VLMGuard: Defending VLMs against Malicious Prompts via Unlabeled Data
Authors: name: Xuefeng Du, name: Reshmi Ghosh, name: Robert Sim, name: Ahmed Salem, name: Vitor Carvalho, name: Emily Lawton, name: Yixuan Li, name: Jack W. Stokes

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. To address the issue, we introduce VLMGuard, a novel
learning framework that leverages the unlabeled user prompts in the wild for
malicious prompt detection. These unlabeled prompts, which naturally arise when
VLMs are deployed in the open world, consist of both benign and malicious
information. To harness the unlabeled data, we present an automated
maliciousness estimation score for distinguishing between benign and malicious
samples within this unlabeled mixture, thereby enabling the training of a
binary prompt classifier on top. Notably, our framework does not require extra
human annotations, offering strong flexibility and practicality for real-world
applications. Extensive experiment shows VLMGuard achieves superior detection
results, significantly outperforming state-of-the-art methods. Disclaimer: This
paper may contain offensive examples; reader discretion is advised.