VulWeaver: Weaving Broken Semantics for Grounded Vulnerability Detection
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917401726025728 |
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| author | Cao, Yiheng Chen, Yihao Hu, Xin Chen, Bihuan Deng, Jiayi Zhou, Zhuotong Wu, Susheng Huang, Yiheng Du, Xueying Chen, Xingman Li, Miaohua Peng, Xin |
| author_facet | Cao, Yiheng Chen, Yihao Hu, Xin Chen, Bihuan Deng, Jiayi Zhou, Zhuotong Wu, Susheng Huang, Yiheng Du, Xueying Chen, Xingman Li, Miaohua Peng, Xin |
| contents | Detecting vulnerabilities in source code remains critical yet challenging, as conventional static analysis tools construct inaccurate program representations, while existing LLM-based approaches often miss essential vulnerability context and lack grounded reasoning. To mitigate these challenges, we introduce VulWeaver, a novel LLM-based approach that weaves broken program semantics into accurate representations and extracts holistic vulnerability context for grounded vulnerability detection. Specifically, VulWeaver first constructs an enhanced unified dependency graph (UDG) by integrating deterministic rules with LLM-based semantic inference to address static analysis inaccuracies. It then extracts holistic vulnerability context by combining explicit contexts from program slicing with implicit contexts, including usage, definition, and declaration information. Finally, VulWeaver employs meta-prompting with vulnerability type specific expert guidelines to steer LLMs through systematic reasoning, aggregated via majority voting for robustness. Extensive experiments on PrimeVul4J dataset have demonstrated that VulWeaver achieves a F1-score of 0.75, outperforming state-of-the-art learning-based, LLM-based, and agent-based baselines by 23%, 15%, and 60% in F1-score, respectively. VulWeaver has also detected 26 true vulnerabilities across 9 realworld Java projects, with 15 confirmed by developers and 5 CVE identifiers assigned. In industrial deployment, VulWeaver identified 40 confirmed vulnerabilities in an internal repository. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10767 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VulWeaver: Weaving Broken Semantics for Grounded Vulnerability Detection Cao, Yiheng Chen, Yihao Hu, Xin Chen, Bihuan Deng, Jiayi Zhou, Zhuotong Wu, Susheng Huang, Yiheng Du, Xueying Chen, Xingman Li, Miaohua Peng, Xin Software Engineering Detecting vulnerabilities in source code remains critical yet challenging, as conventional static analysis tools construct inaccurate program representations, while existing LLM-based approaches often miss essential vulnerability context and lack grounded reasoning. To mitigate these challenges, we introduce VulWeaver, a novel LLM-based approach that weaves broken program semantics into accurate representations and extracts holistic vulnerability context for grounded vulnerability detection. Specifically, VulWeaver first constructs an enhanced unified dependency graph (UDG) by integrating deterministic rules with LLM-based semantic inference to address static analysis inaccuracies. It then extracts holistic vulnerability context by combining explicit contexts from program slicing with implicit contexts, including usage, definition, and declaration information. Finally, VulWeaver employs meta-prompting with vulnerability type specific expert guidelines to steer LLMs through systematic reasoning, aggregated via majority voting for robustness. Extensive experiments on PrimeVul4J dataset have demonstrated that VulWeaver achieves a F1-score of 0.75, outperforming state-of-the-art learning-based, LLM-based, and agent-based baselines by 23%, 15%, and 60% in F1-score, respectively. VulWeaver has also detected 26 true vulnerabilities across 9 realworld Java projects, with 15 confirmed by developers and 5 CVE identifiers assigned. In industrial deployment, VulWeaver identified 40 confirmed vulnerabilities in an internal repository. |
| title | VulWeaver: Weaving Broken Semantics for Grounded Vulnerability Detection |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2604.10767 |