A Multimodal Automatic Redteaming Evaluation based on Atomic Jailbreak Strategy Decoupling and Combination

Abstract

Multimodal Large Language Models (MLLMs) have achieved impressive progress in image-text comprehension and generation, yet they remain susceptible to jailbreak attacks that can trigger harmful outputs and pose serious safety concerns. Existing multimodal jailbreak attacks have shown the feasibility of such attacks, but they still face two fundamental challenges: the lack of a atomic multi-modal strategy space, the absence of a concise and efficient executable framework beyond human-craft experie

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@misc{zhao2026multimodal,
  title = {{A Multimodal Automatic Redteaming Evaluation based on Atomic Jailbreak Strategy Decoupling and Combination}},
  author = {Shiji Zhao and Yuxuan Zhou and Chen Xiong and Dongxian Wu and Yang Bai and Xun Chen},
  year = {2026},
  month = aug,
  eprint = {2608.04034},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.04034}
}