SoK: Automated Vulnerability Repair: Methods, Tools, and Assessments
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Abstract (arXiv)
The increasing complexity of software has led to the steady growth of vulnerabilities. Vulnerability repair investigates how to fix software vulnerabilities. Manual vulnerability repair is labor-intensive and time-consuming because it relies on human experts, highlighting the importance of Automated Vulnerability Repair (AVR). In this SoK, we present the systematization of AVR methods through the three steps of AVR workflow: vulnerability analysis, patch generation, and patch validation. We assess AVR tools for C/C++ and Java programs as they have been widely studied by the community. Since existing AVR tools for C/C++ programs are evaluated with different datasets, which often consist of a few vulnerabilities, we construct the first C/C++ vulnerability repair benchmark dataset, dubbed Vul4C, which contains 144 vulnerabilities as well as their exploits and patches. We use Vul4C to evaluate seven AVR tools for C/C++ programs and use the third-party Vul4J dataset to evaluate two AVR tools for Java programs. We also discuss future research directions.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2025.
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Citing papers per year: 2026: 11.
11 papers.
- 2026CVE-Factory: Scaling Expert-Level Agentic Tasks for Code Security Vulnerabilitycited by 5
- 2026EviACT: An Evidence-to-Action Framework for Agentic Program Repaircited by 3
- 2026PatchIsland: Orchestration of LLM Agents for Continuous Vulnerability Repaircited by 2
- 2026Why LLMs Fail: A Failure Analysis and Partial Success Measurement for Automated Security Patch Generationcited by 1
- 2026MCP-in-SoS: Risk assessment framework for open-source MCP serverscited by 1
- 2026Beyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysiscited by 1
- 2026MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repaircited by 0
- 2026ContraFix: Skill-Enhanced Contrastive Runtime Analysis for Vulnerability Repaircited by 0
- 2026SoK: AI Secure Code Generation: Progress, Pitfalls, and Paths Forwardcited by 0
- 2026Knowledge-Enhanced Agentic Vulnerability Repaircited by 0
- 2026PatchBench: Evaluating AI Agents for Vulnerability Patchingcited by 0
- 2026PatchBench: Evaluating AI Agents for Vulnerability Patching3 Sep 2026
- 2026Beyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysis7 Jul 2026
- 2026Knowledge-Enhanced Agentic Vulnerability Repair1 Jul 2026
- 2026SoK: AI Secure Code Generation: Progress, Pitfalls, and Paths Forward23 Jun 2026
- 2026EviACT: An Evidence-to-Action Framework for Agentic Program Repair26 May 2026
- 2026ContraFix: Skill-Enhanced Contrastive Runtime Analysis for Vulnerability Repair17 May 2026
- 2026MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repair17 May 2026
- 2026MCP-in-SoS: Risk assessment framework for open-source MCP servers10 Mar 2026
- 2026Why LLMs Fail: A Failure Analysis and Partial Success Measurement for Automated Security Patch Generation10 Mar 2026
- 2026CVE-Factory: Scaling Expert-Level Agentic Tasks for Code Security Vulnerability3 Feb 2026
- 2026PatchIsland: Orchestration of LLM Agents for Continuous Vulnerability Repair24 Jan 2026