Telecommunication fraud refers to deceptive activities in the field of communication services. This research focuses on a category of fraud identified as ''mutation telecommunication fraud". There is currently a lack of research on mutation telecommunication fraud detection, allowing this type of fraud to persist uncaught. We identify that detecting mutation fraud requires capturing multi-source patterns, including user communication graphs and temporal-spatial Voice of Call (VOC) features. Specifically, we introduce MutationGuard, which leverages Graph Neural Networks (GNN) to capture changes in user communication graphs. For VOC records, we map call start times onto a 3D cylindrical surface, thereby representing each VOC record in spatial coordinates and utilizing proposed LFFE and TCFE modules to capture local fraud behaviors and temporal behavior changes. The proposed neural modeling approach that facilitates multi-source information fusion constitutes a significant advancement in detecting mutation fraud. Experiment results reveal a significant improvement in the AUC score by 1.52% and the F1 score by 1.36% on the proposed telecommunication fraud dataset. Particularly, our method shows a significant improvement of 13.93% in accuracy on mutation fraud data. We also validate the effectiveness of our method on the publicly available Sichuan Telecommunication Fraud dataset.