Disintegrating the Visuomotor Dependency in World-Action Models

World Action Models (WAMs) integrate world modeling and action generation, offering a promising paradigm for robotic policy. While WAMs show strong potential, their robustness under adversarial attack remains underexplored. A defining characteristic of WAMs is the coupling between world modeling and action generation. How this coupling shapes action responses to semantic changes remains insufficiently characterized. We investigate how changes in visual observations propagate to downstream action generation. However, existing task-level metrics, e.g., success rate, primarily reflect final task outcomes, offering limited insight into the strength of this visuomotor dependency. We therefore introduce Visuomotor Sensitivity (VMS), which quantifies the magnitude of action responses to controlled semantic transformations. Using VMS, we compare attention-pathway interventions in WAMs and identify visual-to-action attention as an internal attack target. Based on these findings, we propose World-Action Disintegration (WAD), which combines visual-to-action attention suppression with a VMS-inspired surrogate targeting paired action differences across consecutive observations from the existing training dataset. Experiments show locally consistent VMS measurements and reproducibility across inference seeds. In simulation, we observe near-static behavior when minimizing the VMS-based surrogate and large-amplitude unstable motion when maximizing it; both conditions fail the task. WAD substantially degrades task performance across WAM architectures in simulation and in real-world trials. These findings characterize action responsiveness and its associated execution behaviors beyond aggregate action deviation and task success.