ARACNE: An LLM-Based Autonomous Shell Pentesting Agent

The complete automation of cyber-attacks is an area of growing interest since the surge of Large Language Models (LLMs) in recent years. Although the application of LLM in all areas of cybersecurity has flourished, the creation of attacking LLM agents that can act independently is among the most popular options [1]. Attacking LLM agents can perform automatic security testing of applications, lowering the cost for organizations to find vulnerabilities and misconfiguration problems and identify other security issues [2]. Existing automated attacking agents, such as PenHeal [2], AutoAttacker [3], and HackSynth [4] show promising results but with clear limitations. Agents are unable to work so far without occasional mistakes and hallucinations.