Targeted Bit-Flip Attacks on LLM-Based Agents

ArXi:2603.10042v1 Announce Type: cross 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