AutoEG: Exploiting Known Third-Party Vulnerabilities in Black-Box Web Applications
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Abstract (arXiv)
Large-scale web applications are widely deployed with complex third-party components, inheriting security risks arising from component vulnerabilities. Security assessment is therefore required to determine whether such known vulnerabilities remain practically exploitable in real applications. Penetration testing is a widely adopted approach that validates exploitability by launching concrete attacks against known vulnerabilities in real-world black-box systems. However, existing approaches often fail to automatically generate reliable exploits, limiting their effectiveness in practical security assessment. This limitation mainly stems from two issues: (1) precisely triggering vulnerabilities with correct technical details, and (2) adapting exploits to diverse real-world deployment settings. In this paper, we propose AutoEG, a fully automated multi-agent framework for exploit generation targeting black-box web applications. AutoEG has two phases: First, AutoEG extracts precise vulnerability trigger logic from unstructured vulnerability information and encapsulates it into reusable trigger functions. Second, AutoEG uses trigger functions for concrete attack objectives and iteratively refines exploits through feedback-driven interaction with the target application. We evaluate AutoEG on 104 real-world vulnerabilities with 29 attack objectives, resulting in 660 exploitation tasks and 55,440 exploit attempts. AutoEG achieves an average success rate of 82.41%, substantially outperforming state-of-the-art baselines, whose best performance reaches only 32.88%.
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References · 17
- 2024AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attackscited by 94
- 2023Getting pwn'd by AI: Penetration Testing with Large Language Modelscited by 82
- 2018The Art, Science, and Engineering of Fuzzing: A Surveycited by 61
- 2024PentestAgent: Incorporating LLM Agents to Automated Penetration Testingcited by 46
- 2023ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We?cited by 44
- 2016A Survey of Symbolic Execution Techniquescited by 36
- 2024AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testingcited by 34
- 2024PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediationcited by 30
- 2025VulnBot: Autonomous Penetration Testing for A Multi-Agent Collaborative Frameworkcited by 30
- 2025PoCGen: Generating Proof-of-Concept Exploits for Vulnerabilities in Npm Packagescited by 12
- 2024Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++cited by 6
- 2025Good News for Script Kiddies? Evaluating Large Language Models for Automated Exploit Generationcited by 6
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- 2025PentestEval: Benchmarking LLM-based Penetration Testing with Modular and Stage-Level Designcited by 6
- 2025A Systematic Study on Generating Web Vulnerability Proof-of-Concepts Using Large Language Modelscited by 5
- 2018Thou Shalt Not Depend on Me: Analysing the Use of Outdated JavaScript Libraries on the Webcited by 2
- 2023Countering the Path Explosion Problem in the Symbolic Execution of Hardware Designscited by 1
- 2025Real-World Usability of Vulnerability Proof-of-Concepts: A Comprehensive Studycited by 1
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