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About this paper
Junyoung Park, Insu Yun
AFuzz searches for logic flaws by treating earlier bugs as lessons: agents analyze causes and test analogous mistakes elsewhere with code and runnable proofs of concept. In V8, it reported 40 findings, including three duplicates; two were assigned CVEs. It processed about 23.8% of collected seeds, and six findings require experimental flags, bounding the results to the evaluated search budget.
Key insights
- Historical bugs serve as valuable templates for root causes and exploitation mechanisms, guiding the search for new, similar vulnerabilities.
- Large Language Models (LLMs) possess the capability for complex, abstract reasoning and semantic understanding, crucial for identifying logic bugs across different implementations.
- Deep agents, powered by LLMs, should act as autonomous systems capable of multi-step planning and code manipulation, effectively mimicking human security auditors.
Summary by alphaXiv.
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