UAV tracking faces significant challenges in real-world scenarios, such as small-size targets, complex backgrounds, and occlusions, which limit the performance of RGB-based trackers. Multispectral images (MSI), which capture additional spectral information, offer a promising solution to these challenges. However, progress in this area has been hindered by the lack of relevant datasets. To address this gap, we introduce the first large-scale dataset for Multispectral UAV Single Object Tracking (MUST), which includes 250 video sequences spanning diverse environments and challenging scenarios, providing a comprehensive data foundation for multispectral UAV tracking. We also propose a novel tracking framework, UNTrack, which integrates spectral, spatial, and temporal features using spectrum prompts, initial templates, and sequential searches. UNTrack employs an asymmetric transformer with a spectral background eliminate mechanism for optimal relationship modeling and an encoder that continuously updates the spectrum prompt to refine tracking, improving both accuracy and efficiency. Extensive experiments show that UNTrack outperforms state-of-the-art UAV trackers. We believe our dataset and framework will drive future research in this area.