GAMBIT: A Gamified Jailbreak Framework for Multimodal Large Language Models (2026.acl-long)
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| Challenge: | Existing attacks focus on increasing the complexity of the modified visual task and do not explicitly leverage the model’s own reasoning incentives. |
| Approach: | They propose a framework that decomposes and reassembles harmful visual semantics and constructs a gamified scene that drives the model to explore, reconstruct intent and answer as part of winning the game. |
| Outcome: | Experiments on reasoning and non-reasoning MLLMs show that the proposed framework outperforms baseline models on both vision and text. |
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| Challenge: | Recent research shows that multimodal large language models are vulnerable to jailbreak attacks . |
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Zonghao Ying, Deyue Zhang, Zonglei Jing, Yisong Xiao, Quanchen Zou, Aishan Liu, Siyuan Liang, Xiangzheng Zhang, Xianglong Liu, Dacheng Tao
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| Challenge: | Existing studies show that video inputs can bypass safety alignment, yet it remains unclear which properties of video input induce this vulnerability. |
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| Challenge: | Existing methods to jailbreak large vision-language models fail against cutting-edge models such as GPT-4o, despite having undergone safety alignment training. |
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Reference Attack: A New Cross-Modal Jailbreaking Attack against Multimodal Large Language Models (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have raised significant safety concerns about generated content, drawing attention from both academia and industry. |
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