Amir Houmansadr

This paper demonstrates that benign fine-tuning significantly degrades safety in Audio LLMs, showing that the vulnerability is distinct from text and vision modalities and is highly dependent on the model's architecture.

This paper presents the first systematic study of membership inference attacks (MIAs) against Vision-Language-Action (VLA) models, demonstrating that these models are highly vulnerable to privacy breaches even when only observing generated actions.

This paper demonstrates that reasoning-enabled Vision-Language-Action (VLA) models for autonomous driving are highly vulnerable to realistic input perturbations, significantly compromising both reasoning accuracy and driving safety.