AutoPenBench：面向渗透测试的生成式智能体基准评测
AutoPenBench: Benchmarking Generative Agents for Penetration Testing
AI总结：
本文提出开放基准 AutoPenBench，通过 33 个分级渗透测试任务和标准化里程碑评估完全自主与人机协作两类生成式智能体，并揭示不同 LLM 对攻击成功率的影响。
摘要：
生成式 AI 智能体是由大型语言模型（LLM）驱动的软件系统，正成为实现网络安全任务自动化的一种有前景的方法。其中，渗透测试因任务复杂性以及模拟网络攻击所需策略的多样性而成为一个具有挑战性的领域。尽管人们对利用生成式智能体自动化渗透测试的兴趣日益增长，并已有初步研究，但在用于其评估和开发的全面、标准框架方面仍存在显著空白。本文介绍 AutoPenBench，这是一个用于评估自动化渗透测试中生成式智能体的开放基准。我们提出了一个包含 33 项任务的综合框架，每项任务都代表智能体必须攻击的一个脆弱系统。任务难度逐级提升，涵盖体外场景和真实世界场景。我们通过通用里程碑和特定里程碑评估智能体性能，从而能够以标准化方式比较结果，并理解被测智能体的局限。我们通过测试两种智能体架构展示 AutoPenBench 的优势：一种是完全自主的智能体，另一种是支持人机交互的半自主智能体。我们比较了它们的性能和局限。例如，完全自主智能体表现不理想，在整个基准测试中取得 21% 的成功率（SR），解决了 27% 的简单任务，并且仅完成一项真实世界任务。相比之下，辅助型智能体表现出显著提升，SR 为 64%。AutoPenBench 还使我们能够观察 GPT-4o 或 OpenAI o1 等不同 LLM 如何影响智能体完成任务的能力。我们相信，该基准以一个标准且灵活的框架填补了空白，可在共同基础上比较渗透测试智能体。我们希望通过在 https://github.com/lucagioacchini/auto-pen-bench 公开 AutoPenBench，与研究社区共同扩展该基准。
英文摘要：
Generative AI agents, software systems powered by Large Language Models (LLMs), are emerging as a promising approach to automate cybersecurity tasks. Among the others, penetration testing is a challenging field due to the task complexity and the diverse strategies to simulate cyber-attacks. Despite growing interest and initial studies in automating penetration testing with generative agents, there remains a significant gap in the form of a comprehensive and standard framework for their evaluation and development. This paper introduces AutoPenBench, an open benchmark for evaluating generative agents in automated penetration testing. We present a comprehensive framework that includes 33 tasks, each representing a vulnerable system that the agent has to attack. Tasks are of increasing difficulty levels, including in-vitro and real-world scenarios. We assess the agent performance with generic and specific milestones that allow us to compare results in a standardised manner and understand the limits of the agent under test. We show the benefits of AutoPenBench by testing two agent architectures: a fully autonomous and a semi-autonomous supporting human interaction. We compare their performance and limitations. For example, the fully autonomous agent performs unsatisfactorily achieving a 21% Success Rate (SR) across the benchmark, solving 27% of the simple tasks and only one real-world task. In contrast, the assisted agent demonstrates substantial improvements, with 64% of SR. AutoPenBench allows us also to observe how different LLMs like GPT-4o or OpenAI o1 impact the ability of the agents to complete the tasks. We believe that our benchmark fills the gap with a standard and flexible framework to compare penetration testing agents on a common ground. We hope to extend AutoPenBench along with the research community by making it available under https://github.com/lucagioacchini/auto-pen-bench.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
