MMJailBench: A Factorized Benchmark for Disentangling Multimodal Jailbreak Vulnerabilities

Abstract

Multimodal Large Language Models (MLLMs) are increasingly deployed in real-world applications, yet how different factors shape their jailbreak vulnerabilities remains poorly understood. Existing benchmarks often couple harmful intent, prompt framing, visual semantics, and instruction carrier within individual jailbreak instances, obscuring the specific sources of observed vulnerabilities. To address this limitation, we introduce MMJailBench, a factorized benchmark that systematically varies and

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@misc{wang2026mmjailbench,
  title = {{MMJailBench: A Factorized Benchmark for Disentangling Multimodal Jailbreak Vulnerabilities}},
  author = {Tianshi Wang and Jingsong Wang and Yafei Huang and Fengling Li and Xin Li and Lei Zhu},
  year = {2026},
  month = aug,
  eprint = {2608.25490},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.25490}
}