VLMGuard: Bootstrapping Malicious Prompt Detectors from Unlabeled Vision-Language Prompts in the Wild

Understand

Vision-language Models (VLMs) are essential for contextual understanding of both visual and textual information.

• However, their vulnerability to adversarially manipulated inputs presents significant risks, leading to compromised outputs and raising concerns about the reliability in VLM-integrated applications.
• Detecting these malicious prompts is thus crucial for maintaining trust in VLM generations.
• A major challenge in developing a safeguarding prompt classifier is the lack of a large amount of labeled benign and malicious data.