CTFExplorer: Evaluating LLM Offensive Agents Through Multi-Target Web CTF Benchmarking
📰 ArXiv cs.AI
Learn how to evaluate LLM offensive agents using multi-target web CTF benchmarking with CTFExplorer, improving strategic reasoning assessment beyond exploitation
Action Steps
- Build a multi-target web CTF environment using CTFExplorer to simulate real-world scenarios
- Configure LLM offensive agents to interact with the CTF environment and prioritize targets
- Run experiments to evaluate the agents' strategic reasoning and exploitation capabilities
- Analyze results to identify areas for improvement in the agents' decision-making processes
- Compare the performance of different LLM offensive agents using the CTFExplorer benchmark
Who Needs to Know This
Security researchers and developers of LLM-based offensive security agents can benefit from this approach to evaluate and improve their agents' strategic reasoning capabilities
Key Insight
💡 CTFExplorer provides a more realistic evaluation of LLM offensive agents by simulating multi-target web CTF scenarios, assessing strategic reasoning and exploitation capabilities
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🚀 Evaluate LLM offensive agents with CTFExplorer! 🤖 Improve strategic reasoning beyond exploitation 💡
Key Takeaways
Learn how to evaluate LLM offensive agents using multi-target web CTF benchmarking with CTFExplorer, improving strategic reasoning assessment beyond exploitation
Full Article
Title: CTFExplorer: Evaluating LLM Offensive Agents Through Multi-Target Web CTF Benchmarking
Abstract:
arXiv:2602.08023v3 Announce Type: replace-cross Abstract: Existing benchmarks for LLM-based offensive security agents use isolated, single-target setups with a known vulnerable service and fixed objective. They measure exploitation effectively, but miss how real Capture-the-Flag (CTF) participants triage unknown surfaces, prioritize targets, and allocate effort under uncertainty. Current evaluations therefore fail to assess strategic reasoning beyond exploitation alone. To address this, we intro
Abstract:
arXiv:2602.08023v3 Announce Type: replace-cross Abstract: Existing benchmarks for LLM-based offensive security agents use isolated, single-target setups with a known vulnerable service and fixed objective. They measure exploitation effectively, but miss how real Capture-the-Flag (CTF) participants triage unknown surfaces, prioritize targets, and allocate effort under uncertainty. Current evaluations therefore fail to assess strategic reasoning beyond exploitation alone. To address this, we intro
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