Teaching AI to Find Real Vulnerabilities — Prof. David Brumley, Bugcrowd
Aug 1, 2026 · 27:17
Carnegie Mellon professor and Bugcrowd chief AI officer David Brumley argues that teaching AI to hack mirrors human learning: a ladder from crashes to arbitrary code execution, graded by deterministic oracles rather than LLM judges. He shows why benchmarks fail when targets hold multiple vulnerabilities — models reward-hack the easiest bug — and presents his 'audit task' scoring precision and recall across all discovered bugs. Testing on Chrome's V8 with 41 real vulnerabilities, MITHOS hit 73% full code execution and GPT 68%, while Gemini and Kimi scored 0%. Several exploits were novel: MITHOS reverse-engineered Math.random to forge a pointer, found a new WASM path, and produced a real zero day. The episode grounds this in stories from picoCTF winner Fluorescence to DARPA's Cyber Grand Challenge, urging RL environments built on real bugs over benchmark-maxxing security.
