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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2504.11711 |
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| _version_ | 1866913868493619200 |
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| author | Li, Haonan Zhang, Hang Pei, Kexin Qian, Zhiyun |
| author_facet | Li, Haonan Zhang, Hang Pei, Kexin Qian, Zhiyun |
| contents | Static analysis plays a crucial role in software vulnerability detection, yet faces a persistent precision-scalability tradeoff. In large codebases like the Linux kernel, traditional static analysis tools often generate excessive false positives due to simplified vulnerability modeling and overapproximation of path and data constraints. While large language models (LLMs) demonstrate promising code understanding capabilities, their direct application to program analysis remains unreliable due to inherent reasoning limitations.
We introduce BugLens, a post-refinement framework that significantly enhances static analysis precision for bug detection. BugLens guides LLMs through structured reasoning steps to assess security impact and validate constraints from the source code. When evaluated on Linux kernel taint-style bugs detected by static analysis tools, BugLens improves precision approximately 7-fold (from 0.10 to 0.72), substantially reducing false positives while uncovering four previously unreported vulnerabilities. Our results demonstrate that a well-structured, fully automated LLM-based workflow can effectively complement and enhance traditional static analysis techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11711 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Hitchhiker's Guide to Program Analysis, Part II: Deep Thoughts by LLMs Li, Haonan Zhang, Hang Pei, Kexin Qian, Zhiyun Software Engineering Artificial Intelligence Static analysis plays a crucial role in software vulnerability detection, yet faces a persistent precision-scalability tradeoff. In large codebases like the Linux kernel, traditional static analysis tools often generate excessive false positives due to simplified vulnerability modeling and overapproximation of path and data constraints. While large language models (LLMs) demonstrate promising code understanding capabilities, their direct application to program analysis remains unreliable due to inherent reasoning limitations. We introduce BugLens, a post-refinement framework that significantly enhances static analysis precision for bug detection. BugLens guides LLMs through structured reasoning steps to assess security impact and validate constraints from the source code. When evaluated on Linux kernel taint-style bugs detected by static analysis tools, BugLens improves precision approximately 7-fold (from 0.10 to 0.72), substantially reducing false positives while uncovering four previously unreported vulnerabilities. Our results demonstrate that a well-structured, fully automated LLM-based workflow can effectively complement and enhance traditional static analysis techniques. |
| title | The Hitchhiker's Guide to Program Analysis, Part II: Deep Thoughts by LLMs |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2504.11711 |