Rug Pull Prevention¶

The Problem: You're Investing Before the Threat Is Visible¶

A rug pull is simple in its mechanics and devastating in its effect. A team launches a token, attracts liquidity from investors, then withdraws all funds from the liquidity pool and disappears — leaving investors holding worthless tokens and the protocol unable to recover.

The scale is staggering. Research shows that approximately 95% of new liquidity pools launched on PancakeSwap show characteristics consistent with potential fraud or abandonment within their first 48 hours. On Ethereum's Uniswap, the proportion of new tokens that end in rug pulls or exit scams exceeds 50%.

For individual investors, the losses are real and often life-altering. For DeFi protocols that host these pools, the reputational damage is severe — and they are increasingly being held responsible for not screening what they list.

Why Standard Tools Fail¶

Most token security tools take one of two approaches:

1. Smart contract audits. Audit the code to check for malicious functions — hidden mint permissions, ownership backdoors, transfer restrictions. This sounds reasonable, but experienced rug pullers have adapted. They deploy contracts that pass audits cleanly, then execute the rug through LP withdrawal or coordinated selling — mechanisms that are perfectly legal in code but catastrophically fraudulent in intent.

2. Social signals. Telegram activity, Twitter followers, team doxxing. These signals are trivially manufactured. Fake community members cost pennies. KYC documents can be falsified. Social proof is the easiest thing to fake in crypto.

Neither approach looks at the thing that actually matters: who is behind this token, and what is their history of on-chain behaviour?

How ChainAware Solves It¶

ChainAware's Predictive Rug Pull Detector analyses the behavioural history of the wallets involved in a token's creation and liquidity provision — not the contract code, and not the social media.

What We Analyse¶

Deployer Wallet History
- Has this wallet deployed tokens before? If so, what happened to them?
- Are there patterns of rapid LP withdrawal, coordinated selling, or token abandonment in the deployer's history?
- Is this a fresh wallet with no history — itself a significant red flag for new pools?

LP Wallet Concentration
- How concentrated is liquidity provision? A pool where two wallets control 80% of LP is extremely vulnerable to a coordinated pull.
- Are LP wallets connected by transaction history in ways that suggest coordination?

Counterparty Network Analysis
- Are the deployer or LP wallets connected (directly or through intermediaries) to wallets previously associated with known rug pulls?
- Do the funding sources of the deployer wallet trace back to suspicious origins?

Behavioural Pattern Matching
- Does the timing, sequence, and structure of this deployment match patterns seen in previous rug pulls in our training dataset?

The Result¶

A rug pull risk score from 0–100, delivered in under 100ms via API or the ChainAware app, accompanied by a human-readable breakdown of the key risk factors driving the score.

68% detection accuracy on new pools within the first hour of launch — before any social signals exist, before influencer promotion, before the community forms.

Key Metric¶

68% of genuine rug pulls are identified before liquidity is removed, based on ChainAware's analysis of PancakeSwap pool data. For comparison, relying on social signals (Telegram activity, Twitter mentions) typically catches 10–15% of rug pulls — mostly after the price has already collapsed.

Products for Rug Pull Prevention¶

For Individual Users: Rug Pull Detector¶

Available free at chainaware.ai:

Paste a token contract address or liquidity pool address
ChainAware analyses the deployer and LP wallets against 14M+ behavioural profiles
Receive a risk score, key risk factors, and a recommendation — in seconds

No wallet connection required. No signup. Works on BNB Smart Chain, Ethereum, and Polygon.

For Protocols and AI Agents: Prediction MCP¶

The open-source Prediction MCP brings rug pull screening directly into AI agent workflows:

Tool: predictive_rug_pull
Input: { "pool_address": "0x...", "chain": "bsc" }
Output: { "risk_score": 87, "risk_level": "HIGH", "key_factors": [...] }

Integrate rug pull scoring into:
- Automated listing screening for DEXs
- AI trading agents that execute token purchases
- Telegram bots that screen links shared in your community
- DeFi protocol onboarding flows

How to Use It¶

Individual User Flow¶

Find a new token you're considering investing in
Copy the contract address or liquidity pool address
Go to chainaware.ai and paste it into the Rug Pull Detector
Review the risk score and key factors
Make an informed decision — and share the result with your community

Protocol / API Flow¶

Register for API access at chainaware.ai/support
Integrate the rug pull endpoint: GET /v1/rugpull/{pool_address}?chain={chain}
Gate listing decisions, display risk warnings, or automate rejection of high-risk pools
Or use the Prediction MCP for AI-agent-native integration

API Documentation →

Try Rug Pull Detector Free¶

No signup. No wallet connection. Results in seconds.

Check a Token Now at chainaware.ai →