@inproceedings{xu-etal-2026-dmn,
title = "{DMN}: A Compositional Framework for Jailbreaking Multimodal {LLM}s with Multi-Image Inputs",
author = "Xu, Wenzhuo and
Wei, Zhipeng and
Ying, Zonghao and
Zhang, Deyue and
Yang, Dongdong and
Zhang, Xiangzheng and
Zou, Quanchen",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.514/",
pages = "11205--11221",
ISBN = "979-8-89176-390-6",
abstract = "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 and a $\textbf{N}$umber chain task to fully enhance the jailbreak performance. Extensive experiments show that DMN is highly effective for MLLM jailbreaking, e.g. achieving attack success rates of over 90{\%} on GPT-4o, Gemini-2.5-pro and Claude Sonnet 4, surpassing other baselines by a large margin. This compositional, multi-image jailbreak strategy reveals fundamental weaknesses in their safety mechanisms."
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<abstract>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, DMN, which leverages Distributed instruction, Multimodal evidence and a Number chain task to fully enhance the jailbreak performance. Extensive experiments show that DMN is highly effective for MLLM jailbreaking, e.g. achieving attack success rates of over 90% on GPT-4o, Gemini-2.5-pro and Claude Sonnet 4, surpassing other baselines by a large margin. This compositional, multi-image jailbreak strategy reveals fundamental weaknesses in their safety mechanisms.</abstract>
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%0 Conference Proceedings
%T DMN: A Compositional Framework for Jailbreaking Multimodal LLMs with Multi-Image Inputs
%A Xu, Wenzhuo
%A Wei, Zhipeng
%A Ying, Zonghao
%A Zhang, Deyue
%A Yang, Dongdong
%A Zhang, Xiangzheng
%A Zou, Quanchen
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F xu-etal-2026-dmn
%X 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, DMN, which leverages Distributed instruction, Multimodal evidence and a Number chain task to fully enhance the jailbreak performance. Extensive experiments show that DMN is highly effective for MLLM jailbreaking, e.g. achieving attack success rates of over 90% on GPT-4o, Gemini-2.5-pro and Claude Sonnet 4, surpassing other baselines by a large margin. This compositional, multi-image jailbreak strategy reveals fundamental weaknesses in their safety mechanisms.
%U https://aclanthology.org/2026.acl-long.514/
%P 11205-11221
Markdown (Informal)
[DMN: A Compositional Framework for Jailbreaking Multimodal LLMs with Multi-Image Inputs](https://aclanthology.org/2026.acl-long.514/) (Xu et al., ACL 2026)
ACL
- Wenzhuo Xu, Zhipeng Wei, Zonghao Ying, Deyue Zhang, Dongdong Yang, Xiangzheng Zhang, and Quanchen Zou. 2026. DMN: A Compositional Framework for Jailbreaking Multimodal LLMs with Multi-Image Inputs. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11205–11221, San Diego, California, United States. Association for Computational Linguistics.