We present our system for SemEval-2025 Task 9: Food Hazard Detection, a shared task focused on the explainable classification of food-incident reports. The task involves predicting hazard and product categories (ST1) and their exact vectors (ST2) from short texts. Our approach leverages zero-shot classification using the BART-large-MNLI model, which allows classification without task-specific fine-tuning. Our model achieves competitive performance, emphasizing hazard prediction accuracy, as evaluated by the macro-F1 score.