Hateful content in memes is difficult to detect automatically, as harmful intent often emerges from the interaction between image and text rather than either modality alone, a challenge compounded in Arabic by dialectal variation and sarcasm. This paper presents the system submitted by team Bahash-AI to Subtask A1 (binary hateful meme detection) of the ArGuard shared task at ArabicNLP 2026. A late-fusion classifier combining MARBERTv2 and ViT-base is first developed and evaluated as a standalone development-phase system. Building on this, the final submission ensembles a CLIP-based text-image classifier with this same late-fusion classifier, retrained on the combined train, development, and development-test partitions. Class imbalance (approximately 30% Hateful) is addressed via class-weighted losses, oversampling, and threshold selection. The final ensemble achieves a Macro-F1 of 0.7457 and an accuracy of 0.7680 on the blind test set, ranking 12th of 16 teams on the official leaderboard and outperforming the development-phase system by 9 Macro-F1 points. Error analysis using per-class precision and recall shows that the two systems reach a similar Hateful-class F1 gap through opposite error patterns.