用大模型自动攻破SSH系统，5步内完成渗透测试
ARACNE: An LLM-Based Autonomous Shell Pentesting Agent
- 设计多大模型协同的自动化渗透架构，可执行真实命令
- 对战自适应防御系统成功率60%，对战CTF挑战成功率达57.58%
- 适合安全研究者与红队实战人员参考，验证大模型在真实环境中的能力
我们提出ARACNE，一个针对SSH服务的全自主大模型渗透测试代理，可在真实Linux Shell系统上执行命令。引入支持多大模型的新型代理架构。实验表明，ARACNE在对抗自主防御系统ShelLM时达到60%的成功率，在过线绕行（Over The Wire Bandit）CTF挑战中成功率达57.58%，优于现有技术。获胜时，平均行动次数低于5次。结果表明，多大模型协同是提升动作准确性的有效途径。
原文摘要 · Abstract (English)
We introduce ARACNE, a fully autonomous LLM-based pentesting agent tailored for SSH services that can execute commands on real Linux shell systems. Introduces a new agent architecture with multi-LLM model support. Experiments show that ARACNE can reach a 60\% success rate against the autonomous defender ShelLM and a 57.58\% success rate against the Over The Wire Bandit CTF challenges, improving over the state-of-the-art. When winning, the average number of actions taken by the agent to accomplish the goals was less than 5. The results show that the use of multi-LLM is a promising approach to increase accuracy in the actions.
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