Learning to Detect Unknown Jailbreak Attacks in Large Vision-Language Models

Shuang Liang; Zhihao Xu; Jialing Tao; Hui Xue; Xiting Wang

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arXiv · 2510.15430

Learning to Detect Unknown Jailbreak Attacks in Large Vision-Language Models

Shuang Liang·Zhihao Xu·Jialing Tao·Hui Xue·Xiting Wang

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Abstract

Despite extensive alignment efforts, Large Vision-Language Models (LVLMs) remain vulnerable to jailbreak attacks, posing serious safety risks. To address this, existing detection methods either learn attack-specific parameters, which hinders generalization to unseen attacks, or rely on heuristically sound principles, which limit accuracy and efficiency. To overcome these limitations, we propose Learning to Detect (LoD), a general framework that accurately detects unknown jailbreak attacks by shifting the focus from attack-specific learning to task-specific learning. This framework includes a Multi-modal Safety Concept Activation Vector module for safety-oriented representation learning and a Safety Pattern Auto-Encoder module for unsupervised attack classification. Extensive experiments show that our method achieves consistently higher detection AUROC on diverse unknown attacks while improving efficiency. The code is available at https://anonymous.4open.science/r/Learning-to-Detect-51CB.

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BibTeX RIS

Shuang Liang, Zhihao Xu, Jialing Tao, Hui Xue, Xiting Wang. 2025-10-17. Learning to Detect Unknown Jailbreak Attacks in Large Vision-Language Models. https://arxiv.org/abs/2510.15430

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