paperMarch 2025Unreviewed

MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models

Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Yujin Potter, Zhun Wang, Zhuowen Yuan, Alexander Xiong, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, J. Tan, Xuandong Zhao, Francesco Pinto, Zhen Xiang, Yu Gai, Zinan Lin, Dan Hendrycks, Bo Li, D. Song

arXiv.org

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Abstract

Multimodal foundation models (MMFMs) play a crucial role in various applications, including autonomous driving, healthcare, and virtual assistants. However, several studies have revealed vulnerabilities in these models, such as generating unsafe content by text-to-image models. Existing benchmarks on multimodal models either predominantly assess the helpfulness of these models, or only focus on limited perspectives such as fairness and privacy. In this paper, we present the first unified platfor

Cite

@misc{xu2025mmdt,
  title = {{MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models}},
  author = {Chejian Xu and Jiawei Zhang and Zhaorun Chen and Chulin Xie and Mintong Kang and Yujin Potter and Zhun Wang and Zhuowen Yuan and Alexander Xiong and Zidi Xiong and Chenhui Zhang and Lingzhi Yuan and Yi Zeng and Peiyang Xu and Chengquan Guo and Andy Zhou and J. Tan and Xuandong Zhao and Francesco Pinto and Zhen Xiang and Yu Gai and Zinan Lin and Dan Hendrycks and Bo Li and D. Song},
  year = {2025},
  month = mar,
  eprint = {2503.14827},
  archivePrefix = {arXiv},
  doi = {10.48550/arXiv.2503.14827},
  url = {https://www.semanticscholar.org/paper/26c02dbc2f6db3e3b7acdb493a880a3456ff2cfd}
}