Record summary
A quick snapshot of what this page covers.
Risk profile
How this risk is described and categorized.
"Non-embodied AIs are known to propagate misinformation [81, 82]. Various studies have shown that LLMs hallucinate information, including academic citations [83], clinical knowledge [84], and cultural references [85]. EAI systems inherit these shortcomings in the physical world, answering user questions with deceptive or incorrect information [86]. Because VLAs fuse vision and language, their hallucinatory failures can be spatially grounded—e.g., misidentifying an object in view and then generating a plausible yet unsafe action plan around it. And although automated home assistants like Amazon’s Alexa already lie about issues as innocuous as Santa Claus’ existence [87], more mobile, capable, and trusted EAI systems in sensitive positions (like home-assistant or community-service positions) could easily spread model developers’ propaganda and talking points to users."
Suggested mitigations
Defenses that may help with related attacks.
Source
Research source for this risk, when available.
Included resource
Embodied AI: Emerging Risks and Opportunities for Policy Action
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.