TAPDreamer: Transferable Adversarial Patches for World Action Models

Excerpt

arXiv:2610.06814v2 Announce Type: cross Abstract: World models learn to predict how their environment will evolve, making them an important foundation for general-purpose robotic control. Yet world action models depend on camera inputs whose manipulation can corrupt the visual representations used across tasks and action policies. Existing attacks on these models optimize against the victim's actions or predicted futures and therefore require access to target-model outputs. In this paper, we pro