Multi-Stage Vision Attack Detection for Efficient LVLM Defense

Large Vision-Language Models (LVLMs) can be vulnerable to adversarial images that subtly bias their outputs toward plausible yet incorrect responses. We introduce a general, efficient, and training-free defense that combines image transformations with agentic data consolidation to recover correct model behavior.

M.Sc. student under the supervision of Prof. Ayellet Tal.