Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability

Quantized Vision-Language-Action (VLA) models expose a weight-fault surface: Rowhammer-style faults can corrupt deployed INT8 bits. We present the first bit-flip attack on a VLA: a few gradient-selected flips reduce closed-loop success to 0%, while hundreds of random flips are harmless. Across four model variants spanning three action-head families, damaging bits concentrate in a few action-generating layers, but the empirical budget depends sharply on the head: direct regression and token policies fall in 1--5 flips, whereas the evaluated flow-matching policies require {\sim}100--300. Our fixed-direction manifold-escape loss cuts \pizero{}'s budget from {\sim}1000 to {\sim}100 flips, and a matched five-direction sweep shows that the attack is not specific to an all-positive direction. On a direct head, protecting 3.1% of weights preserves 60% success at K{=}100, and protecting 5.3% moves the open-loop break threshold from 3 to 100 flips. Finally, task-calibrated emulated K{=}100 flips yield 0/20 real-robot successes, versus 14/20 clean and 16/20 global-random. Weight integrity is therefore a security boundary for embodied foundation models. Code is included as ancillary material.