{"repo":"AmirhosseinHonardoust/How-AI-Detects-Rugpulls","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/How-AI-Detects-Rugpulls","clone":"git clone https://github.com/AmirhosseinHonardoust/How-AI-Detects-Rugpulls.git","description":"A deep technical article exploring how AI, feature engineering, and static smart-contract analysis uncover rugpull risks before humans detect them. Covers Solidity pattern mining, mint abuse detection, blacklist/fee manipulation signals, ML-inspired scoring models, and how to quantify ERC-20 token scam probability.","language":null,"stars":20,"topics":["ai-security","blockchain-security","cybersecurity","data-science","decentralized-finance","defi-security","erc20","feature-engineering","machine-learning","risk-scoring"],"license":"MIT","category":"security-tools","readme_excerpt":"How AI Detects Rugpulls Before Humans Notice: Inside Token Risk Scoring A Deep Dive into Feature Engineering, Security Patterns, and ML-Inspired Analysis for ERC-20 Token Scam Detection --- Introduction Rugpulls are one of the most common and devastating attack patterns in the Web3 ecosystem. A malicious token is deployed, marketed, pumped, and at the moment of maximum hype: the owner mints infinite tokens, or enables a 99% tax, or freezes trading, or blacklists all users, or drains liquidity and the project collapses in minutes. The challenge is most of these risks are hidden inside the token’s Solidity code , invisible to non-technical buyers, and sometimes even invisible to developers who don’t carefully read the contract. This is where AI-inspired, feature-based risk scoring enters the scene. Instead of reading the code manually, we can teach Python to analyze Solidity , extract behavioral signals, convert them into numeric features, and generate an interpretable risk score that reflects the likelihood of malicious behavior. This article explains exactly how that works, step by step. --- 1. Rugpull Patterns Are Predictable Despite creativity among scammers, rugpull contracts share consistent structural behaviors , such as: 1. Owner minting The owner can mint unlimited supply. 2. Fee manipulation The owner can dynamically change tax rates. 3. Blacklists Restrict who can transfer tokens (or just block sellers). 4. Trading control Owner decides when trading is open. 5. maxTx","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/How-AI-Detects-Rugpulls/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}