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arXiv · 2605.21497

Autonomous LLM Agents & CTFs: A Second Look

Youness Bouchari·Matteo Boffa·Marco Mellia·Idilio Drago·Thanh Minh Bui·Dario Rossi
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Abstract

Large Language Model (LLM) agents are increasingly proposed to automate offensive security tasks, with recent studies reporting near human-level success rates in Capture-the-Flag (CTF) challenges. We here revisit these results, providing a second look at these claims. We engineer different agent architectures of increasing complexity and modularity on 30 web-based CTFs challenges spanning 14 vulnerability classes. We instantiate these agents with multiple LLM backbones, and compare them with claude-code, a general-purpose agent that automatically determines its internal architecture. Our evaluation yields three main findings. First, claude-code achieves performance comparable to the engineered architectures (19/30 solved tasks), suggesting that general-purpose agents are strong baselines for offensive security tasks. Second, both our architectures and claude-code struggle in the same challenge categories, revealing persistent barriers that keep current agents below human-level capability. Third, by leveraging our manually designed architectures we can systematically measure the impact of additional components, finding that structured orchestration of specialized roles outperforms monolithic designs, improving run-to-run consistency, and reducing execution costs.

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Youness Bouchari, Matteo Boffa, Marco Mellia, Idilio Drago, Thanh Minh Bui, Dario Rossi. 2026-04-29. Autonomous LLM Agents & CTFs: A Second Look. https://arxiv.org/abs/2605.21497

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