TRAP: Tail-aware Ranking Attack for World-Model Planning

Understand

World models enable long-horizon planning by internally generating and evaluating imagined trajectories, making them a promising foundation for generalist agents.

• However, this imagination-driven decision process also introduces new security risks.
• Existing backdoor attacks typically aim to manipulate local features, one-step predictions, or instantaneous policy outputs.
• While such objectives may suffice for weaker reactive models, they are often ineffective against world models, where the learned dynamics prior and planning process can absorb or wash out the effects of shallow perturbations.