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ARACNE: An LLM-Based Autonomous Shell Pentesting Agent
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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.
Forward citations
Cited by 2 Pith papers
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StealthBench: Measuring Operational Stealth in Autonomous Offensive-Security Agents
StealthBench's LLM-judge panel finds no AI agent solves offensive-security tasks stealthily more than 54% of the time.
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A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges
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.
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