Honeyval evaluates LLM HTTP honeypots with AI attackers and shows they produce longer interactions, lower detection rates, and cost advantages over rule-based baselines.
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Agentic LLMs remain robust to renaming and insertion but degrade on composed transformations and deeper obfuscation in CTF tasks, enabled by a new Evolve-CTF tool for generating equivalent challenge families.
citing papers explorer
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Honeyval: A Comprehensive Evaluation Framework for LLM-powered HTTP Honeypots
Honeyval evaluates LLM HTTP honeypots with AI attackers and shows they produce longer interactions, lower detection rates, and cost advantages over rule-based baselines.
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Capture the Flags: Family-Based Evaluation of Agentic LLMs via Semantics-Preserving Transformations
Agentic LLMs remain robust to renaming and insertion but degrade on composed transformations and deeper obfuscation in CTF tasks, enabled by a new Evolve-CTF tool for generating equivalent challenge families.