The widespread presence of incomplete modalities in multimodal data poses a significant challenge to achieving accurate rumor detection. Existing multimodal rumor detection methods primarily focus on learning joint modality representations from complete multimodal training data, rendering them ineffective in addressing the common occurrence of missing modalities in real-world scenarios. In this paper, we propose a hierarchical soft prompt model TriSPrompt, which integrates three types of prompts, i.e., modality-aware (MA) prompt, modality-missing (MM) prompt, and mutual-views (MV) prompt, to effectively detect rumors in incomplete multimodal data. The MA prompt captures both heterogeneous information from specific modalities and homogeneous features from available data, aiding in modality recovery. The MM prompt models missing states in incomplete data, enhancing the model’s adaptability to missing information. The MV prompt learns relationships between subjective (i.e., text and image) and objective (i.e., comments) perspectives, effectively detecting rumors. Extensive experiments on three real-world benchmarks demonstrate that TriSPrompt achieves an accuracy gain of over 13% compared to state-of-the-art methods. The codes and datasets are available at https: //anonymous.4open.science/r/code-3E88.