Safety, Security, and Cognitive Risks in World Models
World models - learned internal simulators of environment dynamics - are rapidly becoming foundational to autonomous decision-making in robotics, autonomous vehicles, and agentic AI. By predicting future states in compressed latent spaces, they enable sample-efficient planning and long-horizon imagination without direct environment interaction. Yet this predictive power introduces a distinctive set of safety, security, and cognitive risks. Adversaries can corrupt training data, poison latent representations, and exploit compounding rollout errors to cause significant degradation in safety-critical deployments. At the alignment layer, world model-equipped agents are more capable of goal mi...
| Authors | Manoj Parmar |
|---|---|
| Primary category | cs.CR |
| Published | 1 April 2026 |
| Last updated at source | 6 April 2026 |
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