Open Source Claude Code Tools for Automated Bug Bounty Hunting
Three open source repositories turn Claude Code into an automated bug bounty hunting pipeline. The tools cover both web2 and web3 security testing, running entirely within Claude Code conversations.
Key Components
The trilogy consists of three interconnected repositories:
- claude-bug-bounty: You point it at a target and Claude handles recon, maps the attack surface, runs scanners for IDOR, SSRF, XSS, SQLi, OAuth, GraphQL, race conditions, and LLM injection. It walks you through a 4-gate validation checklist, then writes a submission-ready HackerOne or Bugcrowd report.
- web3-bug-bounty-hunting-ai-skills: Focuses on smart contract security, covering 10 bug classes including reentrancy, flash loan attacks, oracle manipulation, and access control issues. Includes Foundry PoC templates and real Immunefi case studies so Claude understands what paid bugs look like.
- public-skills-builder: Feed it 500 disclosed reports from HackerOne or GitHub writeups and it generates structured skill files, one per vulnerability class, ready to load into Claude Code. No private reports needed.
How They Work Together
The three repos function as a pipeline: public-skills-builder builds the knowledge base, web3 repo provides smart contract context, and claude-bug-bounty runs the actual hunting operations. All tools are free and open source, available on GitHub.
The creator is open to contributions for adding scanners or Claude prompt templates. The tools aim to automate manual recon, scanning, and report writing processes that security researchers typically perform manually.
📖 Read the full source: r/ClaudeAI
👀 See Also
Bullshit Benchmark Tests LLM Resistance to Nonsensical Prompts
The Bullshit Benchmark evaluates whether AI models identify and push back on obvious nonsense prompts instead of confidently generating incorrect answers. Results show Claude models perform significantly better than Gemini models at detecting nonsensical questions.
Brunnfeld Agentic World: Multi-Agent Medieval Economy Simulation Without Behavioral Prompts
A TypeScript simulation where 20 LLM agents autonomously trade in a medieval village economy with no behavioral instructions, goals, or trading strategies. Agents receive ~200 token perceptions each tick and interact through a deterministic engine handling physics, recipes, and market mechanics.
Red Queen: A Deterministic Orchestrator That Runs Claude Code as a Worker Pool
Red Queen uses a state machine to orchestrate Claude Code subprocesses, eliminating LLM routing errors and token waste from mega-prompts.
Auto-co: A 50-Line Bash Script That Turns Claude Code Into Autonomous AI Companies
Auto-co is a 50-line bash script that wraps the Claude Code CLI in a loop, allowing it to run autonomously with 14 AI agents playing roles like CEO, engineer, and critic. It has built four products from scratch, including FormReply and Changelog.dev, at a total cost of $268 across 270+ cycles.