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# Teaching AI to Find Real Vulnerabilities — David Brumley, Bugcrowd

**[AI Engineer](https://daily.dev/sources/aidotengineer)** · 27 min read · 1 upvotes · 2 comments

## Summary

David Brumley (CMU professor and Bugcrowd Chief AI Officer) presents a framework for training LLMs to find and exploit real vulnerabilities using reinforcement learning. He identifies critical flaws in existing cybersecurity benchmarks — primarily that programs with multiple vulnerabilities cause models to reward-hack by repeatedly finding the easiest bug. His solution is an 'audit task' approach using precision/recall scoring across all discovered vulnerabilities, with deterministic grading oracles instead of LLM-as-judge. He then shares results from testing frontier models (GPT, Mythos/Claude, Gemini) against 41 real V8 (Chrome) vulnerabilities, finding that top models achieved full sandbox escape and arbitrary code execution 68-73% of the time — including novel exploit paths not previously known publicly. The benchmark environments are available at exploitbench.ai as Docker images with MCP interfaces.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=ZFxh7sqbUZo>

## Community discussion

Top comments from developers on daily.dev.

**@agustinbarrientos** · 0 upvotes

> An LLM grading whether another LLM got code execution sounds far too optimistic.

**@trevorsuna** · 0 upvotes

> Precision and recall across every vulnerability feels much closer to a real audit than rewarding the first easy bug. Deterministic grading is especially important here; otherwise the judge becomes another attack surface for reward hacking.

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---

Tags: [#security](https://daily.dev/tags/security), [#llm](https://daily.dev/tags/llm), [#reinforcement-learning](https://daily.dev/tags/reinforcement-learning), [#v8](https://daily.dev/tags/v8)

[View this post on daily.dev](https://daily.dev/posts/teaching-ai-to-find-real-vulnerabilities-david-brumley-bugcrowd-vbz44q16c)

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