Bit-flip attacks on vision-language-action models: vulnerability depends on action-decoding design
Researchers report the first bit-flip attack against a vision-language-action model, showing that Rowhammer-style faults can corrupt INT8 weights in deployed quantized systems. Using only a few gradient-selected bit flips, they drive the policy's closed-loop success rate down to near zero. The paper finds that a VLA model's action-decoding architecture largely determines how exposed it is to such faults.