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The rapid growth of the digital economy in South-East Asia (SEA) has amplified the risks of audio deepfakes, yet existing datasets provide limited coverage of SEA languages, hindering robust detection. We present SEA-Spoof, the first large-scale audio deepfake detection dataset dedicated to six SEA languages: Tamil, Hindi, Thai, Indonesian, Malay, and Vietnamese. SEA-Spoof contains over 700 hours of paired real and spoof speech generated by diverse state-of-the-art open-source and closed-source systems. Its balanced, transcript aligned design enables controlled language and system level evaluation. Benchmarking reveals severe cross-lingual degradation of models trained on high resource languages, while fine-tuning on SEA-Spoof restores performance across languages and synthesis sources. SEA-Spoof establishes a foundation for robust, cross-lingual and region-aware deepfake detection in SEA.