Newest papers

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

Vlad Mihal, Hosam Alamleh, Alessandro Cantelli-Forti · 2026 · arXiv

DOI: 10.48550/arXiv.2610.27739 · open-access PDF

Japanese

言語モデル攻撃者は、標的を探索し、相互作用する際にコンピュータリソースを消費し、コストを課すためのサイバー詐欺の機会を生み出します。そのような取り組みが防衛者に有利かどうかは、両側の測定が必要です。我々は、防衛言語モデルを用いて攻撃者コマンドを分析し、防御反応を選択するために防御言語モデルを使用するデコイに対して、自律的な言語モデル攻撃者を評価します。

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 disab

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