Jailbreaking Attack against Multimodal Large Language Model

• URL: http://arxiv.org/abs/2402.02309v1
• Date: Sun, 4 Feb 2024 01:29:24 GMT
• Title: Jailbreaking Attack against Multimodal Large Language Model
• Authors: Zhenxing Niu and Haodong Ren and Xinbo Gao and Gang Hua and Rong Jin
• Abstract summary: This paper focuses on jailbreaking attacks against multi-modal large language models (MLLMs) A maximum likelihood-based algorithm is proposed to find an emphimage Jailbreaking Prompt (imgJP) Our approach exhibits strong model-transferability, as the generated imgJP can be transferred to jailbreak various models.
• Score: 69.52466793164618
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper focuses on jailbreaking attacks against multi-modal large language models (MLLMs), seeking to elicit MLLMs to generate objectionable responses to harmful user queries. A maximum likelihood-based algorithm is proposed to find an \emph{image Jailbreaking Prompt} (imgJP), enabling jailbreaks against MLLMs across multiple unseen prompts and images (i.e., data-universal property). Our approach exhibits strong model-transferability, as the generated imgJP can be transferred to jailbreak various models, including MiniGPT-v2, LLaVA, InstructBLIP, and mPLUG-Owl2, in a black-box manner. Moreover, we reveal a connection between MLLM-jailbreaks and LLM-jailbreaks. As a result, we introduce a construction-based method to harness our approach for LLM-jailbreaks, demonstrating greater efficiency than current state-of-the-art methods. The code is available here. \textbf{Warning: some content generated by language models may be offensive to some readers.}

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