Security of World-Model-Based Embodied AI: A Lifecycle of Threats, Defenses, and Evaluation
World models give embodied AI a predictive core: they compress observations into states, simulate action-conditioned futures, and enable planning beyond reactive control. This predictive layer, however, opens a new security boundary-compromise can propagate from data, sensors, prompts, or feedback into physical action. Rather than treating world models as an isolated component, this survey traces threats across their entire lifecycle-from data construction and representation learning, through state grounding and imagination, to trajectory evaluation, execution, and long-term adaptation via memory and tools. We show that familiar attack families: poisoning, backdoors, adversarial examples,...
| Authors | Fazhong Liu, Zhuoyan Chen, Haozhen Tan et al. |
|---|---|
| Primary category | cs.CR |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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