LLM控制机器人时，攻击者可植入隐蔽后门，让指令变物理动作。

The integration of large language models (LLMs) into robotic control pipelines enables natural language interfaces that translate user prompts into executable commands. However, this digital-to-physical interface introduces a critical and underexplored vulnerability: structured backdoor attacks embedded during fine-tuning. In this work, we experimentally investigate LoRA-based supply-chain backdoors in LLM-mediated ROS2 robotic control systems and evaluate their impact on physical robot execution. We construct two poisoned fine-tuning strategies targeting different stages of the command generation pipeline and reveal a key systems-level insight: back-doors embedded at the natural-language reasoning stage do not reliably propagate to executable control outputs, whereas backdoors aligned directly with structured JSON command formats successfully survive translation and trigger physical actions. In both simulation and real-world experiments, backdoored models achieve an average Attack Success Rate of 83% while maintaining over 93% Clean Performance Accuracy (CPA) and sub-second latency, demonstrating both reliability and stealth. We further implement an agentic verification defense using a secondary LLM for semantic consistency checking. Although this reduces the Attack Success Rate (ASR) to 20%, it increases end-to-end latency to 8-9 seconds, exposing a significant security-responsiveness trade-off in real-time robotic systems. These results highlight structural vulnerabilities in LLM-mediated robotic control architectures and underscore the need for robotics-aware defenses for embodied AI systems.