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arXiv · 2605.27932

When Think-with-Image Meets Safety: What Determines Multimodal Jailbreak Robustness?

Yuan Tian·Bing Hu·Fang Wu·Xiaomin Li·Binghang Lu·Neil Zhenqiang Gong
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Abstract

Think-with-image reasoning is emerging as a new inference paradigm for large vision-language models, but its safety implications remain poorly understood. Existing systems already span multiple process designs, including direct response generation, text-only prior turn, visual-state manipulation, and explicit external image-tool invocation. In this paper, we ask which of these evaluated paradigms improves multimodal jailbreak robustness, and why. Across multiple vision-language models, explicit image-tool interaction yields the lowest attack success rates in our experiments, reducing jailbreak success by around 30% relative on average across the evaluated models. This finding is initially surprising: ASR remains low even when the returned image-tool output is manually overridden or itself unsafe-looking, but returns near direct-answering levels under text-only prior turn controls. These results indicate that the lower ASR is not explained by benign returned-image semantics or by the textual image-tool trace alone. To explain the pattern, we introduce an image-tool safety vector framework that models image-tool invocation as a residual shift in hidden representations toward a safety-relevant direction. Representation-level analyses and activation interventions support this account. Overall, our results suggest that explicit image-tool interaction is a promising design pattern for improving jailbreak robustness, while also motivating pipeline-specific safety evaluation.

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BibTeXRIS

Yuan Tian, Bing Hu, Fang Wu, Xiaomin Li, Binghang Lu, Neil Zhenqiang Gong. 2026-05-27. When Think-with-Image Meets Safety: What Determines Multimodal Jailbreak Robustness?. https://arxiv.org/abs/2605.27932

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