  Vision-Language Models (VLMs) combine visual and textual understanding,
rendering them well-suited for diverse tasks like generating image captions and
answering visual questions across various domains. However, these capabilities
are built upon training on large amount of uncurated data crawled from the web.
The latter may include sensitive information that VLMs could memorize and leak,
raising significant privacy concerns. In this paper, we assess whether these
vulnerabilities exist, focusing on identity leakage. Our study leads to three
key findings: (i) VLMs leak identity information, even when the vision-language
alignment and the fine-tuning use anonymized data; (ii) context has little
influence on identity leakage; (iii) simple, widely used anonymization
techniques, like blurring, are not sufficient to address the problem. These
findings underscore the urgent need for robust privacy protection strategies
when deploying VLMs. Ethical awareness and responsible development practices
are essential to mitigate these risks.
