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This paper presents MIPIAD, a multilingual defense framework against indirect prompt injection attacks using a hybrid of Qwen2.5-based classifiers and TF-IDF features with meta-ensemble learning. It demonstrates strong performance on English and Bangla benchmarks, achieving high F1 and AUROC scores while reducing cross-lingual gaps.