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New benchmark RAG-PIBench evaluates prompt-injection detection in RAG systems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed RAG-PIBench, a new benchmark designed to evaluate the effectiveness of prompt-injection detection in retrieval-augmented generation (RAG) systems. The benchmark includes 4,876 contextual examples across distinct training, validation, and testing sets, employing a leakage-aware construction process. Evaluations showed that the DistilBERT model achieved the highest performance, with a F1 score of 0.896 and a PR-AUC of 0.968, though traditional methods like TF-IDF SVM and logistic regression also demonstrated competitive results. AI

IMPACT This benchmark will help improve the security of RAG systems against prompt injection attacks.

RANK_REASON The item is an academic paper detailing a new benchmark for AI security. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New benchmark RAG-PIBench evaluates prompt-injection detection in RAG systems

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The item is an academic paper detailing a new benchmark for AI security. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Niveen O. Jaffal, Ahmet Yuksel, David Mohaisen ·

    RAG-PIBench: A Leakage-Aware Benchmark for Prompt-Injection Detection in Trustworthy RAG Systems

    arXiv:2610.08571v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are vulnerable to prompt-injection attacks embedded in retrieved content. We introduce RAG-PIBench, a benchmark for RAG-style prompt-injection detection containing 4,876 contextual exam…

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