Make the Adversary Spend More Energy: Generative Deception against Language-Model Attackers in Critical Infrastructure
Researchers quantify whether generative-deception defences can impose greater computational costs on LLM-powered attackers than they cost to operate — a key economic question for critical-infrastructure defenders considering AI-augmented honeypots.
Summary written by editorial AI · Source link below
arXiv:2610.27739v1 Announce Type: new Abstract: Language-model attackers consume computational resources as they explore and interact with a target, creating an opportunity for cyber deception to impose costs. Whether such engagements favor the defender requires measuring both sides. We evaluate an autonomous language-model attacker against a decoy that uses a defending language model to analyze attacker commands and select defensive responses. Across 330 sessions, eight model pairs, two attack
Editorial Analysis
As adversaries adopt LLMs for automated reconnaissance and exploitation, understanding the cost economics of generative deception helps defenders allocate budgets to the most effective countermeasures.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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