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arXiv · 2603.10042

Targeted Bit-Flip Attacks on LLM-Based Agents

Jialai Wang·Ya Wen·Zhongmou Liu·Yuxiao Wu·Bingyi He·Zongpeng Li·Ee-Chien Chang
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Abstract

Targeted bit-flip attacks (BFAs) exploit hardware faults to manipulate model parameters, posing a significant security threat. While prior work targets single-step inference models (e.g., image classifiers), LLM-based agents with multi-stage pipelines and external tools present new attack surfaces, which remain unexplored. This work introduces Flip-Agent, the first targeted BFA framework for LLM-based agents, manipulating both final outputs and tool invocations. Our experiments show that Flip-Agent significantly outperforms existing targeted BFAs on real-world agent tasks, revealing a critical vulnerability in LLM-based agent systems.

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Jialai Wang, Ya Wen, Zhongmou Liu, Yuxiao Wu, Bingyi He, Zongpeng Li, Ee-Chien Chang. 2026-03-07. Targeted Bit-Flip Attacks on LLM-Based Agents. https://arxiv.org/abs/2603.10042

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