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VulnBot: Autonomous Penetration Testing for A Multi-Agent Collaborative Framework

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arxiv 2501.13411 v1 pith:LTKGHHOT submitted 2025-01-23 cs.SE

He Kong , Die Hu , Jingguo Ge , Liangxiong Li , Tong Li , Bingzhen Wu This is my paper · ORCID
classification cs.SE
keywords penetrationtestingvulnbotautomatedautonomouscollaborativeexecutionframework
verification ladder T0 review T1 audit T2 compute T3 formal
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Penetration testing is a vital practice for identifying and mitigating vulnerabilities in cybersecurity systems, but its manual execution is labor-intensive and time-consuming. Existing large language model (LLM)-assisted or automated penetration testing approaches often suffer from inefficiencies, such as a lack of contextual understanding and excessive, unstructured data generation. This paper presents VulnBot, an automated penetration testing framework that leverages LLMs to simulate the collaborative workflow of human penetration testing teams through a multi-agent system. To address the inefficiencies and reliance on manual intervention in traditional penetration testing methods, VulnBot decomposes complex tasks into three specialized phases: reconnaissance, scanning, and exploitation. These phases are guided by a penetration task graph (PTG) to ensure logical task execution. Key design features include role specialization, penetration path planning, inter-agent communication, and generative penetration behavior. Experimental results demonstrate that VulnBot outperforms baseline models such as GPT-4 and Llama3 in automated penetration testing tasks, particularly showcasing its potential in fully autonomous testing on real-world machines.

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents

    cs.CR 2026-07 conditional novelty 7.0 of 10

    A trajectory-adaptive honeypot system, AgentSnare, achieves a 0/45 verified exploit rate against LLM-based penetration testers across 15 vulnerable web apps and three attacker models.

  2. The Ethics of Autonomous AI Agents for Offensive Security

    cs.CR 2026-07 conditional novelty 6.0 of 10

    Autonomous AI hacking tools combine three kinds of indeterminacy—action, impact, and users—making moral responsibility diffuse and giving attackers a short-term advantage under current cost asymmetries.

  3. From Controlled to the Wild: Evaluation of Pentesting Agents for the Real-World

    cs.AI 2026-05 unverdicted novelty 6.0 of 10

    A practical evaluation protocol for AI pentesting agents that uses validated vulnerability discovery, LLM semantic matching, and bipartite scoring to assess performance in realistic, complex targets.

  4. Recognition Without Mitigation: Ethical Frameworks in Autonomous Offensive-LLM Agent Research

    cs.CR 2025-06 reject novelty 6.0 of 10

    In offensive-LLM agent papers, dual-use risk is acknowledged in 39% of papers but concrete mitigations appear in only 7%.

  5. Can LLMs Hack Enterprise Networks? Autonomous Assumed Breach Penetration-Testing Active Directory Networks

    cs.CR 2025-02 conditional novelty 6.0 of 10

    An autonomous LLM-driven agent can compromise accounts in a realistic Active Directory testbed, with reasoning models outperforming non-reasoning ones at competitive cost.

  6. A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges

    cs.SE 2026-07 accept novelty 5.5 of 10

    LLM pentest agents co-evolved through four bottleneck-driven phases into RLVR systems, while CTF platforms became dual evaluation/training infrastructure and three linked reliability gaps remain.

  7. Breaking Android with AI: A Deep Dive into LLM-Powered Exploitation

    cs.SE 2025-09 conditional novelty 4.0 of 10

    LLM-generated scripts can automate several Android rooting and exploitation tasks in an emulator, but kernel, bootloader, and A/B-partition attacks are beyond current capability.

  8. Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models

    cs.CR 2025-06 conditional novelty 4.0 of 10

    A multi-agent LLM framework automatically generates traffic simulations, a broadcast-spoofing cyberattack, and a consensus defense, reducing attack-induced travel delay by 3.3% in a five-vehicle case study.

  9. On the Surprising Efficacy of LLMs for Penetration-Testing

    cs.CR 2025-07 conditional novelty 3.0 of 10

    A critical review arguing that LLMs are surprisingly effective for penetration testing because the task is largely pattern-matching, while noting serious reliability, safety, and cost barriers to autonomous use.

  10. Cybersecurity AI: The Dangerous Gap Between Automation and Autonomy

    cs.CR 2025-06 conditional novelty 3.0 of 10

    A roboticist adapts driving-automation levels to cybersecurity, arguing that today's 'autonomous' penetration testers run at Level 3-4 and still require human oversight.

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