RedTeamLLM: an Agentic AI framework for offensive security

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.

@article{challita2025_2505.06913,
  title={ RedTeamLLM: an Agentic AI framework for offensive security },
  author={ Brian Challita and Pierre Parrend },
  journal={arXiv preprint arXiv:2505.06913},
  year={ 2025 }
}