Detect and Suppress: A Mechanistic Defense against Adversarial Patches in VLA Models
Overview
This research paper introduces a defense mechanism for Vision-Language-Action (VLA) models against adversarial patches. By using a sparse autoencoder to identify internal features that correlate with attacks, the authors propose suppressing these features only when an attack is detected via a linear probe. Evaluated on the LIBERO-10 benchmark, this conditional intervention improves success rates under intermittent attacks while avoiding performance degradation associated with continuous suppression, demonstrating that targeted control of internal representations can enhance VLA robustness without fine-tuning.
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- arXiv Robotics — research abstractsSignalDetect and Suppress: A Mechanistic Defense against Adversarial Patches in VLA Models
This research paper introduces a defense mechanism for Vision-Language-Action (VLA) models against adversarial patches. By using a sparse autoencoder to identify internal features that correlate with attacks, the authors propose suppressing these features only when an attack is detected via a linear probe. Evaluated on the LIBERO-10 benchmark, this conditional intervention improves success rates under intermittent attacks while avoiding performance degradation associated with continuous suppression, demonstrating that targeted control of internal representations can enhance VLA robustness without fine-tuning.
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