mlsanityData to 14 Oct 2026

A Dual-Loop Agent Framework for Automated Vulnerability Reproduction

arXiv 2602.05721 ↗First posted 5 Feb 2026cs.SEcited by 1

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

Abstract (arXiv)

Automated vulnerability reproduction from CVE descriptions requires generating executable Proof-of-Concept (PoC) exploits and validating them in target environments. This process is critical in software security research and practice, yet remains time-consuming and demands specialized expertise when performed manually. While LLM agents show promise for automating this task, existing approaches often conflate exploring attack directions with fixing implementation details, which leads to unproductive debugging loops when reproduction fails. To address this, we propose CVE2PoC, an LLM-based dual-loop agent framework following a plan-execute-evaluate paradigm. The Strategic Planner analyzes vulnerability semantics and target code to produce structured attack plans. The Tactical Executor generates PoC code and validates it through progressive verification. The Adaptive Refiner evaluates execution results and routes failures to different loops: the Tactical Loop for code-level refinement, while the Strategic Loop for attack strategy replanning. This dual-loop design enables the framework to escape ineffective debugging by matching remediation to failure type. Evaluation on two benchmarks covering 617 real-world vulnerabilities demonstrates that CVE2PoC achieves 82.9% and 54.3% reproduction success rates on SecBench.js and PatchEval, respectively, outperforming the best baseline by 11.3% and 20.4%. Human evaluation confirms that generated PoCs achieve comparable code quality to human-written exploits in readability and reusability.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 24 of its 51 references that are in the map. First posted on arXiv 5 Feb 2026.

Builds on

24 of 51 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025PoCo: Agentic Proof-of-Concept Exploit Generation for Smart Contracts4 Nov
  2. 2025AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents15 May
  3. 2025VulnBot: Autonomous Penetration Testing for A Multi-Agent Collaborative Framework23 Jan
  4. 2024Teams of LLM Agents can Exploit Zero-Day Vulnerabilities2 Jun
  5. 2024LLM Agents can Autonomously Exploit One-day Vulnerabilities11 Apr

Other references · 19

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  4. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
  5. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
  6. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
  7. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agentscited by 1,002
  8. 2023ExpeL: LLM Agents Are Experiential Learnerscited by 876
  9. 2020GraphCodeBERT: Pre-training Code Representations with Data Flowcited by 609
  10. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 560
  11. 2023PentestGPT: An LLM-empowered Automatic Penetration Testing Toolcited by 97
  12. 2023GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysiscited by 65
7 more
  1. 2024LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoningcited by 59
  2. 2022VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detectioncited by 51
  3. 2025LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insightscited by 47
  4. 2025A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applicationscited by 25
  5. 2025PATCHEVAL: A New Benchmark for Evaluating LLMs on Patching Real-World Vulnerabilitiescited by 21
  6. 2024RePair: Automated Program Repair with Process-based Feedbackcited by 16
  7. 2025CAI: An Open, Bug Bounty-Ready Cybersecurity AIcited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2025From CVE Entries to Verifiable Exploits: An Automated Multi-Agent Framework for Reproducing CVEs
  2. 2026CVE-Factory: Scaling Expert-Level Agentic Tasks for Code Security Vulnerability
  3. 2026A Multi-Agent Framework for Automated Exploit Generation with Constraint-Guided Comprehension and Reflection
  4. 2026Execution-State-Aware LLM Reasoning for Automated Proof-of-Vulnerability Generation
  5. 2026Program Analysis Guided LLM Agent for Proof-of-Concept Generation
  6. 2025AutoPatch: Multi-Agent Framework for Patching Real-World CVE Vulnerabilities
  7. 2026When Passing Tests Hides Vulnerabilities: An Empirical Study of Silent Failures in Agentic Systems
  8. 2025FaultLine: Automated Proof-of-Vulnerability Generation Using LLM Agents