Reliable 3D object detection is critical for autonomous driving, yet LiDAR-only methods often fail under adverse weather, occlusion, or sensor degradation. We introduce TriFusion, a GNN-based multi-modal fusion framework that integrates LiDAR, camera, and radar for robust 3D detection. Our approach builds a heterogeneous graph with nodes representing modality-specific features and edges encoding spatial and cross-modal correspondences, enabling attention based message passing across sensors. Evaluated on the nuScenes benchmark against leading baselines (e.g., PointPainting, MVX-Net, BEVFusion), TriFusion achieves superior accuracy and robustness in challenging conditions while maintaining efficiency. These results underscore the promise of graph-based fusion for reliable perception in autonomous driving.