arXiv AI October 31, 2026 By Xu Zhang, Ziqing Hu, Shuo Han, Ren Wang

Jailbreak Connectivity: Towards Diverse, Transferable, and Universal Multimodal Large Language Model Jailbreak

The paper introduces Jailbreak Connectivity (JC), a framework that exploits low‑loss pathways in image space to generate continuous adversarial paths for multimodal large language models (MLLMs). JC produces diverse jailbreak images, uses lightweight surrogate classifiers for cross‑model transfer, and applies a distribution‑level objective to create universal attacks across harmful queries. On the MM‑SafetyBench benchmark, JC achieves high attack success rates—84.01%, 85.77%, and 97.95% on Qwen3‑VL‑8B‑Instruct, InternVL3.5‑8B, and Gemma‑3‑12B‑IT respectively—outperforming existing baselines by up to 68.36 percentage points, and improves black‑box attack success on GPT‑5.6 and Gemini 3.1 Pro by 24.69 and 27.34 percentage points.

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