RecurScan: Detecting Recurring Vulnerabilities in PHP Web Applications
Youkun Shi, Yuan Zhang, Tianhao Bai, Lei Zhang, Xin Tan, Min Yang
摘要
Detecting recurring vulnerabilities has become a popular means of static vulnerability detection in recent years because they do not require labor-intensive vulnerability modeling. Recently, a body of work, with HiddenCPG as a representative, has redefined the problem of statically identifying recurring vulnerabilities as the subgraph isomorphism problem. More specifically, these approaches represent known vulnerable code as graph-based structures (e.g., PDG or CPG), and then identify subgraphs within target applications that match the vulnerable graphs. However, since these methods are highly sensitive to changes in the code graph, they may miss a significant number of recurring vulnerabilities with slight code differences from known vulnerabilities. In this paper, we propose a novel approach, namely RecurScan, which can accurately detect recurring vulnerabilities with resilience to code differences. To achieve this goal, RecurScan works around security patches and symbolic tracking techniques, detecting recurring vulnerabilities by comparing symbolic expressions and selective constraints between the target applications and known vulnerabilities. Benefiting from this design, RecurScan can tolerate the code differences arising from complex data or control flows within the applications. We evaluated RecurScan on 200 popular PHP web applications using 184 known vulnerability patches. The results demonstrate that RecurScan discovered 232 previously unknown vulnerabilities, 174 of which were assigned CVE identifiers, outperforming state-of-the-art approach (i.e., HiddenCPG) by 25.98% in precision and 87.09% in recall. CCS CONCEPTS • Security and privacy → Web application security.
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引用它的顶会 Paper5
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- Enhancing Security in Third-Party Library Reuse - Comprehensive Detection of 1-day Vulnerability through Code Patch AnalysisShangzhi Xu, Jialiang Dong, Weiting Cai, Juanru Li 等NDSS 2025
- XSSky: Detecting XSS Vulnerabilities through Local Path-Persistent FuzzingYoukun Shi, Yuan Zhang, Tianhao Bai, Feng Xue 等USENIX Security 2025
- ZIPPER: Static Taint Analysis for PHP Applications with Precision and EfficiencyXinyi Wang, Yeting Li, Jie Lu, Shizhe Cui 等USENIX Security 2025
- VulPA: Detecting Semantically Recurring Vulnerabilities with Multi-object Typestate AnalysisLiqing Cao, Haofeng Li, Chenghang Shi, Jie Lu 等FSE 2025
它引用的顶会 Paper11
- VUDDY: A Scalable Approach for Vulnerable Code Clone DiscoverySeulbae Kim, Seunghoon Woo, Heejo Lee, Hakjoo OhS&P 2017 · 被引用 388 次
- Deemon: Detecting CSRF with Dynamic Analysis and Property GraphsGiancarlo Pellegrino, Martin Johns, Simon Koch, Michael Backes 等CCS 2017 · 被引用 74 次
- Black Widow: Blackbox Data-driven Web ScanningBenjamin Eriksson, Giancarlo Pellegrino, Andrei SabelfeldS&P 2021 · 被引用 65 次
- Link: Black-Box Detection of Cross-Site Scripting Vulnerabilities Using Reinforcement LearningSoyoung Lee, Seongil Wi, Sooel SonWWW 2022 · 被引用 34 次
- HiddenCPG: Large-Scale Vulnerable Clone Detection Using Subgraph Isomorphism of Code Property GraphsSeongil Wi, Sijae Woo, Joyce Jiyoung Whang, Sooel SonWWW 2022 · 被引用 27 次
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