Multi-Agent Framework Utilizing Large Language Models for Solving Capture-the-Flag Challenges in Cybersecurity Competitions
Zewen Huang, Jinjing Zhuge, Jinjing Zhuge, Jianwei Zhuge, Jianwei Zhuge · Applied Sciences · 2025
Capture the Flag (CTF) is an important form of competition in cybersecurity, which tests participants’ knowledge and problem-solving abilities. We propose a multi-agent framework based on large language models to simulate human participants and attempt to automate the solutions of common CTF problems, especially in cryptographic and miscellaneous challenges. We implement the collaboration of multiple expert agents and access external tools to give the language model a basic level of practical competence in the field of cybersecurity. We primarily test two capabilities of the large model: to analyze, reason, and determine solutions to CTF problems, and to assist with problem-solving by generating code or utilizing unannotated existing external tools. We construct a benchmark based on the puzzles from the book “Ghost in the Wires” and the THUCTF competition. The experiment results showed that our agents performed well on the former and were significantly improved with some human hints, compared with related work. We also discuss the challenges that language models face in cybersecurity challenges and the effect of leveraging reasoning models.