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

Vlad Mihal, Hosam Alamleh, Alessandro Cantelli‐Forti · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27739 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

语言模型攻击者在探索和与目标互动时消耗计算资源,创造了网络欺骗的机会,以降低成本。 这种承诺是否有利于防御者需要测量双方。 我们对一个使用防御语言模型来分析攻击者命令并选择防御性响应的自主语言模型攻击者进行评估。 在 330 个会话中,8 个模型对,两个攻击者配置和三个反注级别中,双方均在相同的 GPU 上运行,他们的能量被测量在空闲状态之上。 随着运行时的思维障碍,攻击者与防御者能量比在 48 个配置中超过 41 个配置中有 95% 的间隔,而不包括攻击者诊断呼叫,并且在 48 个配置中,这些呼叫都包含了。

Machine translation (Meta M2M100); the English original follows.

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 attacker profiles and three counter-injection levels, both sides run on identical GPUs and their energy is measured above idle. With runtime thinking disabled, the attacker-to-defender energy ratio has a 95% interval above one in 41 of 48 configurations under an allocation excluding attacker diagnostic calls, and in all 48 when those calls are included. Token ratios remain within approximately a factor of two in configurations without generation-cap truncation, showing that token counts can obscure substantial energy asymmetry. Counter-injection increases attacker tokens by approximately 9% per level without a detectable pooled change in energy consumption. Enabling thinking on both sides increases defender energy by 8--17 times and reverses the exchange for one model pair in the original campaign. We use these findings to motivate a proposed SCADA deception architecture with private physical state per identified adversary and deterministic protocol and process emulation.

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Topics: Adversary, Computer security, Security token, Computer science, Deception, Process (computing)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange