Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench
MLLMU-Bench evaluates privacy risks in multimodal large language models via standardized leakage and inference benchmarks.
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MLLMU-Bench delivers a valuable multimodal privacy audit for real-world image leaks, though its vision-specific exposure patterns remain unclear and its defense-agnostic design risks reducing the multimodal gap to a data artifact.
Abstract
Zheyuan Liu, Guangyao Dou, Mengzhao Jia, Zhaoxuan Tan, Qingkai Zeng, Yongle Yuan, Meng Jiang. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.