mlsanityData to 7 Oct 2026

ARACNE: An LLM-Based Autonomous Shell Pentesting Agent

arXiv 2502.18528 ↗First posted 24 Feb 2025cs.CR cs.AI cs.ROcited by 4

On the map: LLM agents and tool use → Agent threats, guardrails and governance

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.

Key facts. Cited by 4 papers in the map (data through 7 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.

Builds on

7 of 7 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing2 Dec

Other references · 6

  1. 2024AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attackscited by 94
  2. 2024Safeguarding Large Language Models: A Surveycited by 73
  3. 2024When LLMs Meet Cybersecurity: A Systematic Literature Reviewcited by 70
  4. 2024PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediationcited by 30
  5. 2023LLM in the Shell: Generative Honeypotscited by 19
  6. 2024Hacking Back the AI-Hacker: Prompt Injection as a Defense Against LLM-driven Cyberattackscited by 15

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025RapidPen: Fully Automated IP-to-Shell Penetration Testing with LLM-based Agents
  2. 2025AutoPentester: An LLM Agent-based Framework for Automated Pentesting
  3. 2024PentestAgent: Incorporating LLM Agents to Automated Penetration Testing
  4. 2025AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents
  5. 2026Toward Secure AI-Powered Penetration Testing Agents: Security Threats, Guardrails, and Architectural Perspectives
  6. 2025Construction and Evaluation of LLM-based agents for Semi-Autonomous penetration testing
  7. 2026APT-Agent: Automated Penetration Testing using Large Language Models
  8. 2026CARE: Pre-Execution Command Verification for Shell-Executing LLM Agents