The Web3 Security Resources Hub is a comprehensive collection of curated tools, guides, and best practices for securing decentralized systems and smart contracts in the blockchain space.

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.

A complete Web3 security toolkit combining AI-powered token auditing, ML-based deployer reputation scoring, and live Etherscan V2 data. Includes static analysis for rugpull detection, RandomForest reputation modeling, contract-fetching automation, and Solidity on-chain registries for transparent, reproducible security insights.

A hybrid Solidity + Python security toolkit that analyzes ERC-20 token contracts using static pattern extraction and ML-inspired scoring. Detects mint backdoors, blacklist controls, fee manipulation, trading locks, and rugpull mechanics. Outputs interpretable risk scores, labels, and structured features for deeper analysis.

AI-powered real-time smart contract scanner that connects Machine Learning with Etherscan V2 to analyze newly deployed contracts instantly. Fetches verified Solidity code, performs static risk analysis, computes ML-driven deployer trust scores, and generates full security intelligence pipelines for Web3 threat detection.

A deep technical exploration of how malicious smart-contract developers weaponize fee logic in ERC-20 tokens. Covers dynamic tax flipping, hidden sell traps, fee obfuscation, whitelist-based bypasses, liquidity-drain funnels, attack timelines, forensic analysis, mathematical modeling, and ML-powered detection strategies for tax abuse.

A research-grade framework for extracting, classifying, and analyzing the “genetic” behavior of smart contract tokens. Identifies economic traits, supply mutations, fee patterns, permission risks, upgradeability vectors, and scam species using a structured gene taxonomy with risk scoring, HTML reports, and token comparison tools.

Free honeypot token scanner for Ethereum, Polygon & Arbitrum. Detect scam tokens before you buy. Instant analysis of smart contracts using 13 specialized patterns. No API keys, no limits, 100% free. Built with Next.js 16 & Cloudflare Workers.

Cross-Contract Reentrancy PoC, a Foundry-based Solidity demo exploiting timing mismatch in DeFi Vault and ICOGov mint flow. Inspired by Inspex.

Learn Solana security by example. 5 critical vulnerabilities demonstrated with vulnerable code, exploits, and fixes. Includes account validation, authority checks, arithmetic safety, CPI re-entrancy, and privilege escalation patterns.

🛡️ Analyze risks in pump.fun tokens to detect malicious activities, enabling users to make informed decisions in a safer DeFi environment.

An interactive and educational platform designed to help users navigate and engage with Decentralized Finance (DeFi).

A decentralized lending platform built on the Stacks blockchain that enables users to deposit Bitcoin as collateral and borrow against it. The protocol implements automated liquidations, dynamic interest rates, and protocol-level security measures.

The Semantics of Collapse: Lawful Instability in Agentic Systems - A Safe-to-Exist Analysis of Optimization-Driven Systemic Risk

🧬 Explore tokenomics with Token-Genome, a framework for analyzing on-chain behavior and smart contract economic structures.

🛡️ Leverage AI to uncover hidden risks in ERC-20 tokens, detecting rugpulls before they harm investors. Analyze Solidity code for real-time threat assessment.

A comprehensive smart contract fuzz-testing tool with AI-assisted analysis. Combines Go-based security analysis with Claude AI for signature extraction and vulnerability detection.

🔍 Explore how developers misuse fee logic in smart contracts, uncovering methods of detection and modeling with machine learning to combat token tax abuse.

Real-time blockchain fraud detection using autonomous multi-agent AI swarms. Detect fraud in <2s with 150x faster vector search (HNSW), 84.8% accuracy, and 73% cost reduction. Features ERC-1155 trust scores, MCP integration, and reflexion learning.