From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs

• URL: http://arxiv.org/abs/2506.13434v1
• Date: Mon, 16 Jun 2025 12:52:19 GMT
• Title: From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
• Authors: Alsharif Abuadbba, Chris Hicks, Kristen Moore, Vasilios Mavroudis, Burak Hasircioglu, Diksha Goel, Piers Jennings,
• Abstract summary: Large Language Models (LLMs) are set to reshape cybersecurity by augmenting red and blue team operations.<n>This position paper maps LLM applications across cybersecurity frameworks such as MITRE ATT&CK and the NIST Cybersecurity Framework (CSF)<n>Key limitations include hallucinations, limited context retention, poor reasoning, and sensitivity to prompts.<n>We recommend maintaining human-in-the-loop oversight, enhancing model explainability, integrating privacy-preserving mechanisms, and building systems robust to adversarial exploitation.
• Score: 5.438441265064793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) are set to reshape cybersecurity by augmenting red and blue team operations. Red teams can exploit LLMs to plan attacks, craft phishing content, simulate adversaries, and generate exploit code. Conversely, blue teams may deploy them for threat intelligence synthesis, root cause analysis, and streamlined documentation. This dual capability introduces both transformative potential and serious risks. This position paper maps LLM applications across cybersecurity frameworks such as MITRE ATT&CK and the NIST Cybersecurity Framework (CSF), offering a structured view of their current utility and limitations. While LLMs demonstrate fluency and versatility across various tasks, they remain fragile in high-stakes, context-heavy environments. Key limitations include hallucinations, limited context retention, poor reasoning, and sensitivity to prompts, which undermine their reliability in operational settings. Moreover, real-world integration raises concerns around dual-use risks, adversarial misuse, and diminished human oversight. Malicious actors could exploit LLMs to automate reconnaissance, obscure attack vectors, and lower the technical threshold for executing sophisticated attacks. To ensure safer adoption, we recommend maintaining human-in-the-loop oversight, enhancing model explainability, integrating privacy-preserving mechanisms, and building systems robust to adversarial exploitation. As organizations increasingly adopt AI driven cybersecurity, a nuanced understanding of LLMs' risks and operational impacts is critical to securing their defensive value while mitigating unintended consequences.

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