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A Model Merging Approach for Continual MLLM Unlearning
Yuhang Wang, Linlin Zhang, Haoxuan Ji +3
Multimodal large language model (MLLM) unlearning methods have been proposed to remove private, sensitive, or proprietary information from well-trained models. However, most existi…
Attention-Guided Reward for Reinforcement Learning-based Jailbreak against Large Reasoning Models
Zheng Lin, Zhenxing Niu, Haoxuan Ji +2
Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in solving complex problems by generating structured, step-by-step reasoning content. However, exposing a mo…
Re-Triggering Safeguards within LLMs for Jailbreak Detection
Zheng Lin, Zhenxing Niu, Haoxuan Ji +2
This paper proposes a jailbreaking prompt detection method for large language models (LLMs) to defend against jailbreak attacks. Although recent LLMs are equipped with built-in saf…
Guaranteed Jailbreaking Defense via Disrupt-and-Rectify Smoothing
Zheng Lin, Zhenxing Niu, Haoxuan Ji +1
This paper proposes a guaranteed defense method for large language models (LLMs) to safeguard against jailbreaking attacks. Drawing inspiration from the denoised-smoothing approach…
Null Space Constrained Contrastive Visual Forgetting for MLLM Unlearning
Yuhang Wang, Zhenxing Niu, Haoxuan Ji +3
The core challenge of machine unlearning is to strike a balance between target knowledge removal and non-target knowledge retention. In the context of Multimodal Large Language Mod…
Robust MLLM Unlearning via Visual Knowledge Distillation
Yuhang Wang, Zhenxing Niu, Haoxuan Ji +3
Recently, machine unlearning approaches have been proposed to remove sensitive information from well-trained large models. However, most existing methods are tailored for LLMs, whi…