Make the Adversary Spend More Energy: Generative Deception against Language-Model Attackers in Critical Infrastructure

Summary

The authors evaluate an autonomous language-model attacker against a decoy whose defending language model analyzes attacker commands and selects defensive responses. Across 330 sessions on identical GPUs, the attacker-to-defender energy ratio exceeded one with a 95% interval in 41 of 48 configurations under the excluding-diagnostics allocation, and in all 48 when diagnostic calls were included. Enabling thinking on both sides raised defender energy by 8 to 17 times and reversed the exchange for one model pair.

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