RedTeamLLM: an Agentic AI framework for offensive security
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909607371210752 |
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| author | Challita, Brian Parrend, Pierre |
| author_facet | Challita, Brian Parrend, Pierre |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_06913 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RedTeamLLM: an Agentic AI framework for offensive security Challita, Brian Parrend, Pierre Cryptography and Security Artificial Intelligence Computers and Society 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. |
| title | RedTeamLLM: an Agentic AI framework for offensive security |
| topic | Cryptography and Security Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2505.06913 |