Last Updated: February 2026

The numbers are worse than you think. On PancakeSwap, 95% of new liquidity pools end as rug pulls. On Pump.fun, the token launch platform that spawned hundreds of viral memecoins, 99% of launched tokens are designed to extract money from buyers. The crypto token market is not a market with some bad actors. It is an industry dominated by organized scam operations that treat retail investors as the product.

Understanding why this happens — and more importantly, how to protect yourself — requires understanding both types of token scam, the social engineering tactics that make them work, and the AI-powered detection tools that can identify both before you invest a single dollar.

This guide covers everything: instant rug pulls, long rug pulls, the DYOR framework that actually works, and how ChainAware’s Rug Pull Detector and Token Rank identify both scam types before the damage is done.

The Scale of the Problem: 95% and 99%

These figures are not exaggerations. They reflect the structural reality of permissionless token creation. On any chain where launching a token costs less than $50 and takes less than 10 minutes, the economics strongly favor scammers.

A rug pull operation works like a factory. A team creates a token with a compelling narrative — usually tapping into a current trend (AI, memecoins, celebrity culture, a viral event). They seed the liquidity pool with a small amount of capital, buy some of their own tokens to create price action, then use coordinated social media campaigns, paid influencers, and Telegram pump groups to generate FOMO among retail investors. When enough retail capital has entered the pool, they drain the liquidity and move on to the next token. Total operation time: 24–72 hours. Total profit: potentially hundreds of thousands of dollars. Total accountability: essentially zero.

According to Chainalysis Crypto Crime Report research, rug pulls and exit scams represent one of the largest categories of crypto fraud by volume, with billions lost annually. The FTC reported that Americans alone lost over $1 billion to crypto scams in 2022, with token scams representing a significant share.

The 95% figure for PancakeSwap reflects the BSC chain’s extremely low token creation cost and high speed — conditions that attract scammers disproportionately. The 99% on Pump.fun reflects a platform specifically designed for rapid token creation where the majority of launches are purely speculative and most devolve into rug pull dynamics within hours of launch.

AI Rug Pull Detection — 98% Accuracy

ChainAware Rug Pull Detector: Check Any Pool Before You Invest

Don’t invest in a pool you haven’t checked. ChainAware’s Rug Pull Detector uses AI to predict rug pull probability before it happens — analyzing liquidity lock status, dev wallet behavior, holder concentration, and contract risk signals. 98% accuracy. Covers ETH, BNB, Base, and more. Free to check.

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Instant Rug Pulls: How They Work

An instant rug pull follows a predictable playbook. Understanding each stage is the first step to recognizing one before it executes.

Stage 1: Token creation. A new token is deployed on a DEX — typically PancakeSwap (BSC), Uniswap (ETH), or a Pump.fun launch (Solana). The token has a name designed to ride a current narrative: a meme, a celebrity, an AI trend, a political figure. The smart contract may include hidden functions: a mint function that allows unlimited token creation, a blacklist function that can block holders from selling, or a maximum transaction size that prevents large sells but allows the dev wallet to exit freely.

Stage 2: Initial liquidity and price action. The scammer seeds the liquidity pool with a small amount of capital (often $1,000–$10,000) to establish an initial price. They then buy their own token in small increments to generate organic-looking price appreciation — creating a chart that shows steady upward movement and building the appearance of genuine demand.

Stage 3: Coordinated promotion. The pump campaign begins. Paid promoters post in Telegram groups and Discord servers. Influencer accounts post about the token (often without disclosing payment). Twitter bots amplify reach. The narrative is always the same: this is the next 100x, early investors are already up 200%, the window is closing fast.

Stage 4: Retail FOMO entry. Inexperienced investors, seeing price movement and social proof, enter the pool. Price continues to rise as more buyers enter. The token appears to be a genuine success. Volume looks real because new buyers are creating it.

Stage 5: Exit and drain. When the liquidity pool contains enough retail capital, the scammer executes the exit. They remove all liquidity from the pool — the pair of tokens and the underlying currency (ETH, BNB) — in a single transaction. Price drops to zero instantly. Everyone who bought is left holding worthless tokens with no way to sell. Total time from launch to exit: 24 to 72 hours in most cases. Some run for weeks to maximize the amount extracted.

The key technical enabler is unlocked liquidity. In a legitimate project, liquidity is locked in a time-locked contract — the developers cannot remove it for a defined period (commonly 6–12 months). In a rug pull, liquidity is held directly in the developer’s wallet and can be removed at any moment. This is the most important single check you can do before buying any new token.

Long Rug Pulls: The Slow Bleed

Long rug pulls are more dangerous than instant rug pulls in one critical way: they look legitimate. The project has a website, a whitepaper, an active community, regular updates, and a development team that appears engaged. The token has been around for months. It has institutional-looking backers. It appears, by every surface metric, to be a real project.

The mechanism is different but the outcome is the same. Instead of draining liquidity in a single transaction, the developers and early insiders continuously sell their token holdings — often disguised through multiple wallets, OTC desk sales, or gradual liquidation — while maintaining the appearance of ongoing development to keep retail holders from selling.

The price chart of a long rug pull has a characteristic shape: a strong initial pump (often engineered), followed by a gradual but relentless decline punctuated by short relief rallies that attract more buyers before the descent continues. Holders lose 80–90% of their investment not in a moment but over weeks or months, during which they are repeatedly told that development is progressing, the team is building, and the dip is a buying opportunity.

Detecting a long rug pull requires on-chain analysis that most investors never do. The key signals are all visible in the blockchain data: are the team wallets selling regularly? Are the top holder addresses changing over time as insider distribution continues? Is the wallet quality of holders improving (genuine DeFi users accumulating) or declining (experienced users exiting, being replaced by new retail)? Is there meaningful protocol revenue, or is volume entirely manufactured?

This is precisely what ChainAware’s Token Rank was built to detect — by analyzing the behavioral quality of a token’s holder base rather than just its quantity.

The Social Engineering Playbook

Token scams are not primarily technical operations. They are social engineering operations that use technical infrastructure. Understanding the psychological levers used is essential for recognizing manipulation before it affects your decisions.

FOMO (Fear Of Missing Out) is the primary weapon. Every message in a token pump campaign is designed to create urgency: “already 500% up from launch”, “still early”, “window closing”, “last chance before exchange listing”. The urgency is artificial but the emotional response it triggers is genuine. Experienced investors have trained themselves to treat urgency as a red flag rather than a signal to act.

Social proof manipulation is the second major lever. Paid Telegram groups show hundreds of members. Fake Twitter accounts amplify posts. KOL promotions create the appearance of community validation. According to SEC guidance on pump-and-dump schemes, this coordinated promotion is a defining characteristic of securities fraud — and in the crypto context, it is industrialized at a scale regulators have struggled to address.

Authority and celebrity fabrication. Scam tokens routinely use AI-generated images of celebrities “endorsing” the token, fake screenshots of mainstream media coverage, and invented advisor relationships with recognized names in the industry. None of these endorsements exist, but their visual presentation is sophisticated enough to fool investors who don’t verify claims independently.

The targets are systematically inexperienced investors — people new to crypto who don’t yet understand that on-chain contract checks, liquidity lock verification, and wallet behavior analysis are prerequisites for any DeFi investment. This is not an accident. The scam industry specifically designs its messaging to reach beginners before they develop the skills to recognize manipulation. As covered in our guide to rug pull detection, the best protection is combining DYOR skills with AI-powered detection tools.

Detect Long Rug Pulls Before They Happen

ChainAware Token Rank: On-Chain Holder Quality Analysis

Token Rank analyzes the behavioral quality of every wallet holding a token — are holders experienced DeFi users accumulating, or are insiders exiting while retail replaces them? Detect the slow-bleed pattern of long rug pulls before you’re down 80%. Free to check any token.

Check Token Rank Free Token Rank Complete Guide

DYOR: The Due Diligence Checklist That Actually Works

DYOR — Do Your Own Research — is the most frequently given advice in crypto and the least frequently followed. Most people who lose money in rug pulls knew they should have researched more. The problem is not motivation; it is knowing specifically what to check and where to find it. Here is the complete due diligence checklist for any new token.

1. Liquidity Lock Verification

This is the single most important check. If liquidity is not locked in a third-party time-locked contract (verifiable on DEXTools, Unicrypt, or similar), the developers can drain the pool at any moment. Check the lock duration — a lock of 30 days is meaningless for a project claiming a 3-year roadmap. Look for locks of 6 months or more. Verify the lock on-chain, not just from the project’s claims.

2. Smart Contract Audit Status

Has the contract been audited by a reputable firm? Audits don’t guarantee safety — many audited contracts still contain rug pull mechanisms — but the absence of any audit for a token asking for significant investment is a strong warning signal. Check whether the audit was performed by a recognized firm and whether it covers the specific functions most commonly used in rug pulls (mint functions, blacklist functions, max transaction limits).

3. Developer Wallet Analysis

Who holds the dev allocation, and what are they doing with it? Use a block explorer (Etherscan, BscScan) to find the wallet that deployed the contract. Check how much of the token supply it holds. Check whether it has been selling. Check whether it has moved tokens to multiple wallets — a common technique for distributing insider holdings before a coordinated exit. As detailed in the Wallet Auditor guide, on-chain wallet behavior tells you far more than any team announcement.

4. Holder Concentration Analysis

What percentage of the token supply is held by the top 10 wallets? If the top 10 wallets hold more than 40–50% of the supply, a coordinated exit by those wallets can crash the price regardless of how much liquidity is locked. Healthy tokens have distributed holder bases with no single wallet controlling enough supply to manipulate price unilaterally.

5. Contract Code Review

Read the contract code on the block explorer, or use a tool that summarizes key functions. Look specifically for: mint functions (can new tokens be created arbitrarily?), pause functions (can trading be stopped?), blacklist functions (can specific addresses be blocked from selling?), and owner privilege functions (what can the contract owner do unilaterally?). Any of these can be used to trap buyers.

6. Team and Project Verification

Is the team doxxed (publicly identified)? Anonymous teams are not automatically scams — Bitcoin was created by an anonymous team — but anonymous teams have no reputational accountability if they exit. Verify any claimed team credentials independently. Search the project name on Twitter and Telegram for scam reports. Check whether the project’s GitHub has genuine commit history or is a copied repository with superficial changes.

7. Token Rank and Rug Pull Detector Check

These two AI tools together cover what manual DYOR cannot: behavioral prediction based on on-chain data patterns across millions of wallets. Run both before investing in any token you are not certain about. The combination catches both instant rug pull setups (Rug Pull Detector) and long rug pull dynamics (Token Rank).

ChainAware Rug Pull Detector: AI Detection Before It Happens

Traditional rug pull detection tools are reactive — they flag contracts after fraud is confirmed. ChainAware’s Predictive Rug Pull Detector is forward-looking: it analyzes contract and pool characteristics to predict rug pull probability before any exit occurs.

The Rug Pull Detector evaluates a set of on-chain signals that, in combination, are predictive of rug pull risk with 98% accuracy. These signals include liquidity lock status and duration, smart contract code flags (hidden mint functions, sell restrictions, owner privileges), developer wallet concentration and historical behavior patterns, trading pattern anomalies (coordinated buys from linked wallets, artificial volume creation), and holder distribution characteristics.

The output is a risk score from Safe through Watchlist to High Risk, with a probability score and a breakdown of the specific risk factors detected. A High Risk rating means the pool’s characteristics match the pattern of confirmed rug pulls with high statistical confidence — not that fraud has already been confirmed, but that the structural setup matches the template.

Critically, the Rug Pull Detector catches what manual research misses: it processes the full on-chain history and contract code simultaneously, identifying subtle combinations of risk factors that individually appear innocuous but together strongly predict a rug pull outcome. A contract with slightly elevated developer wallet concentration, a short liquidity lock, a few hidden functions, and wash-trading-like volume patterns may not raise a red flag from any single check — but the AI model recognizes the combination as high risk from training on thousands of confirmed rug pull cases.

For a complete breakdown of how the Rug Pull Detector works, the forensic signals it analyzes, and how to interpret results, see the complete Rug Pull Detector guide. For the broader context of how predictive fraud detection compares to forensic approaches, see our analysis of forensic vs AI-based crypto analytics.

Don’t Invest Before You Check

Run Both Checks: Rug Pull Detector + Token Rank

The Rug Pull Detector catches instant rug pull setups. Token Rank catches long rug pull dynamics. Together they cover both scam types with AI-powered predictive accuracy. Check any token contract or pool address — free, instant results, no account needed.

Rug Pull Detector Token Rank

Token Rank: Detecting Long Rug Pulls via Holder Quality

Token Rank addresses the detection problem that rug pull detectors don’t cover: the long rug pull, where the project looks legitimate but insider distribution is destroying holder value over time.

Token Rank applies ChainAware’s Wallet Auditor methodology to every wallet that holds a specific token. Instead of just counting holders, it profiles them: are they experienced DeFi users with diversified protocol histories and strong Wallet Ranks? Or are they new, low-quality wallets — potentially linked to the project team — or retail buyers who have replaced exiting insiders?

The key signals Token Rank surfaces for long rug pull detection are the following.

Holder quality trend: Is the average Wallet Rank of holders increasing (smart money accumulating) or decreasing (smart money exiting, retail replacing it)? This single signal is a powerful leading indicator — experienced DeFi users accumulate before breakouts and exit before collapses. When high-rank holders are consistently leaving a token, the long rug pull pattern is often already underway.

Developer and insider wallet behavior: Token Rank identifies which wallets among the top holders are likely insider positions based on behavioral patterns — early receipt of tokens, consistent small-scale selling, and counterparty relationships with the deployer wallet. A project where identified insider wallets are selling while publicly promoting the project is exhibiting the defining characteristic of a long rug pull.

Holder concentration dynamics: Is the token becoming more distributed over time (a healthy sign) or is concentration increasing as small holders exit and large wallets consolidate? Increasing concentration in unidentified wallets combined with declining high-quality holder ratio is a strong long rug pull signal.

Token Rank provides the on-chain perspective that no amount of reading whitepapers or following project Twitter accounts can give you. The blockchain doesn’t lie. When experienced on-chain investors are quietly exiting while the project’s social media celebrates milestones, Token Rank shows you both sides of that picture simultaneously. As noted in our broader guide to crypto trust score metrics, behavioral on-chain data is the only source that cannot be fabricated by a motivated scam team.

Prediction MCP: Rug Pull Detection for AI Agents and Developers

The Rug Pull Detector and Token Rank are built for individual investors checking contracts manually. But what if you’re building a DeFi protocol, a trading bot, a portfolio tool, or an AI agent that needs to screen contracts automatically — at scale, in real time, without human intervention?

This is exactly what the ChainAware Prediction MCP was built for.

What Is the Prediction MCP?

MCP stands for Model Context Protocol — an open standard created by Anthropic that allows AI agents and LLMs (Claude, GPT, custom models) to call external tools via natural language. ChainAware’s Behavioral Prediction MCP server exposes its AI models — including the Rug Pull Detector — as callable tools that any MCP-compatible agent can use without writing custom API integrations.

In plain terms: your AI agent can ask “Is this contract address a rug pull risk?” and get back a structured risk score, probability, and forensic breakdown in under 100ms — the same intelligence that powers the free web tool, accessible programmatically.

The chainaware-rug-pull-detector Agent

ChainAware publishes a ready-to-use open-source agent definition on GitHub specifically for rug pull detection: the chainaware-rug-pull-detector agent. This is a pre-built Claude agent configuration that combines the predictive_rug_pull MCP tool with guided reasoning — so you can deploy a rug pull screening agent in minutes without writing prompts from scratch.

The agent accepts a contract address and network, calls the predictive_rug_pull tool, interprets the output (status, probabilityFraud, forensic_details), and returns a human-readable risk assessment. It can be embedded into any MCP-compatible workflow: a DeFi frontend, a Telegram bot, an automated investment screener, or a compliance pipeline.

Direct API Integration: predictive_rug_pull Tool

For developers who want full control, the predictive_rug_pull tool is directly accessible via the MCP server. The tool takes three inputs — API key, network (ETH, BNB, BASE, HAQQ), and contract address — and returns:

• status: Safe, Watchlist, or HighRisk
• probabilityFraud: decimal score from 0.00 to 1.00
• forensic_details: full breakdown of the on-chain risk signals detected
• lastChecked: timestamp of the last prediction run

This makes it straightforward to build automated screening into any system that processes token addresses — for example, automatically flagging high-risk contracts before they appear in your platform’s listing, or alerting LP providers when a pool they hold a position in crosses a risk threshold.

Example Use Cases for AI Agent Integration

• DeFi protocol listing screening: Before listing a new token or liquidity pool, run every contract address through the rug pull detection agent automatically. Reject or flag High Risk contracts without manual review.
• Telegram and Discord bots: Users paste a contract address, the bot calls the MCP tool and returns an instant risk score with forensic breakdown — giving your community a self-serve due diligence tool.
• AI-powered investment assistant: An AI agent advising on DeFi positions calls predictive_rug_pull as part of its research workflow before any recommendation involving a new token.
• Portfolio monitoring: Periodically re-check contract addresses in a user’s portfolio — if a previously Safe contract moves to Watchlist or High Risk, trigger an alert.
• Compliance pipeline: Automate token contract screening as part of a broader AML and fraud prevention stack alongside the predictive_fraud and aml_scorer tools.

Getting Started with the Prediction MCP

The MCP server is live at https://prediction.mcp.chainaware.ai/sse. Integration takes under 30 minutes:

1. Get an API key via chainaware.ai/mcp
2. Add the server to your Claude, Cursor, or custom MCP client configuration
3. Use the open-source agent definitions on GitHub as a starting point: github.com/ChainAware/behavioral-prediction-mcp
4. Call predictive_rug_pull with any contract address on ETH, BNB, BASE, or HAQQ

The 12 pre-built open-source agent definitions cover the full ChainAware intelligence stack — fraud detection, AML scoring, wallet behavioral analysis, onboarding routing, and rug pull detection — giving you a complete on-chain intelligence layer for any AI agent you’re building. See the full MCP integration guide for complete setup instructions.

Build Rug Pull Detection Into Your AI Agent

ChainAware Prediction MCP — Open Source Agent Definitions

The chainaware-rug-pull-detector agent is ready to deploy. Connect any AI agent to ChainAware’s rug pull detection model via MCP — get structured risk scores, probability scores, and forensic breakdowns in real time. 12 open-source agent definitions on GitHub. API key required.

View on GitHub Get API Key

Red Flag Reference: What to Check Before You Buy

Here is a quick-reference summary of the most important warning signals across both instant and long rug pull types. Consider this a pre-investment checklist.

Instant Rug Pull Red Flags

• Liquidity not locked or locked for less than 3 months
• Contract has mint, blacklist, or sell-restriction functions
• Developer wallet holds more than 15% of supply
• Token launched less than 7 days ago with no audit
• Volume is dominated by a small number of coordinated wallets
• Telegram/Discord group was created days before launch
• Price is up more than 300% with no product or utility

Long Rug Pull Red Flags

• Developer wallets selling regularly while team publicly bullish
• Top holder list changing over time with high-Wallet-Rank wallets consistently exiting
• Revenue metrics don’t match claimed traction — volume is real but protocol fees are minimal
• Team compensation structure rewards token sales rather than protocol performance
• Roadmap milestones completed slowly while token allocation vests on schedule
• Token Rank shows declining holder quality over consecutive weeks

General Red Flags for Both Types

• Anonymous team with no verifiable credentials or accountability
• Guaranteed return claims or minimum price guarantees
• Heavy reliance on KOL promotion without product demonstration
• Whitepaper that describes a product but has no working code or verifiable development
• Community that aggressively attacks skeptics rather than engaging with technical questions

For broader context on crypto security risks and protective measures, the hardware wallets guide covers the infrastructure layer of crypto security, while the Fraud Detector guide explains how behavioral AI detects fraudulent wallets — useful for due diligence on counterparties as well as tokens. According to Europol’s Internet Organised Crime Threat Assessment, crypto fraud has become one of the most profitable categories of organised cybercrime globally — the operations behind these token scams are professional businesses, not amateur opportunists.

ChainAware.ai — Protect Yourself Before You Invest

Rug Pull Detector + Token Rank

95% of new pools are rug pulls. Don’t trust social media. Trust the blockchain. ChainAware’s AI detects instant rug pull setups before they happen, and Token Rank identifies long rug pulls through holder behavior analysis. Both free. Both essential. Check before you buy.

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Frequently Asked Questions

What is a rug pull in crypto?

A rug pull is a type of DeFi scam where developers create a token, artificially inflate its price through coordinated promotion, attract retail investor capital, then suddenly drain the liquidity pool — taking all deposited funds and leaving token holders with worthless assets. The term comes from the expression “pulling the rug out” from under investors. The loss is typically 100% and occurs in a single transaction.

What is a long rug pull?

A long rug pull (or “slow rug”) is a scam where the project appears legitimate but developers and early insiders continuously sell their token allocations over weeks or months while maintaining the appearance of ongoing development. Unlike an instant rug pull, the loss occurs gradually — investors lose 80–90% of their investment over time rather than immediately. Long rug pulls are harder to detect without on-chain holder analysis tools like Token Rank.

Why are 95% of PancakeSwap pools rug pulls?

PancakeSwap on BSC (BNB Smart Chain) has extremely low token creation costs and fast transaction speeds, making it the preferred platform for token scam operations. The barrier to creating and launching a fraudulent token is under $50 and 10 minutes. The 95% figure reflects that the vast majority of new BSC token pools are created by scam operations rather than genuine projects.

How does the ChainAware Rug Pull Detector work?

The Rug Pull Detector uses AI trained on thousands of confirmed rug pull cases to evaluate on-chain signals: liquidity lock status, smart contract code flags, developer wallet concentration, trading pattern anomalies, and holder distribution. It calculates a risk score and probability before any exit occurs — detecting the structural setup of a rug pull rather than waiting for the fraud to complete. Accuracy is 98%. See the complete guide for full methodology.

How does Token Rank detect long rug pulls?

Token Rank profiles every wallet that holds a specific token using the Wallet Auditor behavioral methodology. It then tracks whether high-quality wallets (experienced DeFi users with strong Wallet Ranks) are accumulating or exiting. When experienced holders consistently leave while less experienced retail buyers replace them, this matches the pattern of insider distribution in long rug pull scenarios. The trend in holder quality is a leading indicator that can identify the scam weeks before the price decline becomes obvious.

What is the most important check before buying a new token?

Liquidity lock verification is the single most important manual check. If the liquidity pool is not locked in a third-party time-locked contract, the developers can drain it at any moment. Beyond this, run the ChainAware Rug Pull Detector for instant risk assessment, check Token Rank for holder quality, and verify developer wallet activity on the block explorer. Never invest based solely on social media promotion or KOL endorsement without doing these checks first.

Can I integrate rug pull detection into my own AI agent or platform?

Yes. ChainAware’s Prediction MCP exposes the same rug pull detection model via the Model Context Protocol standard. Any MCP-compatible AI agent (Claude, GPT, custom LLMs) can call the predictive_rug_pull tool with a contract address and receive a structured risk score, probability, and forensic breakdown in real time. A ready-to-use open-source agent definition is available on GitHub at github.com/ChainAware/behavioral-prediction-mcp. API key required — get access at chainaware.ai/mcp.

Disclaimer: This article is for educational purposes only and does not constitute financial or investment advice. Cryptocurrency investments carry significant risk. Always conduct thorough due diligence before investing in any crypto asset.

The post How to Identify Fake Crypto Tokens in 2026: Rug Pulls, Long Rug Pulls, and DYOR first appeared on ChainAware.ai.

X Space with Magic Square — ChainAware co-founder Martin joins the Magic Square community to discuss Web3 AdTech, predictive fraud detection, user acquisition costs, and why the same two forces that drove Web2’s growth will determine whether Web3 crosses the chasm. Listen to the full recording on X

Most Web3 projects excel at building technology and fail at finding users. The unit cost of a blockchain business process has dropped to near zero through full automation — yet customer acquisition costs remain brutally high, hovering around $1,000 per transacting DeFi user. Meanwhile, new entrants burn their fingers on rug pulls and leave the ecosystem permanently, shrinking the addressable market every day. In this X Space hosted by Magic Square, ChainAware co-founder Martin maps exactly why this situation exists, what history tells us about how to fix it, and how ChainAware’s predictive AI platform addresses both problems simultaneously. The conversation covers the intellectual property moat of custom AI models, the critical distinction between predictive AI and LLMs, the mechanics of wallet-based behavioral targeting, and why the Web2 AdTech revolution is the most relevant precedent for where Web3 goes next.

From SmartCredit to ChainAware: How Each Product Discovered the Next

ChainAware did not start as an AI fraud detection company. It started as a DeFi lending platform. Martin and his twin brother Tarmo — both former Credit Suisse Vice Presidents with over ten years at the institution in Zurich — built SmartCredit.io first: a fixed-term, fixed-interest DeFi borrowing and lending marketplace. Before joining Credit Suisse, Martin had already launched four successful products with a combined user base that has grown to somewhere between 250,000 and 500,000 users over the years. That product-building instinct defined how ChainAware was built — through direct observation of what each product needed, not through top-down strategic planning.

SmartCredit required credit scoring. Credit scoring required fraud detection. Fraud detection, once built, revealed it could be applied to smart contract rug pull prediction. Rug pull detection expanded into a full wallet auditing capability. Wallet auditing created the behavioral data foundation needed for personalized user targeting. Each step answered a question raised by the previous one. As Martin explains: “What is Chain Aware? We are practically a prediction engine now. We are predicting behavior. We are predicting who is doing fraud on the blockchain, who is doing rug pulls, who is borrowing next, who is lending next, who is doing trading next. We are predicting behavior.” For the complete product architecture overview, see our ChainAware product guide.

The Prediction Engine: Fraud Detection, Rug Pull Detection, and Wallet Auditing

ChainAware’s platform operates across three interconnected prediction layers, each serving a distinct use case while sharing the same underlying behavioral data infrastructure. Understanding how these layers work together clarifies why they are more powerful as a combined system than as standalone tools.

Fraud detection addresses the most immediate trust problem in Web3: interacting with unknown addresses. On a pseudonymous blockchain, you cannot know whether the person behind an address has a history of scams, money laundering, or protocol manipulation. ChainAware’s fraud detection model analyzes the complete transaction history of any address and produces a real-time fraud probability score — with 98% backtested accuracy against confirmed fraud cases from CryptoScamDB. The prediction is forward-looking, not backward-looking: it tells you what this address is likely to do next, not just what it has done in the past. For the complete fraud detection methodology, see our fraud detection guide.

Rug Pull Prediction: 100% Loss Prevention

Rug pull detection operates on a different threat model. While fraud detection evaluates individual wallets, rug pull detection evaluates the people behind smart contracts and liquidity pools. The distinction matters commercially: a trading loss might cost 20-50% depending on stop losses, but a rug pull results in 100% loss — “chairman total shard” as Martin describes it. ChainAware traces both the contract creator’s funding chain and the behavioral histories of all liquidity providers, identifying the fraud signature in their prior on-chain activity rather than in the contract code itself. This approach catches the sophisticated rug pulls that static contract scanners miss entirely, because sophisticated operators deliberately write clean code while their behavioral history remains permanently on-chain. For the complete rug pull methodology, see our rug pull detection guide.

Wallet Auditing: The Full Behavioral Profile

Wallet auditing combines all prediction layers into a single behavioral profile for any address. The audit calculates experience level, risk tolerance, behavioral intentions (borrower, lender, trader, staker, gamer), and fraud probability — constructing what Martin calls a “human Persona behind the blockchain.” This profile requires no KYC, no identity disclosure, and no data sharing beyond the address itself and its public transaction history. Beyond security, the wallet auditor serves a commercial function: it enables Web3 platforms to understand exactly who is visiting their platform, what those users are likely to do next, and how to reach them with resonating content. For the wallet auditor implementation, see our wallet auditor guide and our behavioral analytics guide.

Three Layers. One Platform. Instant Results.

ChainAware Free Tools — Fraud Detector, Rug Pull Detector, Wallet Auditor

Enter any wallet address or contract and get the full picture in under a second: fraud probability (98% accuracy), rug pull risk with full creator and LP chain analysis, experience level, risk profile, and behavioral intentions. No signup. No KYC. Free for individual use on ETH, BNB, BASE, HAQQ, and more.

Audit Any Wallet Free Check Fraud Risk

The Intellectual Property Moat: Why Custom AI Models Cannot Be Copied

One of the most commercially significant points Martin makes in the conversation concerns the structural difference between building on open-source smart contract code and building proprietary AI models. Most DeFi projects are built on copied foundations — and Martin names this directly with specific examples. Compound wrote the original lending protocol source code. Aave copied Compound’s source code. Then every other lending protocol copied Compound or Aave. PancakeSwap copied the PancakeSwap predecessor. Uniswap then copied or iterated on that, and subsequently the entire DEX ecosystem copied Uniswap. As Martin states clearly: “If you take Uniswap, Uniswap copied a pancreas source code and then everyone copied Uniswap. Everyone copied everyone else’s source code.”

This copying dynamic made DeFi protocols highly replicable but also highly commoditized. Any team with basic Solidity skills can deploy a fork of an existing protocol in days. By contrast, ChainAware’s fraud detection, rug pull prediction, and behavioral analytics models are proprietary intellectual property built over more than two years of model training, backtesting, and iteration. Nobody can fork a trained neural network the way they can fork a GitHub repository. As Martin explains: “If you have AI models, these are not public. This is your intellectual property that you have built. And this intellectual property no one can copy. They can try to redevelop it — meaning it’s a very strong entry barrier.” When competitors claim comparable AI capabilities, ChainAware’s response is direct: specify your prediction accuracy, your data set, and your backtesting methodology. So far, no challenger has provided those details. For more on the competitive positioning, see our predictive AI guide.

98% Accuracy in Real-Time: The Deliberate Downgrade from 99%

ChainAware’s fraud model journey from 60% to 98% accuracy took over two years of iterative development. The path was not linear: initial models achieved roughly 60% prediction accuracy, then improved to 70%, then eventually reached 98%. During that progression, the team also achieved 99% accuracy — and deliberately rejected it. The reason was operational: the 99% model required processing so much additional data that it crossed the threshold from real-time to near-real-time response. For fraud detection specifically, that latency distinction is consequential. A warning that arrives after an interaction has completed offers significantly less user value than one that arrives in time to prevent the interaction entirely.

The decision to stabilize at 98% real-time rather than 99% near-real-time reflects a clear product philosophy: accuracy that arrives too late is less valuable than slightly lower accuracy that arrives in time to act on. As Martin explains: “We had to decide — do we offer 98% real-time or 99% near-real-time? We just say okay, time to scale down. We offer 98% real-time.” The 98% figure is also, as it happens, a more credible claim than 99% — precisely because it acknowledges the real trade-offs involved in production AI systems rather than overpromising. For the complete model accuracy discussion, see our fraud detector guide and our generative vs predictive AI guide.

Predictive AI vs LLM: Two Different Tools for Two Different Jobs

A community member asks whether AI might at some point be turned against users — whether the technology that protects could also harm. Martin’s answer reframes the question entirely by separating two fundamentally different types of AI that the public currently conflates under a single term.

Large Language Models — the category that includes ChatGPT, Claude, Gemini, and the AI tools that became mainstream from 2022 onward — are fundamentally statistical autoregression engines. They learn probabilistic relationships between tokens in text and generate the most statistically probable continuation given the input. Martin is precise about what this means: “LLM is just a statistical auto regression engine, meaning you’re predicting the next word, the next words, the next paragraph, the next sequence.” LLMs are excellent at content generation, conversation, summarisation, and translation. They are not designed to make deterministic numerical predictions about future behavioral events from structured transactional data.

Predictive AI — the category ChainAware operates in — uses supervised learning on labeled behavioral datasets to classify and predict future states. Rather than generating probable text, it produces probability scores for specific outcomes: this address will commit fraud with 0.87 probability, this pool will rug pull with 0.93 probability, this wallet’s next action will be a leveraged trade with 0.74 probability. These are deterministic numerical outputs trained on domain-specific financial behavioral data. As Martin frames it: “Predictive AI will help you to see Personas behind these bits and bytes.” The Matrix analogy is apt — most people see raw transaction data, while ChainAware’s models see the person behind it. For a full breakdown of the two AI categories, see our generative vs predictive AI guide and our real AI use cases guide.

Building Trust in the Web3 Ecosystem: Verification Without KYC

Martin’s argument about ecosystem-level fraud impact extends well beyond individual user protection. The case he makes is structural: the rate at which new users enter and stay in the Web3 ecosystem is directly constrained by the rate at which they encounter fraud, and every user who burns their fingers on rug pulls and leaves permanently represents a permanent reduction in the ecosystem’s growth ceiling.

The pattern Martin describes is familiar to anyone who has tried to onboard non-crypto-native users. A new participant joins, gets exposed to shilling groups, buys into promoted tokens, experiences one or more rug pulls, and concludes that the entire space is fraudulent. They do not try again. They become negative advocates who discourage others from entering. This cycle compounds over time: high fraud rates reduce new user retention, which reduces liquidity and ecosystem vitality, which makes the space less attractive to the next wave of entrants. Conversely, reducing fraud rates creates a trust environment where new users can explore, learn, and eventually become committed participants. As Martin states: “Solving the fraud issue — giving all users possibilities first to verify themselves anonymously. Verification doesn’t mean that you have to open your KYC. You just have to open your address and show who you are. Via this verification, we will create trust in a blockchain.” For the complete trust infrastructure argument, see our Share My Audit guide and our Web3 trust guide.

Anonymous Trust: The Address as Identity

ChainAware’s approach to trust infrastructure rests on a specific insight about blockchain’s properties. On-chain transaction history is immutable, permanent, and public — yet it requires no personal identity disclosure to read or share. This creates a unique opportunity: an address can prove its trustworthiness without ever revealing who owns it. A wallet with five years of sophisticated DeFi interactions, zero fraud associations, and consistent protocol usage tells a compelling story about its owner’s reliability — purely from public behavioral data, without KYC, without identity documents, and without any centralized verification authority. Martin’s practical application is direct: when someone approaches with a business proposal, ask them to sign their wallet and share the audit. If their transaction history is clean and their behavioral profile is consistent with their claims, the interaction can proceed. If it is not, the evidence is cryptographic and permanent. For how this translates into the Share My Wallet product, see our Share My Audit guide.

The Web3 Unit Cost Revolution and the User Acquisition Paradox

One of the most analytically precise arguments in the conversation concerns what Martin calls the unit cost paradox. Web3 has achieved something genuinely revolutionary: it has automated business processes end-to-end, eliminating the back-office operations, settlement delays, counterparty risk, and institutional intermediaries that make financial services expensive in traditional systems. The unit cost of a DeFi lending transaction, a token swap, or a yield farming interaction is a fraction of the equivalent traditional finance operation — and in many cases, the costs shift to the user in the form of gas fees, making the protocol’s marginal cost effectively zero.

Yet despite this dramatic unit cost reduction, Web3 projects consistently fail to become sustainable businesses. The reason is that user acquisition costs are completely disconnected from operational costs. While protocol operations cost pennies, acquiring a genuine transacting DeFi user costs approximately $1,000 or more through existing marketing channels. That asymmetry makes unit economics non-viable at every scale. As Martin explains: “There is no point if your unit cost of a business process is $1, $5, $10 and your customer acquisition costs are $1,000. You have to balance it out, you have to fix it.” Web2 faced the same paradox in the early 2000s — business process costs had dropped dramatically through digitization, but customer acquisition costs remained in the thousands of dollars until AdTech changed the equation. For more on the unit economics framework, see our unit costs and AdTech guide.

The Google Parallel: How Web2 Solved AdTech and What Web3 Must Do Next

Martin’s historical framing of the Web3 problem draws a precise and instructive parallel to Web2’s experience. In Web2’s early growth phase, two specific problems prevented mainstream adoption: rampant credit card fraud that made consumers reluctant to transact online, and prohibitively expensive user acquisition costs driven by mass marketing. Both problems had to be solved for Web2 to cross the chasm from early adopters to mass market.

Fraud was suppressed through mandated transaction monitoring systems — every bank and payment processor was required to deploy real-time AI-based monitoring that could detect new fraud patterns as they emerged. User acquisition costs were reduced through AdTech — Google’s innovation of using search history and browsing behavior to infer user intentions and target advertising accordingly. The critical insight Martin emphasizes is that it was not the search engine itself that made Google the most valuable company in advertising history. Rather, it was the AdTech layer built on top of it. As Martin states directly: “It wasn’t the search engine, it was the AdTech that they created. Twitter, Facebook — let’s be transparent — these are AdTech companies. Google gets 95% of its revenues from AdTech. It’s user targeting.” For the complete Web2-Web3 parallel, see our ChainAware vs Google Web2 guide and Statista’s Google advertising revenue data .

Blockchain History as the Web3 Equivalent of Search History

Google’s AdTech revolution worked because search queries and browsing behavior provided a proxy for user intent — imperfect and easily gamed, but vastly better than demographic targeting. ChainAware’s approach to Web3 AdTech uses a data source that is structurally superior: on-chain transaction history. Every blockchain transaction reflects a deliberate, paid financial decision — not a casual query or accidental page visit. The behavioral signal is higher quality precisely because the gas fee filter removes casual, performative, and accidental behavior. A wallet that has executed twenty leveraged trades on a derivatives protocol has demonstrated its preferences through real money, not just search terms. Predicting its next action with 98% accuracy and targeting it accordingly produces a dramatically higher return on marketing spend than sending the same message to every visitor. For how this translates into the marketing agent product, see our AI marketing for Web3 guide and our intention-based marketing guide.

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Mass Marketing vs Targeted Marketing: Why Web3 Is Stuck in the 1990s

Martin’s critique of Web3 marketing is specific and data-driven. Every major marketing channel in the current Web3 ecosystem delivers the same message to every recipient regardless of their behavioral profile, intentions, or experience level. CoinGecko banner ads reach DeFi veterans and complete beginners simultaneously, showing both the identical creative. CMC listings present the same project overview to retail speculators and sophisticated protocol researchers. KOL posts go out to entire follower bases whether those followers are stakers, traders, NFT collectors, or people who bought their first token last week. Cointelegraph articles are read by everyone who arrives at that headline, regardless of what they are actually looking for.

This mass marketing approach has two compounding problems. First, it generates traffic without generating relevant traffic — visitors arrive at a platform, find messaging that does not speak to their specific needs, and leave without converting. Second, the cost per impression is identical regardless of whether the impression lands in front of a highly qualified prospect or a completely unqualified one. The combination produces terrible unit economics: high spend, low conversion, enormous effective cost per acquired user. As Martin observes: “Crypto media — you go to Cointelegraph, same message for everyone. You see the crypto banners, same message for everyone. But same message for everyone doesn’t resonate with everyone. People are different, people have different intentions, people have different behavior. So you have to resonate with the users.” For more on how personalization addresses this, see our high-conversion Web3 marketing guide and our Web3 personas guide.

The Amazon Landing Page: No Two Visitors See the Same Website

Martin uses Amazon.com as the most vivid illustration of what genuinely personalized user experience looks like at scale. Amazon’s personalization infrastructure means that every visitor to the site sees a different version of the homepage, different product recommendations, different pricing emphasis, and different promotional content — all calculated in real time based on that specific visitor’s browsing history, purchase history, and behavioral signals inferred from millions of comparable user journeys.

This personalization is not cosmetic. It is not about color schemes or font choices. It is about matching the product surface to the specific intent each visitor brings to that session. A user who has been browsing professional photography equipment sees professional camera recommendations. A user who has been researching home office setups sees ergonomic furniture. Neither visitor is served generic “bestsellers” — they are each served a version of Amazon optimized for their specific, data-derived intention profile. Web3 today operates at the opposite extreme: every visitor to every DApp sees the same landing page, the same hero message, the same call-to-action, regardless of whether they are a DeFi native with three years of leveraged trading history or someone connecting a wallet for the first time. As Martin states: “Go on Amazon.com and compare your landing page with others. Every landing page is different because it’s calculated based on your intentions. There’s no two same landing pages. Go in Web3 — everyone gets the same landing page. Every single user.” For how ChainAware’s marketing agent creates this Amazon-style experience for Web3 platforms, see our Web3 adaptive UX guide and our user segmentation guide.

The Web3 AdTech Competitive Landscape: Underdeveloped and Misunderstood

In response to a question about competitors, Martin describes the state of the Web3 AdTech market in precise terms that reveal both the opportunity and the misconception that characterizes most of it. The reference point is the Safary Web3 Growth Landscape — a regularly maintained map of Web3 marketing and analytics companies that ChainAware joined in August, listed in the attribution and AdTech sectors. The landscape contains over 100 companies that have collectively received more than $1 billion in investment.

Looking closely at the companies in the AdTech category, however, reveals a significant mismatch between label and function. Most of them are publisher networks — platforms like Coinzilla and BitMedia that distribute crypto advertising inventory across publisher sites. These are ad distribution networks, not AdTech companies in the behavioral targeting sense. They can deliver impressions but cannot calculate user intentions, segment audiences by behavioral profiles, or serve personalized content based on on-chain history. Real AdTech requires two components: an analytics layer that calculates user behavioral intentions from their history, and a targeting layer that delivers content matched to those intentions. The combination of both in a Web3-native form, using on-chain transaction history as the data source, is what Martin describes as nearly absent from the current market. As he explains: “If you’re looking at the AdTech sector and analyzing these companies, you see that the part of real targeting — intention calculation, behavior calculation, combined with targeting — is pretty underdeveloped.” For a breakdown of how ChainAware fits into the Web3 growth landscape, see our behavioral analytics guide.

Why Wallet-to-Wallet Messaging Fails as a Targeting Method

One approach that some companies have tried is wallet-to-wallet messaging: sending communications directly to wallet addresses via on-chain protocols or aggregator interfaces. Martin dismisses this approach with a specific data point: only approximately 5% of users have enabled wallet-to-wallet messaging. The 95% who have not enabled it either never see the message or find it in a spam folder they rarely check. Beyond the reach problem, there is a consent and relevance problem: unsolicited wallet messages are widely perceived as spam, which actively damages brand perception rather than improving conversion. Effective targeting requires reaching users in the contexts where they are already engaged — not inserting messages into communication channels they mostly ignore. For more on effective Web3 user acquisition approaches, see our Web3 marketing guide.

AML vs Transaction Monitoring: The Regulatory Distinction Most Projects Ignore

Martin addresses the compliance landscape with a technical distinction that has significant practical consequences for any Web3 project that needs to meet regulatory requirements. The two primary compliance tools in the blockchain space — AML (Anti-Money Laundering) analysis and transaction monitoring — are fundamentally different technologies that solve different problems, yet most projects and even most compliance vendors treat them as interchangeable.

AML analysis is a rules-based algorithm. It traces the flow of known-illicit funds through the blockchain ecosystem, following contaminated money from flagged sources through intermediate addresses to identify who may have received proceeds from criminal activity. The rules that define “illicit” are codified based on known past cases. This makes AML analysis effective at tracking funds connected to previously identified bad actors, but structurally incapable of detecting genuinely new fraud patterns that have not yet been flagged. Regulators under MiCA and FATF frameworks require both AML compliance and real-time AI-based transaction monitoring — not one as a substitute for the other. As Martin explains: “AML is a rules-based algorithm. But the regulator mandates transaction monitoring because the same happened in Web2. Every bank, every virtual asset service provider has to do actually both.” For the complete regulatory context and compliance implementation, see our AML and transaction monitoring guide, our blockchain compliance guide, and the FATF virtual assets recommendations .

Why Fraud Farms Stay Ahead of Static Tools

Martin introduces the concept of “fraud farms” — sophisticated organizations that operate fraud as a professional business, continuously adapting their methods to circumvent the detection systems their targets deploy. These operations know what tools their counterparties use. They design their fraud patterns specifically to pass rules-based AML checks while remaining active. Static rules-based systems, by their nature, can only detect patterns that have already been codified — which means they are always behind the current state of fraud innovation. AI-based transaction monitoring learns from new patterns continuously, updating its detection capability as new fraud techniques emerge. This continuous learning capability is what makes it mandated rather than optional under forward-looking regulatory frameworks. For the transaction monitoring agent implementation, see our transaction monitoring agent guide.

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ChainGPT Partnership and IDO: Why the Right Ecosystem Partner Matters

The conversation covers ChainAware’s IDO plans, with Martin providing both the commercial details and the strategic reasoning behind choosing ChainGPT as the exclusive launchpad and lead investor. The IDO was announced the day before this recording, with ChainGPT as lead investor alongside Koinix. The launch would use ChainGPT’s launchpad exclusively. At the time of listing, the fully diluted valuation was set at $3.5 million, with an initial market cap of $80,000 before liquidity — a structure Martin described as deliberately attractive to genuine participants rather than optimized for opening-day hype.

Beyond the economics, Martin’s assessment of ChainGPT as a partner reflects a specific philosophy about which relationships create long-term value. ChainGPT’s investment thesis focuses explicitly on projects with real technology and genuine use cases, screening out the category of project that combines copied source code with a large shilling army. As Martin explains: “ChainGPT is looking for the real stuff. They’re not looking for someone like what we had in DeFi summer — 95% of projects copied someone and put a shilling army on top. ChainGPT is focused on AI, analytics, predictions. That’s what they focus on. We are very happy to be in this family.” The contrast Martin draws with anonymous VC relationships — where partners may not understand the technology they are backing — highlights how partnership quality affects both credibility and long-term project sustainability.

Crossing the Chasm: The Two Requirements for Web3 Mainstream Adoption

Martin’s closing remarks synthesise everything discussed into a single, clear framework for Web3 mainstream adoption. The framework has exactly two components, both historically demonstrated in Web2, both currently unresolved in Web3.

First, fraud rates must decrease significantly. High fraud rates prevent new users from establishing positive experiences in the ecosystem. Every rug pull experienced by a newcomer is a permanent ecosystem exit. Building trust through accessible, anonymous behavioral verification — making it possible for any participant to verify any address without KYC — is the mechanism by which fraud rates fall. When bad actors know they can be identified by their on-chain behavior before they execute the next scam, the cost-benefit calculation of fraud changes. When potential victims can check an address before they interact, the success rate of fraud attempts drops. Both effects compound over time to create a more trustworthy ecosystem that retains new entrants rather than driving them away. For the full fraud ecosystem argument, see our Web3 growth guide and Chainalysis’s crypto crime data .

Innovation Cannot Scale Without Sustainable Unit Economics

Second, user acquisition costs must fall to sustainable levels through targeted, intent-based marketing. Web3 has solved the operational cost problem — business process unit costs are already at levels that make the technology structurally superior to traditional finance. However, solving the operational side while leaving acquisition costs at $1,000 per user creates a business model that cannot reach sustainability regardless of how elegant the technology is. Projects in this situation have two options: raise more capital and burn it on mass marketing, or launch a token and use speculation to subsidize acquisition. Neither path leads to the sustainable revenue generation that enables long-term product iteration. As Martin states in his closing remarks: “From one side we have to introduce the AdTech systems which reduce mass-related user acquisition costs. From the other side, we have to create much higher trust in the ecosystem. That’s all the same that happened in Web2. We are not inventing anything new — we are just repeating what Web2 did.” For how ChainAware’s complete platform addresses both requirements simultaneously, see our product guide and our Web3 agentic economy guide.

Comparison Tables

Web3 Mass Marketing vs ChainAware Intent-Based Targeting

AML Tools vs Transaction Monitoring: What Regulators Actually Require

Frequently Asked Questions

What is Magic Square and why did they host this X Space with ChainAware?

Magic Square is a Web3 app store and launchpad that curates and distributes decentralized applications to its community. The X Space series they run brings Web3 projects to their audience for educational conversations about technology, use cases, and ecosystem development. ChainAware’s focus on fraud detection and Web3 AdTech aligned directly with topics relevant to Magic Square’s community of Web3 users and builders — specifically the questions of how to verify project legitimacy and how Web3 projects can find users sustainably.

Why did ChainAware build its own AI models instead of using OpenAI or other LLMs?

ChainAware’s core use cases — fraud detection, rug pull prediction, and behavioral intention calculation — require deterministic numerical outputs trained on structured financial transaction data. LLMs are designed to generate probable text sequences, not to classify future behavioral events from on-chain data with 98% accuracy. Beyond the technical mismatch, building proprietary AI models creates a defensible intellectual property moat. DeFi smart contract code can be forked in hours. A trained neural network with 2+ years of iteration, carefully curated training data, and validated backtesting results cannot be replicated without equivalent investment of time and expertise. This IP moat is one of ChainAware’s core competitive advantages.

How does ChainAware’s wallet verification work without KYC?

ChainAware analyzes only publicly available on-chain transaction data — no personal identity information is required at any point. A user who wants to verify themselves shares their wallet address and cryptographically signs a message proving they control it. ChainAware’s models then analyze the public transaction history of that address and produce a behavioral profile: fraud probability, experience level, risk tolerance, and predicted intentions. The profile proves trustworthiness through demonstrated financial behavior without revealing who the person behind the address is. This maintains the pseudonymity that blockchain users value while enabling the trust signals that counterparties, investors, and platforms need.

What chains does ChainAware currently support, and which are coming next?

At the time of this X Space, fraud detection was live on four chains and rug pull detection was live on two. ChainAware was actively working on full-package integrations for new chains — adding fraud detection, rug pull detection, and behavioral intention calculation together rather than piecemeal. The next chain announced was HAQQ Network (Islamic Coin). The team aims to add a new chain approximately every one to two months, with the goal of delivering the complete product suite on each new chain rather than partial capabilities. For the current chain coverage, see the chainaware.ai platform directly.

Why are Web3 user acquisition costs so high, and how does ChainAware help reduce them?

Web3 user acquisition costs are high because the entire marketing ecosystem operates on mass marketing — sending the same message to everyone regardless of behavioral profile, experience level, or intent. Mass marketing generates impressions but not conversions, because undifferentiated messages do not resonate with the specific needs of diverse user segments. ChainAware calculates each visiting wallet’s behavioral profile from their on-chain transaction history and uses that profile to serve matched, resonating content automatically. The result is that the marketing message reaching a DeFi trader speaks to their trading context, while the message reaching a first-time user speaks to their entry-level needs. Higher relevance produces higher conversion rates, which reduces the effective cost per acquired user — exactly as Google’s AdTech reduced Web2’s acquisition costs from thousands of dollars to tens of dollars.

This article is based on the X Space hosted by Magic Square featuring ChainAware co-founder Martin. Listen to the full recording on X . For integration support or product questions, visit chainaware.ai.

The post Web3 AdTech and Fraud Detection — X Space with Magic Square first appeared on ChainAware.ai.

X Space #19 — AI-Based Predictive Rug Pull Detection: Why Static Analysis Fails and Behavioral AI Wins. Watch the full recording on YouTube · Listen on X

X Space #19 focuses on one of Web3’s most damaging and least understood fraud categories: rug pulls. Co-founders Martin and Tarmo open with a striking claim — that the tools most Web3 users rely on for rug pull protection are not just ineffective but actively dangerous, because they create false security while leaving investors completely exposed to the real risk. This session explains the anatomy of a professional rug pull operation, why static token contract analysis fundamentally cannot protect against rug pulls, and why only behavioral AI operating on pool dynamics can predict which liquidity pools will collapse before they do.

What a Rug Pull Actually Is — and Why It Differs from a Trading Loss

Before discussing detection, Martin and Tarmo establish a precise definition of rug pulls that distinguishes them from other forms of investment loss. This distinction matters because it changes both the risk calculation and the protection strategy required.

In standard crypto trading, losses are partial and bounded. A trader using stop-losses might lose 20%, 30%, or 50% of a position — painful, but survivable. Positions can be reduced, hedged, or timed. The loss is proportional to the degree to which the price moves against the position.

The 100% Loss Mechanism

A rug pull produces a categorically different outcome: total loss of 100% of the invested capital, instantly. Martin explains the mechanism: “From one second to the next, you just do a trade, and the next five seconds the liquidity is taken. So the value of your tokens that you just bought is zero. You lose not 20%, not 50% — you lose everything.” The rug pull executes in one of two ways. Either the liquidity providers withdraw all base currency (typically BNB or ETH) from the trading pool simultaneously, collapsing the token price to zero in a single transaction — or the token contract’s creators sell an enormous pre-allocated token supply into the pool, diluting the price to zero in seconds. In either case, the result is identical: investors who bought the token before the event hold worthless tokens with no liquidity to sell into. There is no stop-loss that triggers fast enough. There is no partial exit. The loss is instantaneous and total. For more context on how this connects to the broader Web3 trust problem, see our guide on building trust in anonymous Web3 ecosystems.

The Scale of the Problem: 1,400 Pools Per Day, 95% Rug Rate

Martin presents the quantitative reality of rug pull prevalence on PancakeSwap — numbers that most Web3 participants have never encountered directly but that shape the ecosystem fundamentally.

Approximately 1,400 to 1,500 new liquidity pools launch on PancakeSwap every single day. This number is not declining — it grows as the ecosystem expands. Of those new pools, approximately 95% execute rug pulls. The timeline is remarkably consistent: median trading activity runs for 30 to 90 minutes from pool creation before the liquidity event that destroys value. As Martin describes: “You see on these new pools they are created, and then some trading is starting maybe 30 to 90 minutes. That’s the median average. And after this trading, 30 minutes more or less, the liquidity is on zero.”

What These Numbers Mean in Practice

The arithmetic is brutal: on any given day, approximately 1,330 to 1,425 new PancakeSwap pools are designed to fail — each one a coordinated operation to extract capital from investors before collapsing. On Solana’s pump.fun platform, the rug rate is even higher at 98-99%. These numbers mean that any investor participating in early-stage pool activity without a reliable predictive system is operating in an environment where the overwhelming majority of opportunities are traps. Tarmo frames it directly: “If you know this number — 95% of early pools are rug pools where you lose all your investment — two options: think twice, or second, identify which pools are doing rug pull.” For context on how these statistics affect the overall ecosystem growth trajectory, see our guide to AI-based predictive fraud detection in Web3.

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The Rug Pull Industry: Professional Social Engineering at Scale

Martin and Tarmo make an argument that fundamentally reframes how most people understand rug pulls: they are not opportunistic individual fraud events. They are the output of a professional, systematically organised industry with dedicated staffing, psychological expertise, and refined operational procedures.

Tarmo explains the structure: “Rug pull is social engineering, where a mood is created. And if you have been in crypto, you know there are armies of shillers and armies of bots which can create mood and just push certain new pools or new ideas so that everybody thinks this is the next big thing.” The rug pull visible to investors — the contract, the pool, the token price pumping and then collapsing — is the final 60-90 minute execution phase of a much longer operation. Before that execution phase, a systematic social engineering campaign runs across dozens of Telegram channels, Discord servers, and Twitter accounts simultaneously.

The Social Engineering Infrastructure

This infrastructure is genuinely professional. Tarmo is specific: “These guys are expert psychologists. They know which patterns to push. They know mass psychology, group psychology, individual psychology — how to influence.” The shilling operation includes: coordinated message timing across multiple channels (creating the impression of organic discovery), carefully crafted narratives (positioning the token as the “next Ethereum” or “next Bitcoin” to trigger FOMO), manufactured social proof (bot accounts generating apparent community enthusiasm), and precision timing (launching the pump during peak trading hours in target markets). Each element is designed by people who understand behavioral psychology at a professional level. The token contract is not where the rug pull lives — it is merely the final instrument. The rug pull lives in the social engineering operation surrounding it. For more on how this connects to the broader fraud ecosystem, see our guide to why AI agents will accelerate Web3.

The Human Psychology Exploit: Why Victims Keep Coming Back

One of the most psychologically astute observations in X Space #19 is Martin’s explanation of why new investors continue falling for rug pulls even after the statistics are well-known and widely discussed. The answer lies not in ignorance but in a specific cognitive pattern that the rug pull industry deliberately exploits.

Bitcoin’s rise from pennies to $60,000+ created a generation of investors who deeply regret missing the early opportunity. Ethereum’s rise from cents to thousands created another cohort of “should have invested earlier” narratives. Every major crypto success story generates a corresponding cohort of people searching for “the next one.” As Martin describes: “People are searching for the next Vitalik, the next Solana, the next Bitcoin. It’s the search for the next ten x. It’s the search for the next hundred x. And the rug pullers know it very well. They play on this human psychology.”

The Newcomer Vulnerability

Experienced crypto participants develop resistance to shilling over time — through burned fingers, pattern recognition, and community knowledge. However, the Web3 ecosystem constantly onboards new participants who lack this protective scepticism. As Tarmo explains: “Newbies come and there are masters in psychology who just start manipulating them. They think this is the next big thing. I buy this token, there are so many good messages about it — it resonates with me.” The rug pull industry’s primary target is always this cohort: people who entered the ecosystem recently enough to still believe every heavily-promoted new launch might be a genuine opportunity. The social engineering is specifically calibrated for their psychological profile — the combination of FOMO, optimism about transformative technology, and insufficient pattern-recognition experience. For more on how trust infrastructure can protect newcomers, see our guide to building trust in Web3.

Token Contract vs Pool Contract: The Critical Distinction

The most technically important section of X Space #19 is Tarmo and Martin’s explanation of why virtually all currently available rug pull protection tools are analyzing the wrong object. Understanding this distinction is essential for understanding both why current protections fail and what effective protection actually requires.

Every meme coin or new token launch involves two separate contracts: the token contract and the pool contract. The token contract defines the token’s properties — its name, total supply, minting rules, ownership mechanics, transfer restrictions, and tax parameters. The pool contract (on Uniswap, PancakeSwap, or equivalent) defines the liquidity pool — the trading pair, the liquidity depth, and the mechanics of adding and removing liquidity.

Where the Rug Actually Happens

Rug pulls happen at the pool level, not the token level. When a rug pull executes, the liquidity providers remove their base currency (BNB or ETH) from the pool contract — leaving the token with no liquidity to trade against. This is a pool event, not a token event. The token contract may be perfectly clean — no hidden minting capability, no unusual tax parameters, no proxy mechanisms — and the rug pull still happens because the pool’s liquidity providers choose to withdraw. As Tarmo states directly: “You can have a token contract clean, but you have a pool which is fully doing a rug pull in the next 30 minutes — with a clean token contract.” Tools that analyze only the token contract are therefore analyzing a completely different object from the one where the actual risk event occurs.

The False Security Problem: Why Token Audits Don’t Protect You

Martin and Tarmo are explicit and pointed about the most popular rug pull protection tools — GoPlus, QuickIntel, Honeypot.is, and similar platforms — arguing that they create a false sense of security that is arguably worse than no protection at all.

These tools work by analyzing the EVM bytecode of the token contract, searching for known patterns associated with malicious tokens: high buy/sell taxes that trap investors, proxy contract structures that allow code replacement after deployment, unlimited minting capabilities that can dilute the token supply, and blacklisting functions that prevent specific addresses from selling. When none of these patterns appear, the tool reports the token as “safe” or “low risk.”

Why Sophisticated Actors Don’t Use These Patterns Anymore

Martin’s response to this approach is pointed: “Really? If someone is using buy-sell tax — I really don’t believe that anyone is doing this now, because the tools are there. It sounds like nineties.” Professional rug pullers know exactly what GoPlus, QuickIntel, and Honeypot.is check. They know the specific EVM patterns that trigger flags. Consequently, they simply don’t use those patterns. A sophisticated rug pull operation deploys a token contract with no buy/sell tax, no proxy mechanisms, no unlimited minting — passing all static analysis checks with a clean report — and then executes the rug entirely at the pool level, where no static analysis tool is looking. As Tarmo summarises: “People get things. They can trust somebody. They don’t know what the real trade is and what the real risk is. And the real risk is the pool.” For context on how this compares to similar false security in the broader fraud detection landscape, see our guide to AML vs transaction monitoring.

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Static vs Dynamic: Why Rules-Based Analysis Always Loses

Martin frames the fundamental problem with all static analysis approaches using a systems theory argument: static rules cannot effectively protect against dynamic adversaries. This principle applies universally — not just to rug pull detection but to every security challenge where the threat actor adapts.

A static analysis system publishes its detection logic — either explicitly (as GoPlus does with its documentation) or implicitly (as rug pullers reverse-engineer it through experimentation). The moment adversaries understand what patterns a static system detects, they simply design their operations to avoid those patterns. The system becomes permanently obsolete the moment its detection logic is understood. As Martin argues: “If you are a dynamical adversary, you need to respond with dynamical pattern matching tools. If you are static with published rules against a dynamic adversary — who is going to win? It’s an easy prey.”

Complexity Theory Applied to Rug Pull Detection

Tarmo applies a principle from complexity theory: “If you have a complex problem, there is no simple solution.” The rug pull ecosystem is a complex, adaptive, self-evolving system with professional participants who invest in understanding and defeating detection systems. Attempting to address this complexity with simple static rules is a category error — not just a technical limitation but a fundamental misalignment between problem complexity and solution complexity. The correct response to a complex, dynamic adversary is a complex, dynamic defense system: AI that learns from observed rug pull patterns, continuously retrains on new cases, and detects behavioral signatures that the rules-based tools cannot see because they manifest at the pool level rather than the token contract level. For the broader application of this principle, see our comparison of forensic vs AI-based crypto analytics.

ChainAware Copied: A Real-World Rug Pull Anatomy

Martin shares a direct example of how rug pull operations work in practice — using ChainAware itself as the target of an impersonation attack. ChainAware has been copied at least three known times (and likely ten or more), with its web interface, branding, and product design replicated by rug pull operators who then used the copied interface to direct users to fraudulent systems.

In at least two of these cases, the copycat operation created a fake token associated with the fraudulent ChainAware site. In one specific case that Martin and Tarmo tracked in real time: “We saw the token was created. Someone copied our website fully — a full copy, fully working. They copied the token and started shilling it. We saw how the token price was going up. Very interesting. Of course our detectors said this is a rug — don’t use it.” The shilling operation pushed the fake ChainAware token to a market cap of approximately $120,000. Then, within seconds of reaching that peak, the liquidity was withdrawn entirely — a textbook rug pull execution.

The Anatomy of the Operation

This specific case illustrates every element of the professional rug pull operation Tarmo described: the social engineering phase (shilling across multiple channels, leveraging ChainAware’s legitimate brand recognition to create credibility), the fake token with a clean contract (designed to pass all static analysis checks), the coordinated pump (likely involving bots and paid shillers who knew the token would rug), and the instantaneous collapse. The $120K peak was not an accident — it was the target. The operators pumped to their planned exit level, withdrew liquidity, and moved to the next operation. New investors who bought near the peak held worthless tokens. The entire operation from pool creation to collapse likely ran in under two hours. For more on how ChainAware’s fraud tools detected this in real time, see our Fraud Detector complete guide.

Why AML Tools Cannot Detect Rug Pulls

Martin and Tarmo address a natural question: given that Chainalysis has received $500M+ in investment and TRM Labs has received approximately $149M, aren’t these tools protecting Web3 users from rug pulls? The answer is no — and the reason explains why the investment has not reduced rug pull rates.

AML tools are forensic documentation systems. They start from a database of known bad addresses — wallets confirmed to have participated in fraud, sanctioned entities, mixer service outputs — and then trace the flow of funds from those starting points through the blockchain. An address receives a risk score based on how much of its balance can be traced back to known bad sources within a defined number of transaction hops. This approach has legitimate value for compliance purposes: preventing known criminal funds from entering regulated platforms.

The Six-Hop Evasion Problem

However, AML analysis has a well-known limitation that rug pull operators fully exploit: after six transaction hops, funds are considered “clean” by most AML systems. Martin explains the mechanism: “You have a bad address. You do six hops — just next, next, next, six times counter. Your money is clean. Really? Yes, that’s how these systems work.” A rug pull operation that routes its proceeds through six intermediate addresses before extracting them completely defeats AML detection. More fundamentally, rug pull operations typically use freshly created addresses that have no prior bad history — they don’t appear in any AML database because they’ve never been flagged before. The entire premise of AML analysis (checking against known-bad address lists) is irrelevant to rug pulls executed by newly created, previously-unknown operators. For the full comparison, see our guide to Web3 AI transaction monitoring agents.

How Behavioral AI Predicts Rug Pulls Before They Happen

Having established what doesn’t work and why, Tarmo explains ChainAware’s approach to rug pull detection — which operates on entirely different principles from both static token analysis and AML fund-flow tracing.

ChainAware’s rug pull detector treats pool behavior as a dynamic system to be monitored continuously rather than a static object to be checked once at creation. The analysis integrates multiple data streams simultaneously: the behavioral profile of the addresses that created the pool, the fraud probability scores of the addresses adding liquidity, the pattern and timing of liquidity additions, and the evolving dynamics of pool activity over time. Each of these inputs provides signal about the probability of a rug pull — and the combination of all inputs produces a prediction far more accurate than any single indicator could provide.

The Liquidity Provider Analysis

The key insight is that rug pulls are executed by specific addresses — the liquidity providers who will withdraw the base currency when the pump reaches its target. These addresses have behavioral histories. Even if they have never participated in a confirmed rug pull before, their overall transaction patterns, their associations with other addresses, and their behavioral signatures all contain information about their likely intentions. Martin explains: “We’re looking on the contracts, it’s not only contract creation, it’s as well liquidity adding. Let’s say someone is creating a contract from a good address, but then the liquidity is added by a bad address — what do we do in this case? Or liquidity is added on a good address, and then a lot of liquidity is added on a bad address.” Every event in the pool’s lifecycle contributes to its evolving risk assessment. Additionally, the system recalculates continuously — not just at creation but with every new on-chain event. For the full technical specification, see our Rug Pull Detector complete guide.

The Web2 Credit Card Fraud Parallel

Throughout X Space #19, Martin repeatedly draws the parallel between Web3’s current rug pull problem and Web2’s credit card fraud crisis of the late 1990s and early 2000s. This parallel is not merely rhetorical — it provides a precise historical blueprint for how the problem gets solved.

Web2 in its early phase had an extremely high credit card fraud rate that prevented mainstream adoption of e-commerce. Consumers were afraid to enter payment information online. Web2 companies couldn’t reach cash flow positive because every time someone tried to transact, there was meaningful probability of fraud. The transaction was simply too risky for most users.

How Web2 Solved It

Web2 solved this not through static rules but through AI-based transaction monitoring: dynamic behavioral detection systems that learned from confirmed fraud cases and predicted future fraud from behavioral patterns. As Martin and Tarmo note: “When Web2 started it was like the beginning — 50 million users. And there was very, very high fraud rate. And then the solution was introduction of AI-based transaction monitoring on the credit cards.” The parallel to Web3 is exact: the same mechanism that cleaned up credit card fraud in Web2 — dynamic behavioral AI rather than static rules — is what can clean up rug pull fraud in Web3. The technology exists. ChainAware has built it. The question is adoption speed. For the full fraud detection analysis including Web2 historical parallels, see our complete Web3 fraud detection guide.

The Two Solutions Web3 Needs to Cross the Chasm

X Space #19 closes by connecting rug pull detection to the broader framework that Martin and Tarmo have developed across multiple X Space sessions: the two technological capabilities that Web3 must develop to cross the chasm from early adoption to mainstream use.

The first capability is AI-based fraud and rug pull detection — reducing the fraud rate to a level where new users can participate in Web3 without an overwhelming probability of losing everything in their first experiences. Web2’s crossing of the chasm required exactly this: making transactions safe enough for ordinary people to participate. Every user who gets rugged and leaves Web3 permanently, warning friends to stay away, is a permanent loss to the ecosystem’s growth potential.

The Second Capability: AdTech for User Acquisition

The second capability is intention-based AdTech — bringing down user acquisition costs through behavioral targeting. Martin covered this extensively in previous X Spaces (see our intention-based Web3 AdTech guide). These two capabilities are complementary: fraud detection creates a safe environment that users can trust, and AdTech provides the coordination mechanism that routes users to products relevant to their specific needs. Without the first, new users get burned and leave. Without the second, they never find the right products in the first place. As Tarmo summarises: “Web3 can repeat Web2’s success story. Just learn from the past and do the same. We have the technologies.” The implementation of both capabilities is what Martin calls the “crossing of the chasm” for Web3 — the transition from a high-friction, high-fraud environment with 50 million users to a trusted, efficiently coordinated ecosystem with hundreds of millions of users. For the full crossing-the-chasm analysis, see our guide to how ChainAware is doing for Web3 what Google did for Web2.

Comparison Tables

Static Token Analysis vs Behavioral AI Rug Pull Detection

Property	Static Token Analysis (GoPlus, QuickIntel, Honeypot.is)	Behavioral AI Detection (ChainAware)
What it analyzes	Token contract EVM bytecode	Pool dynamics + liquidity provider behavior
Where rug actually happens	Not analyzed — rug happens at pool level	Analyzed — pool is the primary subject
Detection method	Static pattern matching — known bad patterns	Dynamic behavioral prediction — learned patterns
Bypassed by sophisticated actors?	Yes — trivially, by avoiding known patterns	Much harder — behavioral patterns persist
Detects fresh addresses?	No — no history to flag	Yes — behavioral signals in transaction patterns
Continuous monitoring	No — snapshot at query time	Yes — recalculates on every new on-chain event
Covers liquidity provider risk	No	Yes — fraud probability scores for LP addresses
False security risk	High — “safe” token can still rug at pool level	Low — analyzes actual rug pull mechanism
Accuracy	Limited — sophisticated actors bypass easily	Very high — behavioral AI trained on confirmed cases
Free for individuals	Yes	Yes — limited free tier, ~$20/month for full access

AML Tools vs Predictive Rug Pull Detection

Frequently Asked Questions

Why does a clean token contract not protect against rug pulls?

Because rug pulls happen at the pool level, not the token level. The liquidity pool is a separate contract from the token contract. When rug pull operators withdraw liquidity from the pool, they do so through pool mechanics — not through any function in the token contract. A token contract with no buy/sell tax, no proxy structure, and no unlimited minting capability can still be paired with a pool that gets rugged in 30 minutes. Static analysis tools that check token contracts are analyzing the wrong object entirely. For more, see our Rug Pull Detector complete guide.

Why do sophisticated rug pull operators have clean token contracts?

Because they know exactly what GoPlus, QuickIntel, Honeypot.is, and similar tools check. These tools analyze EVM bytecode for known patterns: high buy/sell taxes, proxy contracts, unlimited minting functions. Professional rug pull operators simply don’t use these patterns. They deploy token contracts that pass all static analysis checks with clean results, then execute the rug entirely at the pool level — where no static analysis tool is looking. As Martin notes: “If someone is using buy/sell tax in 2024, I really don’t believe anyone is doing this now. The tools are there. It sounds like nineties.”

Why can’t AML tools prevent rug pulls?

AML tools trace the flow of funds from known-bad addresses through a defined number of transaction hops (typically 5-6). Rug pull operators bypass this in two ways: (1) they use freshly created addresses with no prior bad history, so they don’t appear in any AML database; (2) even if proceeds from a previous rug are routed through 6 hops, most AML systems classify them as clean. More fundamentally, AML analysis is backward-looking — it identifies addresses that have already committed fraud. Rug pull protection requires forward-looking prediction of which pools will rug in the future.

How does behavioral AI detect rug pulls that static analysis misses?

Behavioral AI analyzes the dynamic pattern of pool activity rather than the static code of the token contract. ChainAware monitors: who created the pool and what their fraud probability score is, who added liquidity and what their behavioral profiles indicate, how liquidity has flowed in and out over time, and what the overall pattern of activity looks like relative to confirmed past rug pulls. These behavioral signals manifest at the pool level — exactly where rug pulls actually happen — and they persist regardless of how clean the token contract appears. The system recalculates continuously with every new on-chain event, so emerging risk patterns trigger alerts before the liquidity withdrawal executes.

Why do people keep getting rugged despite warnings?

Because rug pull operators target a specific psychological vulnerability: the desire to be an early investor in “the next Bitcoin” or “the next Ethereum.” Every major crypto success story creates a large cohort of investors who regret missing the early opportunity. The rug pull industry employs professional psychologists and coordinated shilling operations specifically designed to trigger this FOMO response in new entrants who haven’t yet developed the pattern-recognition skills to identify manipulation. Additionally, the 30-90 minute window between pool creation and rug execution is short enough that even suspicious investors may not complete their research in time. Predictive tools that provide instant risk assessment at pool creation are the practical solution — see chainaware.ai/rug-pull-detector.

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This article is based on X Space #19 hosted by ChainAware.ai co-founders Martin and Tarmo. Watch the full recording on YouTube · Listen on X . For questions or integration support, visit chainaware.ai.

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