VLA DefenseExpert
Detect and Suppress: A Mechanistic Defense against Adversarial Patches in VLA Models
Preprint. The authors build an inference-time defense for vision-language-action (VLA) policies against adversarial image patches by training a sparse autoencoder, identifying an internal feature that tracks patch presence, and suppressing that feature only when a linear probe detects an attack. On LIBERO-10 under intermittent UADA attacks, conditional suppression raises success rate, e.g. from 26.0% to 32.4% for π0.5 at n=10, while avoiding the large benign-condition drop caused by continuous suppression. This points to lightweight, fine-tuning-free robustness layers for deployed robot policies.