Data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3. Week 28 = week ending approximately July 13, 2026.

Week 28 delivered the sharpest single-week acceleration since W23. $74,910,369 was extracted across 8,318 separate rug events – a 16.5% surge from W27’s $64,301,289 and the highest weekly fraud total since W23’s $73,425,127. The W1-W28 cumulative total now stands at $1,167,762,790 – the 2026 running total has crossed $1 billion. That figure covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3, and represents a confirmed floor: it excludes honeypot contracts, LP token transfer rugs, and associated wallet extraction.

Week 28 Key Numbers

Week 28 Analysis

Sharpest Acceleration Since W23

The 16.5% week-on-week increase in extracted fraud brings W28 to $74.9M – the highest single-week figure in five weeks and a decisive break from the $63M-$64M range that characterised W26 and W27. Both extraction value and event count moved in the same direction: rug events rose from 8,144 to 8,318 (+2.1%), while average extraction per event climbed from $7,896 to $9,007 – a 14.1% jump. This combination of more events and higher per-event value is characteristic of the surge cycles visible throughout the 2026 dataset, most notably in the W23 spike and the W43-W45 2025 peaks.

The removed-to-added liquidity ratio ticked up to 1.508x in W28 – creators removed $1.51 for every $1.00 they seeded. This ratio had been compressing slightly through W26-W27 (1.480x and 1.466x respectively). The W28 expansion signals that larger operators with higher extraction efficiency re-entered the market this week alongside the sustained baseline of smaller-scale activity.

Rug Events at Highest Level Since W23

8,318 rug pull events is the highest weekly event count since W23’s 8,225. Event count has now climbed for three consecutive weeks: 7,579 (W26) → 8,144 (W27) → 8,318 (W28). This sustained upward trend in event frequency, combined with the W28 jump in per-event extraction value, produces the largest weekly fraud total since the W23 anomaly.

New pool creation at 13,427 mints is marginally lower than W27’s 13,901 but remains well above the W16-W17 low of approximately 9,800-13,000 mints per week. The rug-to-mint ratio of 61.9% means that for every 10 new pools launched in W28, more than 6 ended in a liquidity extraction event. That ratio has been above 55% for every week since W21.

$1 Billion in Context

The 2026 cumulative total of $1,167,762,790 – based on the restated full-year dataset – places the scale of tracked rug pull fraud in sharper relief. Several context points are worth stating explicitly.

First, this figure covers four venues only: PancakeSwap V2 and V3 on BNB Smart Chain, and Uniswap V2 and V3 on Ethereum. It does not include rug pull activity on Raydium, Jupiter, or other Solana DEXes; on Base, Arbitrum, or other L2s; or on any other EVM chain. The actual 2026 total across the full DeFi ecosystem is almost certainly a multiple of the tracked figure.

Second, the definition used is conservative: only direct liquidity removal events where the creator removes more than they added are counted. Honeypot contracts, LP token transfer rugs, and associated wallet extraction are excluded. The $1.17B figure is a methodologically conservative floor.

Third, the pace is accelerating. The first $500M was tracked across W1-W15 (15 weeks). The second $500M was tracked across W16-W26 (11 weeks). W27 and W28 together added another $139M. The weekly run rate has increased materially since the scope expanded to four venues from W23 onward.

Week-on-Week Comparison: W26 – W28

2026 Running Total – W23 to W28

Note: W1-W22 data covers PancakeSwap V2 only. From W23 onward, data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3. Running totals restated from full 2026 dataset.

About This Tracker

ChainAware.ai’s weekly rug pull tracker monitors liquidity removal fraud across PancakeSwap V2 and V3 on BNB Smart Chain and Uniswap V2 and V3 on Ethereum. Rug pull fraud is defined as liquidity removed by the pool creator that exceeds liquidity added by the creator – a net extraction event. The tracker uses ChainAware’s predictive rug pull detection engine, which achieves 90.1% detection accuracy in V3. Data is updated weekly. Historical data from W1-W22 covers PancakeSwap V2 only; multi-venue tracking expanded from W23 onward.

ChainAware.ai – Predictive Rug Pull Detection

Check Any Token Before You Invest

90.1% detection accuracy. Deployer wallet behavioral history + smart contract analysis. Free, no signup required. Results in under 2 seconds.

Check Rug Pull Risk Free

The post $74.9M Extracted in Week 28 – Rug Pull Fraud Surges 16.5% as 2026 Total Crosses $1 Billion first appeared on ChainAware.ai.

Data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3. Week 27 = week ending approximately July 6, 2026.

Week 27 confirmed that W26’s modest decline was a pause, not a trend break. $64,252,248 was extracted across 8,139 separate rug events – a 1.2% increase week-on-week, with rug events climbing to their highest level since W23. New pool creation rose to 13,893 first mints, the highest since W25. The running W1-W27 total now stands at $950,027,412 – crossing the $950M milestone and setting up a potential $1 billion cumulative total within the next two weeks.

Week 27 Key Numbers

Week 27 Analysis

W26 Dip Confirmed as a Pause

The 1.2% week-on-week increase in extracted fraud – from $63,470,936 to $64,252,248 – is small in percentage terms but meaningful in what it signals structurally. The three metrics that matter most all moved in the same direction: rug events rose from 7,579 to 8,139 (+7.4%), new pool mints rose from 12,682 to 13,893 (+9.5%), and total pools under observation rose from 10,897 to 11,248 (+3.2%). Fraud did not follow the same magnitude of increase only because average extraction per event fell slightly – to $7,892 per event versus $8,375 in W26. The underlying fraud machinery accelerated; the value extracted per event pulled back marginally.

The pattern is consistent with what this tracker has observed across previous multi-week sequences: rug pull fraud does not trend linearly. It oscillates week-to-week around a prevailing baseline, with the baseline itself shifting over multi-week periods. The W23-W27 baseline is materially higher than the W16-W20 baseline, and W27’s numbers offer no evidence of the baseline declining.

Rug Events Hit Highest Level Since W23

8,139 rug pull events in a single week is the highest event count recorded since W23’s 8,225. The practical implication is that the number of individual pools executing liquidity removal fraud continues to run well above the levels seen in W16-W22. High event counts with moderate per-event extraction values – the W27 pattern – are characteristic of a broad, distributed fraud environment rather than a concentrated one. Many smaller operators are running rug events simultaneously rather than a small number of large actors dominating the numbers. This is harder to detect and disrupt than concentrated fraud, because no single address or cluster generates a visible anomaly large enough to trigger conventional monitoring.

$950M Crossed – $1B Within Reach

The W1-W27 cumulative total of $950,027,412 crosses the $950M milestone and sets up a significant psychological threshold: $1 billion in cumulative rug pull fraud tracked since January 2026. At the W27 run rate of $64.3M per week, that milestone will be reached during W28 – approximately the week ending July 13, 2026.

The $1B figure requires its standard qualification: it covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 only, with the W1-W22 data covering PancakeSwap V2 exclusively. It does not capture rug pull activity on other DEXes, other chains, or through mechanisms other than direct liquidity removal. The actual total across the full DeFi ecosystem in 2026 is almost certainly a multiple of this figure. The tracked total is a floor, not a ceiling.

New Pool Creation Rises to Highest Since W25

13,893 new pool mints in W27 is the highest single-week figure since W25’s 13,620 and represents a 9.5% increase from W26. New pool creation is the most reliable leading indicator in this dataset – it precedes rug pull activity by approximately one to two weeks as newly minted pools accumulate liquidity before extraction events occur. The W27 elevation in new mints suggests W28 and W29 fraud values will remain elevated or increase further, absent a structural change in the market conditions driving new token creation.

Week-on-Week Comparison: W25 – W27

2026 Running Total – W21 to W27

Note: W1-W22 data covers PancakeSwap V2 only. From W23 onward, data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3.

About This Tracker

ChainAware.ai’s weekly rug pull tracker monitors liquidity removal fraud across PancakeSwap V2 and V3 on BNB Smart Chain and Uniswap V2 and V3 on Ethereum. Rug pull fraud is defined as liquidity removed by the pool creator that exceeds liquidity added by the creator – a net extraction event. The tracker uses ChainAware’s predictive rug pull detection engine, which achieves 90.1% detection accuracy in V3. Data is updated weekly. Historical data from W1-W22 covers PancakeSwap V2 only; multi-venue tracking expanded from W23 onward.

ChainAware.ai – Predictive Rug Pull Detection

Check Any Token Before You Invest

90.1% detection accuracy. Deployer wallet behavioral history + smart contract analysis. Free, no signup required. Results in under 2 seconds.

Check Rug Pull Risk Free

The post $64.3M Extracted in Week 27 – Running Total Crosses $950M as Fraud Reaccelerates first appeared on ChainAware.ai.

Data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3. Week 26 = week ending approximately June 29, 2026.

Week 26 brought a modest pullback in rug pull fraud across all four tracked venues. $63,470,936 was extracted across 7,579 separate rug events – a 7.1% decline from W25’s $68,295,752. New pool creation remained elevated at 12,682 first mints, keeping the rug-to-mint ratio near 60%. The running W1-W26 total now stands at $885,775,164 – within striking distance of the $900M milestone.

Week 26 Key Numbers

Week 26 Analysis

A Modest Cooling – Not a Trend Break

The 7.1% week-on-week decline in extracted fraud brings W26 to $63.5M – the lowest single-week figure since W22’s $38M, but still materially elevated versus the W16-W20 baseline range of $18M-$35M. The structural conditions driving rug pull activity have not changed: new pool creation remains high at 12,682 first mints, and the ratio of rug events to new mints held at 59.8% – meaning roughly six in every ten pools launched this week ended in a rug pull.

The decline in extracted value is better explained by a shift in pool composition than by any reduction in fraudulent intent. W26 saw fewer large-value pools among those that were rugged – the average extraction per event fell to approximately $8,375, down from $8,588 in W25. The fraud infrastructure remains fully active; the week simply produced fewer high-value targets.

New Pool Creation Stays Elevated

12,682 new pool mints across the four tracked venues keeps W26 in the upper range of 2026 weekly creation activity. For context, W16 saw only 9,827 new mints – W26’s figure is 29% higher. The sustained elevation in new pool creation is significant because it is the primary leading indicator of rug pull activity: more new pools means more potential extraction targets in the following 1-2 weeks. If new mint rates stay above 12,000 through W27-W28, extracted fraud values are likely to remain in the $55M-$75M range.

Running Total Closes In on $900M

The W1-W26 cumulative total of $885,775,164 puts the $900M milestone approximately one average week away. At the W26 run rate of $63.5M per week, the total will cross $900M during W27. This figure covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 – with the important caveat that W1-W22 data covers PancakeSwap V2 only, and the multi-venue expansion from W23 onward introduced a scope change that accounts for a portion of the step-up in weekly figures from W23.

Week-on-Week Comparison: W24 – W26

2026 Running Total – W1 to W26

Note: W1-W22 data covers PancakeSwap V2 only. From W23 onward, data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3.

About This Tracker

ChainAware.ai’s weekly rug pull tracker monitors liquidity removal fraud across PancakeSwap V2 and V3 on BNB Smart Chain and Uniswap V2 and V3 on Ethereum. Rug pull fraud is defined as liquidity removed by the pool creator that exceeds liquidity added by the creator – a net extraction event. The tracker uses ChainAware’s predictive rug pull detection engine, which achieves 90.1% detection accuracy in V3. Data is updated weekly. Historical data from W1-W22 covers PancakeSwap V2 only; multi-venue tracking expanded from W23 onward.

ChainAware.ai – Predictive Rug Pull Detection

Check Any Token Before You Invest

90.1% detection accuracy. Deployer wallet behavioral history + smart contract analysis. Free, no signup required. Results in under 2 seconds.

Check Rug Pull Risk Free

The post $63.5M Extracted in Week 26 – Fraud Stabilises as Running Total Approaches $900M first appeared on ChainAware.ai.

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.

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.

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. 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.

Rug pull events declined slightly to 10,274, down 8.0% from W24’s record 11,164 – but this remains the third-highest weekly event count in the dataset, behind only W23 and W24. Three consecutive weeks above 10,000 rug pull events confirms this is not a single-week anomaly. It is a sustained higher-intensity phase.

Three Weeks Above $54M: A New Baseline, Not a Spike

W23, W24, and W25 are now the three highest-value weeks in the dataset other than each other. With W25 reaccelerating rather than continuing the post-peak pullback seen in W24, the evidence increasingly points away from “spike and revert” and toward a structural step-change in extraction intensity. The removed-to-added liquidity ratio climbed back to 1.46x in W25, up from 1.40x in W24 – nearly matching the W23 peak ratio of 1.45x.

Reading the Reacceleration

Pool creation eased to 10,877 in W25, down from W24’s 11,790 – the lowest pool count since W22. Fewer new pools, fewer rug events, but higher fraud value and a higher extraction ratio. This combination points to a smaller number of operators running larger, more efficient extractions rather than a broad wave of low-value schemes. It is the inverse signature of W21, where event volume surged while value per rug fell – and a reminder that the rug pull industry shifts between volume-driven and value-driven phases depending on which operator cohort is most active in a given week.

The 5-week trailing average (W21-W25) now stands at approximately $50.5M – confirming that the post-W23 elevated baseline has held for a third consecutive week rather than reverting. The 25-week mean has risen to approximately $32.9M, pulled upward by three consecutive high-extraction weeks. For the full methodology and the original 20-week dataset that trained Rug Pull Detector V3, see our $569M PancakeSwap V2 analysis.

RUG PULL DETECTOR V3 – FREE

Check Any Pool Before You Invest – 90.1% Accuracy

Behavioral analysis of contract creators + smart contract code inspection. Handles pools and individual tokens. No signup required. For businesses, subscribe to the API. For AI agents, X402 protocol is enabled.

→ Run a Free Check at chainaware.ai/rugpull

25-Week Running Total: $822.4M

Week 25 pushes the cumulative total past the $800M milestone – $822,350,515 extracted from retail investors across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 in the first 25 weeks of 2026, just under halfway through the year.

Halfway through 2026, retail investors have lost $822.4M to confirmed liquidity-extraction rug pulls across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 alone – excluding LP token transfer schemes, unlocked token sell-offs, honeypots, and associated wallet extraction. At the current 25-week average of $32.9M per week, the full-year run rate projects to approximately $1.71 billion if the pattern of recent weeks persists. The three-week reacceleration in W23-W25 is the single largest contributor to that upward revision since the dataset began.

How to Protect Yourself

With three consecutive weeks above 10,000 rug pull events and extraction value reaccelerating rather than reverting, the current environment across PancakeSwap and Uniswap V2/V3 represents the highest-risk sustained period in the dataset’s history. ChainAware’s free tools screen any pool or token in under two minutes:

• Rug Pull Detector V3 – behavioral analysis of the contract creator + smart contract code inspection. 90.1% prediction accuracy. Free, no signup.
• Fraud Detector – full behavioral history of any deployer wallet. 98% fraud prediction accuracy.
• Wallet Auditor – for P2P transactions. Complete behavioral profile of any receiving wallet before sending funds.

NEXT WEEK

Week 26 Data Published Every Monday

ChainAware publishes fresh rug pull data every week across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 – pool creation, rug events, fraud value, and week-over-week analysis. Follow the archive for every weekly update.

→ Follow the Rug Pull News archive

About This Tracker

The ChainAware Weekly Rug Pull Tracker measures the most basic, mathematically verifiable form of rug pull across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3: a contract creator adds liquidity (Mint event), retail investors buy in, and the creator removes more than they added (Burn event). The difference is confirmed retail loss. This definition is conservative – it excludes LP token transfer rug pulls, unlocked token sell-offs, honeypot contracts, and associated wallet extraction. Numbers reported here represent the confirmed floor. For complete methodology, see chainaware.ai/resources/rugpull-verification .

The post $64.4M Extracted in Week 25 – Reacceleration Confirms New Baseline, Total Crosses $800M first appeared on ChainAware.ai.

Rug pull events followed a similar pattern – 11,164 events, up slightly from W23’s 10,955 (+1.9%), even as fraud value declined. This divergence mirrors the W20→W21 transition: value retreats from a peak while event count holds near record levels, signaling that the broader operator base from the spike week has not exited – only the highest-value extractions have become less frequent.

Volume Holds, Value Retreats

W24 confirms the pattern flagged after the W23 spike: a record-setting week is typically followed by a partial value pullback while event volume stays elevated. Pool creation also held steady at 11,790, up slightly from W23’s 11,693 – the fraud factories are not slowing down their deployment pipeline even as average value per extraction falls.

Reading the Post-Peak Pullback

The removed-to-added liquidity ratio fell to 1.40x in W24, down from 1.45x in W23 – the lowest ratio since W19. This is consistent with the post-spike pattern: a wave of new, lower-sophistication operators entering during the surge tends to dilute the average extraction efficiency per pool, even while gross fraud value and event counts both remain elevated relative to the pre-spike baseline.

Critically, W24’s $54.2M is still the second-highest weekly fraud value in the 24-week dataset – only W23 itself was higher. This is not a return to the W21-W22 plateau. The 5-week trailing average (W20-W24) now stands at approximately $49.5M, more than 80% above the prior 5-week trailing average reported after W22 ($26.4M). The extraction baseline has structurally shifted upward over the past three weeks. For the full methodology and the original 20-week dataset that trained Rug Pull Detector V3, see our $569M PancakeSwap V2 analysis.

RUG PULL DETECTOR V3 – FREE

Check Any Pool Before You Invest – 90.1% Accuracy

Behavioral analysis of contract creators + smart contract code inspection. Handles pools and individual tokens. No signup required. For businesses, subscribe to the API. For AI agents, X402 protocol is enabled.

→ Run a Free Check at chainaware.ai/rugpull

24-Week Running Total: $757.9M

The peak fraud value (W23) and the peak rug event count (W24) are now one week apart – a sign that this acceleration cycle has not yet fully completed its arc. Whether W25 brings a further pullback toward the W21-W22 plateau, or a second value spike, will determine whether this was a single-cycle event or the start of a sustained higher-extraction phase. We will report the data exactly as measured next Monday.

How to Protect Yourself

With 11,164 rug pull events – a new weekly record – and 94.7% of pools with active liquidity ending in a confirmed rug pull, the risk environment in W24 was the most hostile of the 24-week dataset by event count. ChainAware’s free tools screen any pool or token in under two minutes:

• Rug Pull Detector V3 – behavioral analysis of the contract creator + smart contract code inspection. 90.1% prediction accuracy. Free, no signup.
• Fraud Detector – full behavioral history of any deployer wallet. 98% fraud prediction accuracy.
• Wallet Auditor – for P2P transactions. Complete behavioral profile of any receiving wallet before sending funds.

NEXT WEEK

Week 25 Data Published Every Monday

ChainAware publishes fresh rug pull data every week across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 – pool creation, rug events, fraud value, and week-over-week analysis. Follow the archive for every weekly update.

→ Follow the Rug Pull News archive

About This Tracker

The ChainAware Weekly Rug Pull Tracker measures the most basic, mathematically verifiable form of rug pull across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3: a contract creator adds liquidity (Mint event), retail investors buy in, and the creator removes more than they added (Burn event). The difference is confirmed retail loss. This definition is conservative – it excludes LP token transfer rug pulls, unlocked token sell-offs, honeypot contracts, and associated wallet extraction. Numbers reported here represent the confirmed floor. For complete methodology, see chainaware.ai/resources/rugpull-verification .

The post $54.2M Extracted in Week 24 – Value Pulls Back, Event Count Hits New Peak first appeared on ChainAware.ai.

Rug pull events also surged – 10,955 in a single week, up 67.1% from W22’s 6,557. This is not a gradual climb. It is a step change, consistent with the same compression-then-spike dynamic that produced the W20 event.

The Cycle Called Its Shot

Two weeks ago, analyzing the W22 plateau, the projection was explicit: “Based on the W3-W8 and W13-W20 cycle patterns, the next acceleration window opens somewhere in W24-W26.” W23 arrived one week ahead of that window – close enough to confirm the underlying pattern, early enough to show that compression periods can break faster than the historical average once a new wave of operators is ready to deploy.

What’s Different This Time: Volume AND Value Both Surged

The W20 spike was driven primarily by value concentration – fewer high-value extractions. The W21 surge was driven primarily by event volume – more operators, lower value per rug. W23 is the first spike in the dataset where both metrics moved together at scale: rug events up 67.1% AND fraud value up 112.4% in the same week. This dual surge produced the highest single-week fraud value in the entire 23-week dataset, surpassing the previous peak (W04 at $53.4M) by 29%.

The removed-to-added liquidity ratio also climbed to 1.45x, up from 1.48x in W22 – essentially unchanged on a ratio basis, but applied against nearly double the pool volume. This is the clearest signal yet that the operators who compressed activity during the W21-W22 plateau redeployed at scale in W23, rather than exiting the market.

RUG PULL DETECTOR V3 – FREE

Check Any Pool Before You Invest – 90.1% Accuracy

Behavioral analysis of contract creators + smart contract code inspection. Handles pools and individual tokens. No signup required. For businesses, subscribe to the API. For AI agents, X402 protocol is enabled.

→ Run a Free Check at chainaware.ai/rugpull

23-Week Running Total: $703.8M

Week 23 pushes the cumulative total past the $700M milestone – $703,764,431 extracted from retail investors across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 in 23 weeks of 2026.

W23 now holds two records simultaneously: the highest single-week fraud value and the highest single-week rug pull event count of the entire 23-week dataset. The 5-week trailing average has jumped from $26.4M (reported last week) to $37.2M – a clear signal that the extraction baseline is shifting upward, not just experiencing a temporary spike. For the full methodology and the original 20-week dataset that trained Rug Pull Detector V3, see our $569M PancakeSwap V2 analysis.

How to Protect Yourself

With 10,955 rug pull events in a single week – the highest count in the dataset – the probability of any random new pool being fraudulent is now higher than at any prior point in 2026. Approximately 94% of new pools with liquidity activity in W23 ended in a confirmed rug pull event. ChainAware’s free tools screen any pool or token in under two minutes:

• Rug Pull Detector V3 – behavioral analysis of the contract creator + smart contract code inspection. 90.1% prediction accuracy. Free, no signup.
• Fraud Detector – full behavioral history of any deployer wallet. 98% fraud prediction accuracy.
• Wallet Auditor – for P2P transactions. Complete behavioral profile of any receiving wallet before sending funds.

NEXT WEEK

Week 24 Data Published Every Monday

ChainAware publishes fresh rug pull data every week across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 – pool creation, rug events, fraud value, and week-over-week analysis. Follow the archive for every weekly update.

→ Follow the Rug Pull News archive

About This Tracker

The ChainAware Weekly Rug Pull Tracker measures the most basic, mathematically verifiable form of rug pull across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3: a contract creator adds liquidity (Mint event), retail investors buy in, and the creator removes more than they added (Burn event). The difference is confirmed retail loss. This definition is conservative – it excludes LP token transfer rug pulls, unlocked token sell-offs, honeypot contracts, and associated wallet extraction. Numbers reported here represent the confirmed floor. For complete methodology, see chainaware.ai/resources/rugpull-verification .

The post $68.9M Extracted in Week 23 – Extraction Doubles as the Predicted Cycle Hits first appeared on ChainAware.ai.