Multi-agent systems powered by large foundation models (LFMs) are increasingly deployed to control industrial robots through natural language, creating deployments in which security failures produce physical consequences. We analyse this threat landscape through Cobot-Claw, a deployed four-agent system for UR3e robotic arm control, and identify five attack classes specific to agentic cyber-physical systems. We propose ZTPM, a Zero Trust Policy Model comprising 25 typed primitives across five enforcement domains with Physical Impact Tiers as a runtime policy dimension. An empirical evaluation across 60 execution traces on two LFM backends provides initial evidence that actuation parameter selection is model-dependent and non-deterministic, motivating the need for policy-level enforcement at the physical actuation boundary.

翻译：由大型基础模型（LFM）支持的多Agent系统正越来越多地通过自然语言被部署以控制工业机器人，这种部署模式中安全故障会产生物理后果。我们通过Cobot-Claw系统（一个用于UR3e机械臂控制的四Agent部署系统）分析该威胁景观，并识别出五种特定于Agent化信息物理系统的攻击类别。我们提出ZTPM零信任策略模型，该模型包含跨五个执行域的25类类型化基元，并以物理影响层级作为运行时策略维度。在两种LFM后端上对60条执行轨迹进行的实证评估初步表明，驱动参数选择具有模型依赖性和非确定性，这凸显了在物理执行边界实施策略级管控的必要性。