML monitoring), and Alterya (AI-powered fraud prevention connecting crypto and fiat fraud signals for exchanges and payment processors). According to Chainalysis’s platform documentation , the firm recently added AI natural language agents to its investigation workflow.

Chainalysis’s USP is forensic depth and government credibility – the most court-admissible blockchain evidence available. Critically, however, pricing runs $100,000-$500,000 per year with 3-6 month procurement cycles. A DeFi protocol has no compliance team and no procurement budget at that scale. For a detailed analysis of MiCA-grade compliance at DeFi-native pricing, see our MiCA Compliance for DeFi at 1% of the Cost guide.

Elliptic – Cross-Chain AML at Scale

Elliptic processes 300M+ screenings per quarter, covers 1,100+ blockchain networks and 1,130+ cross-chain bridges, and maintains 2 billion labeled addresses. Its Holistic Screening product treats all blockchains as interconnected – addressing sophisticated chain-hopping and multi-chain laundering. Clients include Coinbase, Revolut, and Santander. According to Elliptic’s compliance platform , the firm focuses specifically on high-volume regulated-finance compliance. Like Chainalysis, it targets institutional compliance teams rather than DApp-native integration.

TRM Labs – Developer-First Blockchain Intelligence

TRM Labs distinguishes itself with sub-second API latency and a developer-first architecture for high-volume real-time screening. Products include TRM Forensics, TRM Transaction Monitoring, and TRM Veriscope (Travel Rule compliance). Notably, TRM partnered with Hypernative in April 2026 to embed its risk intelligence into Hypernative’s pre-transaction enforcement engine – creating a combined solution for wallet providers and exchanges. TRM’s USP is integration speed and latency for consumer-facing apps. Nevertheless, like the other incumbents, it targets VASPs and exchanges requiring regulatory compliance stacks rather than DApps screening individual connecting wallets.

Hypernative – Real-Time Protocol Security

Hypernative raised $65M in its Series B in June 2025 and protects 75+ blockchains by monitoring 300+ threat types. Its Transaction Guard simulates and evaluates every transaction before execution, detecting 98% of hacks more than 2 minutes before the first transaction. According to Hypernative’s platform documentation , the firm’s core value is stopping exploits before they execute – specifically for protocols facing active exploit risk in their own code, governance attacks, and bridge vulnerabilities. Transaction Guard is designed for protocols monitoring external contract interactions and their own code integrity, not for screening individual connecting wallets at sub-100ms latency.

GoPlus Security – Decentralized Token Security at Scale

GoPlus Security averaged 717 million monthly API calls in 2025. Its Token Security API, Transaction Simulation API, and DeepScan (AI smart contract analysis covering Solidity, Move, and Rust) make it the highest-volume decentralized security infrastructure in Web3. AgentGuard protects 200+ AI agents with real-time on-chain security. According to GoPlus Security’s infrastructure overview , the platform focuses on token-centric and contract-level security. This design is ideal for wallets and users interacting with unknown tokens – but it is not designed for DApps screening their own users’ wallet behavioral history at connection.

ChainAware – Purpose-Built for DApps

ChainAware is the only fraud detection platform designed specifically for DApps. Every architectural decision flows from a single insight: a DApp trusts its own contract. Therefore, the entire threat surface is the connecting wallet – and the correct response to a bad wallet is to ban it before it ever initiates a transaction.

Transaction Monitoring via Google Tag Manager

ChainAware’s Transaction Monitoring deploys via a single Google Tag Manager pixel – no code changes to the DApp required and active within 12 minutes (see the Transaction Monitoring setup guide). This zero-code integration is structurally correct for DApps for a precise reason: screening happens at wallet connection, before any transaction begins. Additionally, it covers two distinct wallet populations simultaneously:

• New wallets – scored at first connection, before any interaction with the protocol begins
• Returning wallets – automatically re-screened on every subsequent visit, catching wallets whose risk profile changes after initial onboarding

When a bad event occurs – a fraud-flagged wallet connects, a sanctioned address appears, an AML-risk wallet returns – the DApp admin receives an immediate Telegram alert. Furthermore, webhook automation fires a programmatic response: shadow ban, block, redirect, or any custom action, without any human in the loop. This is precisely the pre-transaction enforcement capability that TRM and Hypernative just partnered to build together in April 2026 for exchanges. ChainAware already delivers it for DApps as a zero-code pay-per-use integration. For the complete integration walkthrough, see our Transaction Monitoring Agent guide and our AML and Transaction Monitoring for DApps guide.

Predictive Fraud Detection – 98% Accuracy, 19 Forensic Categories

The core intelligence layer is ChainAware’s predictive_fraud model – 98% accuracy trained on behavioral patterns that precede fraud, not just confirmed bad-address databases. This distinction matters enormously for DApps. A wallet with no prior fraud record but behavioral patterns matching pre-fraud activity gets flagged. Chainalysis, Elliptic, and TRM would give it a clean score because they screen against known-bad address lists – backward-looking, not predictive.

The 19 forensic categories cover the full DeFi-specific fraud spectrum beyond simple AML: cybercrime, money laundering, darkweb transactions, phishing activities, fake KYC, mixer interactions, sanctioned addresses, stealing attacks, honeypot associations, gas abuse, financial crime, reinit exploits, blackmail activities, malicious mining, fake tokens, fake standard interfaces, blacklist associations, and more. Consequently, DApps get operational fraud prevention coverage that legacy compliance tools were never designed to provide. For the complete technical methodology, see our Predictive AI for KYC, AML and Transaction Monitoring guide.

Two Open-Source Agents for the AI Pipeline Layer

Beyond the GTM integration, ChainAware publishes two open-source agents that add a complete AI pipeline layer – deployable via git clone and API key, with no custom engineering required.

chainaware-transaction-monitor – Real-time transaction risk scoring for autonomous agent workflows. Produces a composite score (0-100) and a pipeline action (ALLOW / FLAG / HOLD / BLOCK) for every transaction before execution. Designed specifically for agentic DeFi protocols where no human is in the approval loop and decisions must happen at machine speed.

chainaware-compliance-screener (see Security & Fraud Agents) – Runs four specialist sub-agents in sequence: fraud detector, AML scorer, sanctions screener, and transaction risk scorer. Together, they provide full compliance pipeline coverage for batch pre-screening of waitlists, token launch registrations, airdrop eligibility lists, and backend compliance workflows. Both agents integrate natively with Claude, GPT, and any MCP-compatible LLM. For how these agents fit the broader agentic DeFi economy, see our Web3 Agentic Economy guide and our 12 Blockchain Capabilities Any AI Agent Can Use.

Behavioral Analytics and Growth Layer

Beyond fraud prevention, ChainAware adds a dimension that no security provider in this market offers: a growth intelligence layer built on the same behavioral data. The predictive_behaviour tool delivers 22-dimension Web3 Personas including 12 forward-looking intention probabilities (Prob_Lend, Prob_Trade, Prob_Stake, Prob_Borrow, Prob_Yield_Farm, and more), experience level (1-5), risk profile, and protocol engagement history.

Consequently, the same GTM pixel that screens for fraud also identifies high-value wallets, predicts what each user will do next, and enables personalized DApp onboarding in under 100ms. This combination drives 8x engagement and 2x conversions in production at SmartCredit.io – turning security infrastructure into revenue infrastructure simultaneously. For the complete behavioral analytics methodology, see our Web3 Wallet Auditing Providers guide.

P2P Payments – The Other 50% of On-Chain Volume

Most fraud detection discussions focus entirely on protocol transactions – wallets interacting with DApp smart contracts. However, on-chain transactions split into two roughly equal categories, and the second one is almost entirely ignored.

Protocol transactions account for approximately 50% of on-chain volume. A swap on Uniswap, a lend on Aave, a token purchase on a launchpad – all of these flow through a DApp interface where the fraud monitoring layer can be deployed. ChainAware’s Transaction Monitoring covers this category directly via the GTM integration.

P2P payments account for the other approximately 50%. These involve a user sending funds directly from one wallet to another – no smart contract, no DApp interface, and no existing fraud screening in the flow. The user is about to send irreversible funds to an address they may not fully know. This is exactly the scenario where wallet validation is most critical and most often skipped.

Before any P2P payment, the sending user needs answers to five questions:

• Is the receiving wallet associated with known fraud? (98% accuracy predictive score – learn about rug pull prevention)
• Does it carry AML or OFAC sanctions exposure?
• Has it interacted with mixing services or darkweb-linked addresses?
• Is it a brand-new wallet with no history – itself an elevated-risk signal?
• Has it been involved in phishing, blackmail, or stealing attacks?

ChainAware’s free Wallet Auditor and Fraud Detector solve precisely this use case – instantly, at no cost, with no account required. A user pastes any receiving address and gets the complete behavioral fraud profile before sending a single token. This P2P validation layer addresses half of all on-chain transaction volume that DApp monitoring structurally cannot reach, because there is no DApp in the flow to deploy it. For a complete walkthrough of the wallet auditing ecosystem, see our Web3 Wallet Auditing Providers guide.

MiCA Compliance for DeFi in 2026

MiCA’s full EU-wide enforcement arrives in July 2026, creating a hard deadline for DeFi protocols with EU legal entities or front-end operators (see DeFi Compliance use case). Specifically, protocols must demonstrate continuous on-chain monitoring, AML screening, and sanctions compliance. The tools most DeFi teams currently consider – Chainalysis and Elliptic – deliver MiCA-grade compliance for centralized exchanges at $100,000-$500,000 per year.

DeFi protocols need the same compliance coverage at a price and deployment speed that matches their architecture. ChainAware delivers 70-75% MiCA coverage for DeFi protocols via pay-per-use pricing with zero annual contract – at approximately 1% of the cost of enterprise compliance tools. MiCA alignment covers: AML obligations (FATF Recommendations 10 and 16), sanctions and OFAC screening (MiCA Article 83), predictive fraud detection with timestamped audit records, and continuous transaction monitoring for returning wallets. For the full MiCA compliance analysis for DeFi protocols, see our MiCA Compliance for DeFi guide and our Blockchain Compliance KYT and AML guide.

Crucially, ChainAware’s GTM integration means compliance executes before transactions happen – not in a downstream review queue. For regulated DeFi, pre-execution compliance is not optional: irreversible blockchain transactions cannot be undone after the fact.

Complete Provider Comparison – DApp Lens

The following table maps each major provider against the dimensions that matter most for DApp teams evaluating fraud detection tools in 2026. For the full product overview, see the ChainAware MiCA Compliance for DeFi Businesses guide.

Dimension	Chainalysis / Elliptic / TRM	Hypernative + GoPlus	ChainAware
Primary customer	CEXs, banks, law enforcement	Wallet providers, exchanges	DApps
Core problem solved	Where did funds come from?	Is this contract dangerous?	Is this wallet trustworthy?
Transaction simulation	For VASP compliance	Core capability	Not needed – DApp trusts own contract
Wallet scoring at connection	Address screening only	Partial address risk	Core capability, sub-100ms
Zero-code DApp integration	Enterprise API	API integration required	GTM pixel, 12 minutes
Returning wallet re-screening	Manual	Manual setup	Automatic on every visit
Telegram alerts + webhooks	Dashboard only	Dashboard / API	Native – automated response
P2P payment validation	Enterprise only		Free Wallet Auditor
MiCA DeFi compliance	For CEXs ($100K-$500K/yr)	Partial	1% of cost, pay-per-use
Behavioral prediction (forward-looking)			Unique – 98% accuracy
Growth / personalization layer			Unique – 8x engagement
AI agent pipeline			chainaware-transaction-monitor + chainaware-compliance-screener
Pricing	$100K-$500K/yr	Enterprise	Pay-per-use, no contract

Frequently Asked Questions

Why can’t a DApp use Chainalysis or Elliptic?

Chainalysis and Elliptic are excellent tools for their intended customers – centralized exchanges, banks, and law enforcement agencies with compliance teams and annual budgets of $100,000-$500,000. DApps typically have neither. Additionally, both tools run post-transaction monitoring and forensic investigation – not wallet screening before any transaction occurs. A DApp needs threats screened before the transaction, not analyzed after it settles irreversibly on-chain.

Does a DApp need transaction simulation?

No – and this is the most important distinction in this guide. Simulation reveals what an unknown external contract will do. A DApp already knows what its own contract will do because it wrote and audited the contract. Therefore, simulating a transaction on a DApp’s smart contract provides no new information. The only useful question is whether the connecting wallet is trustworthy. Simulation is right for wallet providers and CEXs. Behavioral wallet scoring is right for DApps.

What is the difference between AML screening and behavioral fraud prediction?

AML screening checks whether a wallet has known associations with illicit activity – sanctions lists, flagged addresses, mixer exposure. It is backward-looking. Behavioral fraud prediction answers a different question: based on this wallet’s complete behavioral history, is it likely to commit fraud in the future? A wallet can pass AML screening with a clean score and still carry a high fraud probability based on behavioral signals that consistently precede fraud. DApps need both layers: AML for regulatory compliance and behavioral prediction for operational fraud prevention. See our Crypto AML vs Transaction Monitoring guide for the full breakdown.

How does ChainAware’s GTM integration work technically?

A single Google Tag Manager pixel deploys to the DApp front end – no changes to the DApp’s codebase required, active within 12 minutes. When any wallet connects, the pixel fires and ChainAware’s predictive_fraud and AML screening scores the wallet in sub-100ms. If a flagged wallet connects, a Telegram alert reaches the admin immediately. Additionally, a webhook fires an automated response – shadow ban, block, redirect – without any human review required. Returning wallets are automatically re-screened on every visit, so a wallet that was clean at first connection but becomes fraudulent later does not slip through undetected. See our ChainAware Complete Product Guide for a full overview of how each capability fits together.

What are the P2P payment risks and how does ChainAware address them?

Approximately 50% of all on-chain transactions are direct wallet-to-wallet P2P payments with no DApp in the flow. These transactions are irreversible – once sent, they cannot be recalled. Before sending funds to any address, users should validate the receiving wallet using ChainAware’s free Wallet Auditor or Fraud Detector. Both tools are instant, require no account, and reveal fraud probability, AML status, mixer history, darkweb exposure, and full forensic detail for any address on 8 blockchains. For context on how wallet auditing works as an ecosystem, see our Web3 Wallet Auditing Providers guide.

Is ChainAware MiCA compliant for DeFi protocols?

ChainAware delivers 70-75% MiCA coverage for pure DeFi protocols operating in the EU – covering AML obligations, sanctions screening, predictive fraud detection, and continuous transaction monitoring with timestamped audit records. Integration runs via GTM pixel at pay-per-use pricing – approximately 1% of the annual cost of Chainalysis or Elliptic. Full enforcement arrives in July 2026. See our Blockchain Compliance KYT and AML guide for complete coverage requirements.

How does ChainAware compare to Hypernative for DeFi protocols?

Hypernative excels at protocol-level exploit prevention – detecting smart contract vulnerabilities, governance attacks, and bridge risks before they execute. Consequently, it is extremely valuable for protocols that face active exploit risk in their own code. ChainAware addresses a completely different layer: the behavioral fraud risk of individual wallets connecting to the protocol. The two tools are complementary for protocols that face both risks simultaneously. However, for most DeFi protocols whose smart contracts are audited and trusted, the primary remaining fraud surface is the wallet population – which ChainAware was specifically designed to address.

External sources: Chainalysis Blockchain Intelligence Platform · Elliptic Holistic Screening · TRM Labs Blockchain Intelligence · Hypernative Real-Time Security Platform · GoPlus Decentralized Security Infrastructure

The post Web3 Fraud Detection for DApps in 2026 – Why Wallet Screening Beats Transaction Simulation first appeared on ChainAware.ai.