Vision-language-action models are highly vulnerable to membership inference attacks, including practical black-box versions that exploit generated actions and motion trajectories.
Membership inference attacks against fine-tuned large language models via self-prompt calibration.Advances in Neural Information Processing Systems, 37:134981–135010
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Membership Inference Attacks on Vision-Language-Action Models
Vision-language-action models are highly vulnerable to membership inference attacks, including practical black-box versions that exploit generated actions and motion trajectories.