Title: Model Stealing Attacks Against Vision-Language Models. Abstract: Vision-language models have flourished these years and are regarded as promising solutions to vision-language tasks. However, training vision-language models always requires enormous effort, making the models valuable intellectual properties (IPs). In this paper, we pioneer to propose the first model stealing attack against the vision-language models, the goal of which is to steal the functionality of the target models. Specifically, we target fine-tuned CLIP models with black-box access. We query the model to extract model information through either the text-to-image retrieval or the image-to-text retrieval API and then leverage the information to train a local copy of the target model. Experiments show the effectiveness of the model stealing attacks. We validate that our attacks are query-efficient, API-agnostic, data-agnostic, and architecture-agnostic, which broaden the attack scenarios. As a counterpart, we examine a defense based on the idea of out-of-distribution detection, which is impotent without strong assumptions. Our research pressures the unprotected release and prevalence of powerful vision-language models and appeals to the community that their IP protections, if not the least, cannot be less.