PoCGen: Generating Proof-of-Concept Exploits for Vulnerabilities in Npm Packages
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Abstract (arXiv)
Security vulnerabilities in software packages are a significant concern for developers and users alike. Patching these vulnerabilities in a timely manner is crucial to restoring the integrity and security of software systems. However, previous work has shown that vulnerability reports often lack proof-of-concept (PoC) exploits, which are essential for fixing the vulnerability, testing patches, and avoiding regressions. Creating a PoC exploit is challenging because vulnerability reports are informal and often incomplete, and because it requires a detailed understanding of how inputs passed to potentially vulnerable APIs may reach security-relevant sinks. In this paper, we present PoCGen, a novel approach to autonomously generate and validate PoC exploits for vulnerabilities in npm packages. The approach is the first to address this task by combining the complementary strengths of large language models (LLMs), e.g., to understand informal vulnerability reports, with static analysis, e.g., to identify taint paths, and dynamic analysis, e.g., to validate generated exploits. PoCGen successfully generates exploits for 77% of the vulnerabilities in the SecBench.js dataset. This success rate significantly outperforms a recent baseline (by 45 absolute percentage points), while imposing an average cost of only 0.02 per generated exploit. Moreover, PoCGen generates six successful exploits for recent real-world vulnerabilities, five of which are now included in their respective vulnerability reports.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Jun 2025.
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Citing papers per year: 2025: 1; 2026: 11.
12 papers.
- 2025FaultLine: Automated Proof-of-Vulnerability Generation Using LLM Agentscited by 12
- 2026SEC-bench Pro: Can Language Models Solve Long-Horizon Software Security Tasks?cited by 9
- 2026Patch-to-PoC: A Systematic Study of Agentic LLM Systems for Linux Kernel N-Day Reproductioncited by 7
- 2026AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detectioncited by 4
- 2026Execution-State-Aware LLM Reasoning for Automated Proof-of-Vulnerability Generationcited by 3
- 2026Program Analysis Guided LLM Agent for Proof-of-Concept Generationcited by 2
- 2026PoC-Adapt: Semantic-Aware Automated Vulnerability Reproduction with LLM Multi-Agents and Reinforcement Learning-Driven Adaptive Policycited by 1
- 2026AutoEG: Exploiting Known Third-Party Vulnerabilities in Black-Box Web Applicationscited by 0
- 2026uGen: An Agentic Framework for Generating Microarchitectural Attack PoCscited by 0
- 2026ContraFix: Skill-Enhanced Contrastive Runtime Analysis for Vulnerability Repaircited by 0
- 2026FORGE: Multi-Agent Graduated Exploitation and Detection Engineeringcited by 0
- 2026WitnessGym: Benchmarking Coding Agents on the Construction of Bug Witnessescited by 0
- 2026WitnessGym: Benchmarking Coding Agents on the Construction of Bug Witnesses29 Sep 2026
- 2026FORGE: Multi-Agent Graduated Exploitation and Detection Engineering2 Jun 2026
- 2026SEC-bench Pro: Can Language Models Solve Long-Horizon Software Security Tasks?26 May 2026
- 2026ContraFix: Skill-Enhanced Contrastive Runtime Analysis for Vulnerability Repair17 May 2026
- 2026uGen: An Agentic Framework for Generating Microarchitectural Attack PoCs15 May 2026
- 2026AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detection13 Apr 2026
- 2026Program Analysis Guided LLM Agent for Proof-of-Concept Generation8 Apr 2026
- 2026PoC-Adapt: Semantic-Aware Automated Vulnerability Reproduction with LLM Multi-Agents and Reinforcement Learning-Driven Adaptive Policy8 Apr 2026
- 2026AutoEG: Exploiting Known Third-Party Vulnerabilities in Black-Box Web Applications1 Apr 2026
- 2026Execution-State-Aware LLM Reasoning for Automated Proof-of-Vulnerability Generation14 Feb 2026
- 2026Patch-to-PoC: A Systematic Study of Agentic LLM Systems for Linux Kernel N-Day Reproduction7 Feb 2026
- 2025FaultLine: Automated Proof-of-Vulnerability Generation Using LLM Agents21 Jul 2025