Merging multiple Low-Rank Adaptation (LoRA) experts into a single backbone is a promising approach for efficient multi-task deployment. While existing methods strive to alleviate interference via weight interpolation or subspace alignment, they rest upon the implicit assumption that all LoRA matrices contribute constructively to the merged model. In this paper, we uncover a critical bottleneck in current merging paradigms: the existence of negative modules—specific LoRA layers that inherently degrade global performance upon merging. We propose Evolutionary Negative Module Pruning (ENMP), a plug-and-play LoRA pruning method to locate and exclude these detrimental modules prior to merging. By leveraging an evolutionary search strategy, ENMP effectively navigates the discrete, non-differentiable landscape of module selection to identify optimal pruning configurations. Extensive evaluations demonstrate that ENMP consistently boosts the performance of existing merging algorithms, achieving a new state-of-the-art across both language and vision domains. Code is available at https://github.com/CaoAnda/ENMP-LoRAMerging.

Text-to-image (T2I) models can be maliciously used to generate harmful content such as sexually explicit, unfaithful, and misleading or Not-Safe-for-Work (NSFW) images. Previous attacks largely depend on the availability of the diffusion model or involve a lengthy optimization process. In this work, we investigate a more practical and universal attack that does not require the presence of a target model and demonstrate that the high-dimensional text embedding space inherently contains NSFW concepts that can be exploited to generate harmful images. We present the Jailbreaking Prompt Attack (JPA). JPA first searches for the target malicious concepts in the text embedding space using a group of antonyms generated by ChatGPT. Subsequently, a prefix prompt is optimized in the discrete vocabulary space to align malicious concepts semantically in the text embedding space.We further introduce a soft assignment with gradient masking technique that allows us to perform gradient ascent in the discrete vocabulary space.We perform extensive experiments with open-sourced T2I models, e.g. stable-diffusion-v1-4 and closed-sourced online services, e.g. DALL·E 2 and Midjourney with black-box safety checkers. Results show that (1) JPA bypasses both text and image safety checkers, (2) while preserving high semantic alignment with the target prompt. (3) JPA demonstrates a much faster speed than previous methods and can be executed in a fully automated manner. These merits render it a valuable tool for robustness evaluation in future text-to-image generation research.