Efficient LLM-Jailbreaking by Introducing Visual Modality
<|reference_start|>Efficient LLM-Jailbreaking by Introducing Visual Modality: This paper focuses on jailbreaking attacks against large language models (LLMs), eliciting them to generate objectionable content in response to harmful user queries. Unlike previous LLM-jailbreaks that directly orient to LLMs, our approach begins by constructing a multimodal large language model (MLLM) through the incorporation of a visual module into the target LLM. Subsequently, we conduct an efficient MLLM-jailbreak to generate jailbreaking embeddings embJS. Finally, we convert the embJS into text space to facilitate the jailbreaking of the target LLM. Compared to direct LLM-jailbreaking, our approach is more efficient, as MLLMs are more vulnerable to jailbreaking than pure LLM. Additionally, to improve the attack success rate (ASR) of jailbreaking, we propose an image-text semantic matching scheme to identify a suitable initial input. Extensive experiments demonstrate that our approach surpasses current state-of-the-art methods in terms of both efficiency and effectiveness. Moreover, our approach exhibits superior cross-class jailbreaking capabilities.<|reference_end|>
@article{niu2024efficient,
    title={Efficient LLM-Jailbreaking by Introducing Visual Modality},
    author={Zhenxing Niu, Yuyao Sun, Haodong Ren, Haoxuan Ji, Quan Wang, Xiaoke
  Ma, Gang Hua, Rong Jin},
    journal={arXiv preprint arXiv:2405.20015},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20015},
    primaryClass={cs.AI cs.CL}
}