Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks

Embodied AI systems, integrating Large Vision-Language Models (LVLMs) and Large Language Models (LLMs) with physical actuators and sensors, face unique robustness and security challenges stemming from the complex interplay between perception, cognition, and actuation in real-world environments. This survey provides a systematic analysis of these vulnerabilities and associated attack surfaces. We propose a tripartite vulnerability taxonomy comprising foundational, integration, and contextual risks. Foundational vulnerabilities arise from inherent limitations in current AI architectures and training paradigms; Integration vulnerabilities emerge from the composition of cyber-physical components; And contextual…

• NN
  National Natural Science Foundation of China

  Award: 62376246