mlsanityData to 14 Oct 2026

Patch2Vuln: Agentic Reconstruction of Vulnerabilities from Linux Distribution Binary Patches

arXiv 2605.06601 ↗First posted 7 May 2026cs.CR cs.AIcited by 0

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

Security updates create a short but important window in which defenders and attackers can compare vulnerable and patched software. Yet in many operational settings, the most accessible artifacts are binary packages rather than source patches or advisory text. This paper asks whether a language-model agent, restricted to local binary-derived evidence, can reconstruct the security meaning of Linux distribution updates. Patch2Vuln is a local, resumable pipeline that extracts old/new ELF pairs, diffs them with Ghidra and Ghidriff, ranks changed functions, builds candidate dossiers, and asks an offline agent to produce a preliminary audit, bounded validation plan, and final audit. We evaluate Patch2Vuln on 25 Ubuntu `.deb` package pairs: 20 security-update pairs and five negative controls, all manually adjudicated against private source-patch and binary-function ground truth. The agent localizes a verified security-relevant patch function in 10 of 20 security pairs and assigns an accepted final root-cause class in 11 of 20. Oracle diagnostics show that six security pairs fail before model reasoning because the binary differ or ranker omits the right function, with one additional context-export miss. A separate bounded validation pass produces two target-level minimized behavioral old/new differentials, both for tcpdump, but no crash, timeout, sanitizer finding, or memory-corruption proof; all five negative controls are classified as unknown and produce no validation differentials. These results support agentic vulnerability reconstruction from binary patches as a useful research target while showing that binary-diff coverage and local behavioral validation remain the limiting components.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 8 of its 31 references that are in the map. First posted on arXiv 7 May 2026.

Builds on

8 of 31 references.

Closest ancestors · 5

Same direction, newest first

  1. 2026Towards Optimal Agentic Architectures for Offensive Security Tasks20 Apr
  2. 2025Multi-Agent Penetration Testing AI for the Web28 Aug
  3. 2024EnIGMA: Interactive Tools Substantially Assist LM Agents in Finding Security Vulnerabilities24 Sep
  4. 2024Teams of LLM Agents can Exploit Zero-Day Vulnerabilities2 Jun
  5. 2024LLM Agents can Autonomously Exploit One-day Vulnerabilities11 Apr

Other references · 3

  1. 2023PentestGPT: An LLM-empowered Automatic Penetration Testing Toolcited by 97
  2. 2017Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detectioncited by 65
  3. 2018SAFE: Self-Attentive Function Embeddings for Binary Similaritycited by 33

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  1. 2026Patch-to-PoC: A Systematic Study of Agentic LLM Systems for Linux Kernel N-Day Reproduction
  2. 2025From LLMs to Agents: A Comparative Evaluation of LLMs and LLM-based Agents in Security Patch Detection
  3. 2026Vulnerability Localization Benchmark: Measuring Agentic Security Analysis at Repository Scale
  4. 2026VeriPort: Automated and Verified Patch Backporting at Scale
  5. 2026Veritas: Grounding LLM Agents for Reliable Vulnerability Reasoning over Stripped Binaries
  6. 2025Benchmarking LLMs and LLM-based Agents in Practical Vulnerability Detection for Code Repositories
  7. 2026Favia: Forensic Agent for Vulnerability-fix Identification and Analysis
  8. 2026SyzHarness: Patch-Based Kernel Bug Reproduction with LLM-Synthesized Fuzzing Harnesses