Mystique: Automated Vulnerability Patch Porting with Semantic and Syntactic-Enhanced LLM

Branching repositories facilitates efficient software development but can also inadvertently propagate vulnerabilities. When an original branch is patched, other unfixed branches remain vulnerable unless the patch is successfully ported. However, due to inherent discrepancies between branches, many patches cannot be directly applied and require manual intervention, which is time-consuming and leads to delays in patch porting, increasing vulnerability risks. Existing automated patch porting approaches are prone to errors, as they often overlook essential semantic and syntactic context of vulnerability and fail to detect or refine faulty patches. We propose Mystique, a novel LLM-based approach to address these limitations. Mystique first slices the semantic-related statements linked to the vulnerability while ensuring syntactic correctness, allowing it to extract the signatures for both the original patched function and the target vulnerable function. Mystique then utilizes a fine-tuned LLM to generate a fixed function, which is further iteratively checked and refined to ensure successful porting. Our evaluation shows that Mystique achieved a success rate of 0.954 at function level and of 0.924 at CVE level, outperforming state-of-the-art approaches by at least 13.2% at function level and 12.3% at CVE level. Our evaluation also demonstrates Mystique’s superior generality across various projects, bugs, and programming languages. Mystique successfully ported patches for 34 real-world vulnerable branches.

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Publication Details

Journal
Proceedings of the ACM on software engineering.
Published
2025-06-19
DOI
https://doi.org/10.1145/3715718
Primary Topic
Software Engineering Research
Type
article
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article

Mystique: Automated Vulnerability Patch Porting with Semantic and Syntactic-Enhanced LLM

Ruisi Wang, Bihuan Chen, Susheng Wu, Yiheng Huang et al.
Proceedings of the ACM on software engineering.
Software Engineering Research
article

Mystique: Automated Vulnerability Patch Porting with Semantic and Syntactic-Enhanced LLM

Ruisi Wang, Bihuan Chen, Susheng Wu, Yiheng Huang, Yuhua Cao, Xin Peng, J. Zhao, Zhuotong Zhou
article en

Abstract

Branching repositories facilitates efficient software development but can also inadvertently propagate vulnerabilities. When an original branch is patched, other unfixed branches remain vulnerable unless the patch is successfully ported. However, due to inherent discrepancies between branches, many patches cannot be directly applied and require manual intervention, which is time-consuming and leads to delays in patch porting, increasing vulnerability risks. Existing automated patch porting approaches are prone to errors, as they often overlook essential semantic and syntactic context of vulnerability and fail to detect or refine faulty patches. We propose Mystique, a novel LLM-based approach to address these limitations. Mystique first slices the semantic-related statements linked to the vulnerability while ensuring syntactic correctness, allowing it to extract the signatures for both the original patched function and the target vulnerable function. Mystique then utilizes a fine-tuned LLM to generate a fixed function, which is further iteratively checked and refined to ensure successful porting. Our evaluation shows that Mystique achieved a success rate of 0.954 at function level and of 0.924 at CVE level, outperforming state-of-the-art approaches by at least 13.2% at function level and 12.3% at CVE level. Our evaluation also demonstrates Mystique’s superior generality across various projects, bugs, and programming languages. Mystique successfully ported patches for 34 real-world vulnerable branches.

Proceedings of the ACM on software engineering.Vol. 2(FSE)
Fudan University (CN)
Openalex Percentile: Top 80%
Software Engineering Research
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Mystique: Automated Vulnerability Patch Porting with Semantic and Syntactic-Enhanced LLM — Ruisi Wang, Bihuan Chen, et al. · Proceedings of the ACM on software engineering. (2025) | TGRS Research Map | TGRS