  Embodied artificial intelligence (AI) represents an artificial intelligence
system that interacts with the physical world through sensors and actuators,
seamlessly integrating perception and action. This design enables AI to learn
from and operate within complex, real-world environments. Large Language Models
(LLMs) deeply explore language instructions, playing a crucial role in devising
plans for complex tasks. Consequently, they have progressively shown immense
potential in empowering embodied AI, with LLM-based embodied AI emerging as a
focal point of research within the community. It is foreseeable that, over the
next decade, LLM-based embodied AI robots are expected to proliferate widely,
becoming commonplace in homes and industries. However, a critical safety issue
that has long been hiding in plain sight is: could LLM-based embodied AI
perpetrate harmful behaviors? Our research investigates for the first time how
to induce threatening actions in embodied AI, confirming the severe risks posed
by these soon-to-be-marketed robots, which starkly contravene Asimov's Three
Laws of Robotics and threaten human safety. Specifically, we formulate the
concept of embodied AI jailbreaking and expose three critical security
vulnerabilities: first, jailbreaking robotics through compromised LLM; second,
safety misalignment between action and language spaces; and third, deceptive
prompts leading to unaware hazardous behaviors. We also analyze potential
mitigation measures and advocate for community awareness regarding the safety
of embodied AI applications in the physical world.
