  In the dynamic landscape of medical artificial intelligence, this study
explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP)
model, a Vision Language Foundation model, under targeted adversarial
conditions. Leveraging the Kather Colon dataset with 7,180 H&E images across
nine tissue types, our investigation employs Projected Gradient Descent (PGD)
adversarial attacks to intentionally induce misclassifications. The outcomes
reveal a 100% success rate in manipulating PLIP's predictions, underscoring its
susceptibility to adversarial perturbations. The qualitative analysis of
adversarial examples delves into the interpretability challenges, shedding
light on nuanced changes in predictions induced by adversarial manipulations.
These findings contribute crucial insights into the interpretability, domain
adaptation, and trustworthiness of Vision Language Models in medical imaging.
The study emphasizes the pressing need for robust defenses to ensure the
reliability of AI models.

Image and Video Processing;Computer Vision and Pattern Recognition;Tissues and Organs