静かなる妨害：LLM搭載ロボットシステムに対する内部状態トリガーバックドア攻撃
Silent Sabotage: Internal State Triggered Backdoor Attacks on LLM-Powered Robotic Systems
LLMで制御されるロボットに、外部刺激ではなくロボット自身の過去の行動履歴をトリガーとする隠れたバックドアを埋め込めることを示し、通常時は性能を保ちつつ特定条件下で停止や衝突を引き起こせることを実験で明らかにした。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Doniyorkhon Obidov, Shivayogi Akki, Tan Chen, Kaichen Yang
分類: cs.RO, cs.AI, cs.CR
原文アブストラクト
The integration of Large Language Models (LLMs) into robotic control systems is enabling a new generation of autonomous agents capable of complex reasoning and planning. While this paradigm shift accelerates progress, it also introduces novel security risks that remain largely unexplored. Current research into LLM backdoors has focused on attacks triggered by external stimuli, such as specific words, visual objects, or environmental states. These attacks, while potent, overlook a more insidious class of vulnerability where the trigger is internal to the agent's own operational logic. This paper presents the first comprehensive study of history-based backdoor attacks on LLM-powered robotic systems. We demonstrate that an attacker can embed a stealthy backdoor into an LLM-based robot controller by manipulating its instructions. This backdoor is triggered not by an external cue, but by a specific, rare sequence of the robot's own past actions. It remains dormant during normal operation, preserving the robot's utility, but can be activated to induce a malicious behavior, such as a complete stop or a collision. Our experiments, conducted in a simulated environment with a variety of robots and LLMs, show that this history-based attack is highly effective, achieving a near-perfect attack success rate while remaining exceptionally difficult to detect. These findings reveal a critical and previously unaddressed vulnerability in autonomous systems and underscore the urgent need for security measures that account for an agent's internal state.