P2Patch: Exploit-Guided Vulnerability Repair
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Abstract (arXiv)
Large language models are increasingly deployed as autonomous agents for automated program repair, patching software vulnerabilities through multi-step reasoning and tool use. Yet these systems typically validate a patch against the same evidence used to construct it -- a static alert, sanitizer trace, published exploit, or test suite. A patch that blocks that one piece of evidence can therefore look successful while remaining vulnerable to the same vulnerability with some variation. We present P2Patch, a generative adversarial repair system that treats each patch as a new attack target. Starting from a normalized vulnerability finding, an exploitation agent constructs a reproducing proof of vulnerability (PoV), a patching agent repairs the program, and the exploitation agent then attacks the patched program for a distinct same-vulnerability exploit. Every successful exploit becomes a permanent repair requirement, and the final patch must block all PoVs accumulated during the patching process, with construction-time PoVs kept strictly separate from held-out evaluation oracles. Across 101 C/C++ and Java CVE subjects, this adversarial loop surfaces a distinct confirmed exploit in 41% of runs after the patch has already blocked its original construction-time PoV. Against single-pass repair from the same finding using Claude Code, P2Patch raises held-out fix-closed coverage from 78.1% to 84.7% and beyond-upstream coverage from 42.1% to 62.5%. Under the same held-out oracles, it also certifies more attack paths as blocked than recent state-of-the-art agentic repair systems on overlapping subjects. These results show that adversarial testing and independent security oracles expose repair gaps that one-shot validation misses.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 79 references that are in the map. First posted on arXiv 14 Oct 2026.
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References · 27
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- 2025From CVE Entries to Verifiable Exploits: An Automated Multi-Agent Framework for Reproducing CVEscited by 18
15 more
- 2017Identifying Patch Correctness in Test-Based Program Repaircited by 17
- 2022Detecting False Alarms from Automatic Static Analysis Tools: How Far are We?cited by 17
- 2019Automated Patch Assessment for Program Repair at Scalecited by 14
- 2024Are Large Language Models Memorizing Bug Benchmarks?cited by 12
- 2025SoK: Automated Vulnerability Repair: Methods, Tools, and Assessmentscited by 12
- 2025FaultLine: Automated Proof-of-Vulnerability Generation Using LLM Agentscited by 12
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- 2026PoC-Gym: Towards More Reliable LLM-Assisted Proof-of-Concept Exploit Generationcited by 2
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- 2025AutoPatch: Multi-Agent Framework for Patching Real-World CVE Vulnerabilities
- 2026Patch2Vuln: Agentic Reconstruction of Vulnerabilities from Linux Distribution Binary Patches
- 2026ContraFix: Skill-Enhanced Contrastive Runtime Analysis for Vulnerability Repair
- 2025Red Teaming Program Repair Agents: When Correct Patches can Hide Vulnerabilities
- 2026Vul4Py: Benchmarking Automated Vulnerability Repair in Python with Paired Exploit and Functional Oracles