  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.
