TNAS Diagnostic Exposes Silent Action Attacks on Reasoning VLA Models
The key implication is that oversight mechanisms relying on reading a model's language output give a false sense of safety, since action channels can be steered invisibly. Robotics and autonomous-driving teams deploying reasoning VLAs should add direct verification of predicted action sequences rather than textual rationales. The evaluation uses NVIDIA's Alpamayo driving stack alongside InstructVLA, making the finding relevant to embodied-AI safety benchmarks rather than chat-only models.