When LLMs Team Up: A Coordinated Attack Framework for Automated Cyber Intrusions

• URL: http://arxiv.org/abs/2605.08763v1
• Date: Sat, 09 May 2026 07:47:42 GMT
• Title: When LLMs Team Up: A Coordinated Attack Framework for Automated Cyber Intrusions
• Abstract summary: This paper presents CAESAR, a coordinated multi-agent framework for analysis of intrusion-style tasks.<n>CAESAR decomposes the workflow into five typed roles and coordinates them through a bounded protocol.<n>Compared with a single-agent baseline under matched budgets and tool access, CAESAR improves task success and reduces performance variance.
• Score: 27.62993988829038
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated intrusion-style workflows require LLM agents to reason over partial observations, tool outputs, and executable artifacts under bounded budgets. A single LLM instance often compresses evidence extraction, planning, execution, and validation into one context, which increases the risk of context drift and error propagation. Existing LLM-based multi-agent systems support general collaboration, but they do not explicitly model the role boundaries, artifact provenance, and cost constraints that characterize multi-stage intrusion workflows. This paper presents CAESAR, a coordinated multi-agent framework for controlled analysis of LLM-agent behavior in intrusion-style tasks. CAESAR decomposes the workflow into five typed roles and coordinates them through a bounded round protocol with a persistent knowledge base, a per-round workspace, validator-gated knowledge promotion, and capability-token write isolation. We evaluate CAESAR on 25 CTF tasks across five categories and four LLM backends. Compared with a single-agent baseline under matched budgets and tool access, CAESAR improves task success and reduces performance variance, with larger gains on tasks requiring multi-step exploit composition. A secondary simulated interactional-security study suggests that the role structure can transfer beyond code-native surfaces. The results indicate that role transitions, artifact provenance, and knowledge-promotion events provide useful structural signals for monitoring coordinated LLM-agent behavior beyond individual prompt and output inspection. The dataset, implementation, and evaluation logs are released at https://github.com/Xu-Qiu/CMAS.

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