DMN: A Compositional Framework for Jailbreaking Multimodal LLMs with Multi-Image Inputs
Multimodal Large Language Models (MLLMs) are vulnerable to jailbreak attacks, which can elicit harmful responses from MLLMs. Many MLLMs support multi-image inputs, inadvertently introducing new vulnerabilities due to less efforts on multi-image safety alignment. Previous MLLM jailbreak methods only uses a single image, which restricts the attack space: they cannot distribute harmful requests across multiple images, carry abundant information, or exploit additional visual reasoning tasks to distract MLLMs. To address these limitations, in this paper, we propose a compositional jailbreak framework, \textbf{DMN}, which leverages \textbf{D}istributed instruction, \textbf{M}ultimodal evidence ...
| Authors | Wenzhuo Xu, Zhipeng Wei, Zonghao Ying et al. |
|---|---|
| Primary category | cs.CR |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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