Survey reviews security threats and defenses across the full lifecycle of world-model embodied intelligence

A survey (Liu et al.) systematically reviews security threats, defenses, and evaluation methods for world-model-based embodied intelligence across the complete lifecycle, pointing out that predictive world models directly convert digital-level corruption into physical execution risks. The authors distinguish explicit latent imagination models (e.g., DreamerV3) from general VLA models with implicit physical priors (e.g., OpenVLA, Octo), and propose five core insights: the semantic-to-simulation-to-action gap, state- and uncertainty-conditioned risks, rollout-to-execution amplification, trajectory-level non-compositionality, and predictive safety hallucinations. The authors also analyze that attackers can bypass runtime safety monitors such as SafeDreamer, VLM-SAFE, and RoboGuard through four channels—observation, dynamics, constraints, and uncertainty—and provide three layers of defense: data provenance auditing, multi-sensor physical invariants, and execution gating such as CBF-QP, along with a four-step evaluation protocol and corresponding metric families.