New defense framework tackles multilingual prompt injection attacks

Researchers have developed MIPIAD, a defense framework to combat indirect prompt injection attacks in multilingual large language model systems. The framework combines a Qwen2.5-1.5B model fine-tuned with LoRA, TF-IDF lexical features, and an ensemble learning approach. Evaluated on English and Bangla, MIPIAD achieved a high F1 score of 0.9205 with a hybrid ensemble and an AUROC of 0.9378 with a boosting ensemble, demonstrating effectiveness in reducing the cross-lingual gap. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Introduces a novel defense against prompt injection attacks, enhancing the security of multilingual LLM applications.

RANK_REASON The cluster contains an academic paper detailing a new defense mechanism for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• MIPIAD
• Qwen2.5-1.5B
• English
• Bangla
• TF-IDF
• LoRA
• BIPIA

• paper
• safety