# ARACNE: An LLM-Based Autonomous Shell Pentesting Agent

arXiv: https://arxiv.org/abs/2502.18528 · HTML: https://mlsanity.com/paper/2502.18528/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 7 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 7 of 7)

- [AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks](https://mlsanity.com/paper/2403.01038/) (2024)
- [Safeguarding Large Language Models: A Survey](https://mlsanity.com/paper/2406.02622/) (2024)
- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing](https://mlsanity.com/paper/2412.01778/) (2024)
- [PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediation](https://mlsanity.com/paper/2407.17788/) (2024)
- [LLM in the Shell: Generative Honeypots](https://mlsanity.com/paper/2309.00155/) (2023)
- [Hacking Back the AI-Hacker: Prompt Injection as a Defense Against LLM-driven Cyberattacks](https://mlsanity.com/paper/2410.20911/) (2024)

## Built on by (most cited first, 5 of 5)

- [A Survey on Agentic Security: Applications, Threats and Defenses](https://mlsanity.com/paper/2510.06445/) (2025)
- [Hackers or Hallucinators? A Comprehensive Analysis of LLM-Based Automated Penetration Testing](https://mlsanity.com/paper/2604.05719/) (2026)
- [Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities](https://mlsanity.com/paper/2509.06921/) (2025)
- [AdvancedShelLM: A Stateful Multi-Agent LLM Honeypot for SSH Deception](https://mlsanity.com/paper/2606.27990/) (2026)
- [StealthBench: Measuring Operational Stealth in Autonomous Offensive-Security Agents](https://mlsanity.com/paper/2607.26314/) (2026)

## Abstract (arXiv)

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.