Modern Vision-Language Models (VLMs) pose significant individual-level privacy risks by linking fragmented multimodal data to identifiable individuals through hierarchical chain-of-thought reasoning. However, existing privacy benchmarks remain structurally insufficient for this threat, as they primarily evaluate privacy perception while failing to address the more critical risk of privacy reasoning: a VLM's ability to infer and link distributed information to construct individual profiles. To address this gap, we propose MultiPriv, the first benchmark designed to systematically evaluate individual-level privacy reasoning in VLMs. We introduce the Privacy Perception and Reasoning (PPR) framework and construct a bilingual multimodal dataset with synthetic individual profiles, where identifiers (e.g., faces, names) are linked to sensitive attributes. This design enables nine challenging tasks spanning attribute detection, cross-image re-identification, and chained inference. We conduct a large-scale evaluation of over 50 open-source and commercial VLMs. Our analysis shows that 60 percent of widely used VLMs can perform individual-level privacy reasoning with up to 80 percent accuracy, posing a significant threat to personal privacy. MultiPriv provides a foundation for developing and assessing privacy-preserving VLMs.
@article{arxiv.2511.16940,
title = {MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models},
author = {Xiongtao Sun and Hui Li and Jiaming Zhang and Yujie Yang and Kaili Liu and Ruxin Feng and Wen Jun Tan and Wei Yang Bryan Lim},
journal= {arXiv preprint arXiv:2511.16940},
year = {2026}
}