@inproceedings{wang-etal-2025-jailbreak,
    title = "Jailbreak Large Vision-Language Models Through Multi-Modal Linkage",
    author = "Wang, Yu  and
      Zhou, Xiaofei  and
      Wang, Yichen  and
      Zhang, Geyuan  and
      He, Tianxing",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-long.74/",
    pages = "1466--1494",
    ISBN = "979-8-89176-251-0",
    abstract = "With the rapid advancement of Large Vision-Language Models (VLMs), concerns about their {\textcompwordmark}potential misuse and abuse have grown rapidly. Prior research has exposed VLMs' vulnerability to jailbreak attacks, where carefully crafted inputs can lead the model to produce content that violates ethical and legal standards. However, current jailbreak methods often fail against cutting-edge models such as GPT-4o. We attribute this to the over-exposure of harmful content and the absence of stealthy malicious guidance. In this work, we introduce a novel jailbreak framework: Multi-Modal Linkage (MML) Attack. Drawing inspiration from cryptography, MML employs an encryption-decryption process across text and image modalities to mitigate the over-exposure of malicious information. To covertly align the model{'}s output with harmful objectives, MML leverages a technique we term evil alignment, framing the attack within the narrative context of a video game development scenario. Extensive experiments validate the effectiveness of MML. Specifically, MML jailbreaks GPT-4o with attack success rates of 99.40{\%} on SafeBench, 98.81{\%} on MM-SafeBench, and 99.07{\%} on HADES-Dataset. Our code is available at https://github.com/wangyu-ovo/MML."
}