Large Vision-Language Models (LVLMs) have demonstrated remarkable multimodal understanding capabilities, yet their inherent security vulnerabilities remain a pressing concern. Prior studies have indicated that enhancing chain-of-thought reasoning can inadvertently facilitate jailbreaks; however, existing attempts often rely on image masking, which only modestly extends reasoning depth and performs poorly against advanced commercial closed-source models. To bridge this gap, we propose an effective jailbreak method, Text–Image Segmentation and Reassembly (TISR). TISR disperses harmful semantic fragments across the visual modality while employing text-based role-playing to guide their reassembly. This strategic organization conceals surface-level toxicity while preserving malicious intent, thereby extending LVLM reasoning chains and enabling successful jailbreaks. Our experiments show that LVLMs, leveraging their visual understanding and reasoning abilities, can reconstruct complete harmful semantics while failing to uphold safety constraints. Extensive evaluations across massive LVLMs demonstrate that the proposed TISR achieves state-of-the-art attack performance, surpassing prior multi-modal jailbreak methods. Specifically, it attains average Attack Success Rates (ASR) exceeding 90% across three benchmark datasets on four leading commercial LVLMs.

Parameter-efficient fine-tuning (PEFT) has become a common method for fine-tuning large language models, where a base model can serve multiple users through PEFT module switching. To enhance user experience, base models require periodic updates. However, once updated, PEFT modules fine-tuned on previous versions often suffer substantial performance degradation on newer versions. Re-tuning these numerous modules to restore performance would incur significant computational costs. Through a comprehensive analysis of the changes that occur during base model updates, we uncover an interesting phenomenon: continual training primarily affects task-specific knowledge stored in Feed-Forward Networks (FFN), while having less impact on the task-specific pattern in the Attention mechanism. Based on these findings, we introduce Trans-PEFT, a novel approach that enhances the PEFT module by focusing on the task-specific pattern while reducing its dependence on certain knowledge in the base model. Further theoretical analysis supports our approach. Extensive experiments across 7 base models and 12 datasets demonstrate that Trans-PEFT trained modules can maintain performance on updated base models without re-tuning, significantly reducing maintenance overhead in real-world applications.