Bullying LLMs into Submission: Building an Autonomous AI-Powered Zero-Day Hunting Pipeline
This technical deep dive describes how a security researcher built an autonomous vulnerability discovery platform that combines Claude Code, Model Context Protocol (MCP), fuzzing, reverse engineering, retrieval-augmented generation (RAG), and custom tooling to identify zero-day vulnerabilities at scale. The article details a workflow that treats every AI-generated finding as a potential hallucination until validated through multiple verification gates, integrates historical knowledge and bug bounty intelligence to prioritize targets, and continuously improves through feedback from previous campaigns. The result is an AI-assisted vulnerability research system that reduces manual overhead while maintaining rigorous human validation to minimize false positives.
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