We propose a unified framework for not only attributing synthetic speech to its source but also for detecting speech generated by synthe sizers that were not encountered during training. This requires methods that move beyond simple detection to support both detailed foren- sic analysis and open-set generalization. To address this, we introduce SIGNAL, a hybrid framework that combines speech foundation models (SFMs) with graph-based modeling and open-set-aware inference. Our framework integrates Graph Neural Networks (GNNs) and a k-Nearest Neighbor (KNN) classifier, allowing it to capture meaningful relationships between utterances and recognize speech that doesn't belong to any known generator. It builds a dynamic graph where utterances are connected based on how closely their learned representations align, enabling the GNN to capture subtle relationships across samples. Meanwhile, the KNN branch handles open-set detection through confidence-based thresholding. We evaluate SIGNAL using the DiffSSD dataset, which offers a diverse mix of real speech and synthetic audio from both open-source and commercial diffusion-based TTS systems. To further assess generalization, we also test on the SingFake benchmark. Our results show that SIGNAL consistently improves performance across both tasks, with Mamba based embeddings delivering especially strong results. To the best of our knowledge, this is the first study to unify graph-based learning and open-set detection for tracing synthetic speech back to its origin.