This research introduces Adversarial Prompt Distillation (APD), a method to make large Vision-Language Models more robust against adversarial attacks in critical applications.

• Creates adversarially robust prompt embeddings for Vision-Language Models
• Specifically designed to enhance security in safety-critical domains like autonomous driving and medical diagnosis
• Improves upon existing Adversarial Prompt Tuning methods with cross-modal approach
• Addresses significant security vulnerabilities in current Vision-Language Models

For security professionals, this research offers practical defenses against attacks that could compromise AI systems in high-stakes environments, where visual misinterpretation could lead to catastrophic consequences.

Original Paper: Adversarial Prompt Distillation for Vision-Language Models