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LLM Agent Honeypots

The emergence of autonomous AI agents capable of independent reconnaissance and exploit execution necessitates a shift from human-centric defense to AI-aware deception. LLM Agent Honeypots utilize simulated API endpoints, honey-tokens, and decoy orchestration frameworks to lure adversarial agents into controlled environments. By capturing behavioral telemetry, researchers analyze LLM-to-LLM interaction patterns, iteration speeds, and specific tool-use chains. This methodology enables the differentiation between human attackers and autonomous agents while mapping the reasoning loops and prompt-injection triggers utilized by offensive AI in the wild.


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