KNighter: Transforming Static Analysis with LLM-Synthesized Checkers
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912569186320384 |
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| author | Yang, Chenyuan Zhao, Zijie Xie, Zichen Li, Haoyu Zhang, Lingming |
| author_facet | Yang, Chenyuan Zhao, Zijie Xie, Zichen Li, Haoyu Zhang, Lingming |
| contents | Static analysis is a powerful technique for bug detection in critical systems like operating system kernels. However, designing and implementing static analyzers is challenging, time-consuming, and typically limited to predefined bug patterns. While large language models (LLMs) have shown promise for static analysis, directly applying them to scan large systems remains impractical due to computational constraints and contextual limitations.
We present KNighter, the first approach that unlocks scalable LLM-based static analysis by automatically synthesizing static analyzers from historical bug patterns. Rather than using LLMs to directly analyze massive systems, our key insight is leveraging LLMs to generate specialized static analyzers guided by historical patch knowledge. KNighter implements this vision through a multi-stage synthesis pipeline that validates checker correctness against original patches and employs an automated refinement process to iteratively reduce false positives. Our evaluation on the Linux kernel demonstrates that KNighter generates high-precision checkers capable of detecting diverse bug patterns overlooked by existing human-written analyzers. To date, KNighter-synthesized checkers have discovered 92 new, critical, long-latent bugs (average 4.3 years) in the Linux kernel; 77 are confirmed, 57 fixed, and 30 have been assigned CVE numbers. This work establishes an entirely new paradigm for scalable, reliable, and traceable LLM-based static analysis for real-world systems via checker synthesis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09002 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KNighter: Transforming Static Analysis with LLM-Synthesized Checkers Yang, Chenyuan Zhao, Zijie Xie, Zichen Li, Haoyu Zhang, Lingming Software Engineering Artificial Intelligence Cryptography and Security Operating Systems Static analysis is a powerful technique for bug detection in critical systems like operating system kernels. However, designing and implementing static analyzers is challenging, time-consuming, and typically limited to predefined bug patterns. While large language models (LLMs) have shown promise for static analysis, directly applying them to scan large systems remains impractical due to computational constraints and contextual limitations. We present KNighter, the first approach that unlocks scalable LLM-based static analysis by automatically synthesizing static analyzers from historical bug patterns. Rather than using LLMs to directly analyze massive systems, our key insight is leveraging LLMs to generate specialized static analyzers guided by historical patch knowledge. KNighter implements this vision through a multi-stage synthesis pipeline that validates checker correctness against original patches and employs an automated refinement process to iteratively reduce false positives. Our evaluation on the Linux kernel demonstrates that KNighter generates high-precision checkers capable of detecting diverse bug patterns overlooked by existing human-written analyzers. To date, KNighter-synthesized checkers have discovered 92 new, critical, long-latent bugs (average 4.3 years) in the Linux kernel; 77 are confirmed, 57 fixed, and 30 have been assigned CVE numbers. This work establishes an entirely new paradigm for scalable, reliable, and traceable LLM-based static analysis for real-world systems via checker synthesis. |
| title | KNighter: Transforming Static Analysis with LLM-Synthesized Checkers |
| topic | Software Engineering Artificial Intelligence Cryptography and Security Operating Systems |
| url | https://arxiv.org/abs/2503.09002 |