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New defense framework tackles multilingual prompt injection attacks

By PulseAugur Editorial · [1 sources] ·

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

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New defense framework tackles multilingual prompt injection attacks

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

  1. arXiv cs.CL TIER_1 English(EN) · Mostafa Rifat Tazwar ·

    MIPIAD: Multilingual Indirect Prompt Injection Attack Defense with Qwen -- TF-IDF Hybrid and Meta-Ensemble Learning

    Indirect prompt injection remains a persistent weakness in retrieval-augmented and tool-using LLM systems, and the problem becomes harder to characterise in multilingual settings. We present MIPIAD, a defense framework evaluated on English and Bangla that combines a sequence clas…

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