Project Details
BugBountyBot
Semi-autonomous bug bounty hunting with multi-agent AI
What I Built
Designed multi-agent architecture with checkpoint/resume, RAG database for learning, and safety-first approach to autonomous hunting
Key Outcomes
Tech Stack
Project Overview
A semi-autonomous bug bounty hunting system that assists security researchers in discovering vulnerabilities. Features a multi-agent architecture (Recon, Testing, Validator, Reporter) with evidence-gated progression, RAG-enhanced learning from past findings, and safety mechanisms like rate limiting and scope validation. Maintains human-in-the-loop control for all submissions while automating the tedious reconnaissance and testing phases.
Built For
Security researchers and bug bounty hunters
Tedious reconnaissance and testing phases consume 80% of hunting time
Key Features
Multi-Agent Architecture
Specialized agents for Recon, Testing, Validation, and Reporting
Evidence-Gated Progression
0.85+ confidence threshold before advancing findings
RAG-Enhanced Learning
SQLite database learns from past successes and failures
Safety Mechanisms
Rate limiting, scope validation, and ban detection
Challenge Solved
Building an autonomous system that finds real vulnerabilities while maintaining safety, compliance with bug bounty platforms, and human oversight for all submissions.
Key Learnings
- school Evidence-gating prevents false positive submissions that damage reputation
- school Human-in-the-loop is required for platform compliance, not optional