We present ArA-DF 2026, the first shared task for Arabic speech deepfake detection, held at ArabicNLP-2026. Unlike existing benchmarks that conflate all distribution shift into a single evaluation set, ArA-DF 2026 separates dialect generalization (Track 1: unseen Arabic dialects and speakers) from acoustic robustness (Track 2: codec compression, noise, reverberation, re-encoding). We release 43,500 labeled training utterances spanning MSA and regional dialects, plus blind evaluation sets of 144,210 (Track 1) and 127,746 (Track 2) utterances, ranking systems by EER. Fourteen teams competed on Track 1 and twelve on Track 2, with eight system description papers submitted. The best submissions cut EER from 14.54% to 0.87% (Track 1) and from 27.11% to 0.66% (Track 2). Across independent submissions, three findings recur: augmentation and data curation move performance far more than architecture; simpler pooling heads often outperform graph-attention back-ends once augmentation is applied; and external synthetic Arabic speech with mismatched generation artifacts hurts rather than helps. Residual errors concentrate on short utterances and on spoofed audio misclassified as bona fide.