Online social media platforms have become central to communication and information exchange, however, they also serve as fertile ground for hate speech, offensive language, and bullying targeting individuals and communities. Such content undermines online safety and inclusion, underscoring the need for reliable detection systems—especially in low-resource languages with limited moderation tools. For Bangla, existing work provides valuable resources and models, however, they are mostly single-task (e.g., binary hate/offense) with narrow coverage of key dimensions such as type, severity, and target. We address these gaps by introducing *the first multi-task* Bangla hate-speech dataset, *BanglaMultiHate*, one of the largest manually annotated dataset to date. Using this resource, we performed a comparative study across different baselines, monolingual pretrained models, and LLMs under zero-shot, few-shot, and LoRA fine-tuning settings. Our findings show that while LoRA-tuned LLMs rival BanglaBERT, culturally grounded pretraining remains crucial for robust performance. Overall, *BanglaMultiHate* establishes a stronger benchmark for hate speech detection in low-resource contexts. All data and scripts are released for reproducibility.

Online discourse in Bangla is rife with nuanced toxicity expressed through code-mixing, dialectal variation, and euphemism. Effective moderation thus requires fine-grained detection of hate type, target, and severity, rather than a binary label. To address this, we organized the Bangla Hate Speech Identification Shared Task at the BLP 2025 workshop, co-located with IJCNLP-AACL 2025, comprising three subtasks: (1A) hate-type detection, (1B) hate-target detection, and (1C) joint prediction of type, target, and severity in a multi-task setup. The subtasks attracted 161, 103, and 90 participants, with 36, 23, and 20 final submissions, respectively, while a total of 19 teams submitted system description papers. The submitted systems employed a wide range of approaches, ranging from classical machine learning to fine-tuned pretrained models and zero-/few-shot LLMs. We describe the task setup, datasets, and evaluation framework, and summarize participant systems. All datasets and evaluation scripts are publicly released.