RedTeamLLM: an Agentic AI framework for offensive security

TL;DR

RedTeamLLM is an innovative agentic AI framework designed for automated penetration testing, addressing key challenges like plan correction and context management, and demonstrated through solving complex CTF security challenges.

Contribution

It introduces a comprehensive security model with reasoning capabilities for automating pentest tasks, tackling open challenges in agentic AI security applications.

Findings

Successfully automated resolution of complex CTF challenges

Demonstrated reasoning capability enhances pentest automation

Addresses key challenges like memory management and plan correction

Abstract

From automated intrusion testing to discovery of zero-day attacks before software launch, agentic AI calls for great promises in security engineering. This strong capability is bound with a similar threat: the security and research community must build up its models before the approach is leveraged by malicious actors for cybercrime. We therefore propose and evaluate RedTeamLLM, an integrated architecture with a comprehensive security model for automatization of pentest tasks. RedTeamLLM follows three key steps: summarizing, reasoning and act, which embed its operational capacity. This novel framework addresses four open challenges: plan correction, memory management, context window constraint, and generality vs. specialization. Evaluation is performed through the automated resolution of a range of entry-level, but not trivial, CTF challenges. The contribution of the reasoning capability of our agentic AI framework is specifically evaluated.