This research introduces RoboGuard, a comprehensive safety framework for LLM-enabled robots that addresses unique security challenges at the intersection of AI and robotics.

• Prevents both common LLM errors (hallucinations) and targeted attacks from translating into harmful physical actions
• Implements a multi-layer safety architecture with specialized guardrails for language processing, planning, and physical execution
• Demonstrates effectiveness against jailbreaking attempts and other security vulnerabilities specific to robotic systems
• Provides a framework for responsible deployment of increasingly autonomous LLM-powered robots

This research is critical for security professionals as it bridges the gap between traditional robot safety approaches and LLM safeguards, protecting against novel threats in systems with real-world physical capabilities.

Safety Guardrails for LLM-Enabled Robots