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New LLMail-Inject dataset reveals high success rate for adaptive prompt injection attacks

By PulseAugur Editorial · [1 sources] ·

A new dataset called LLMail-Inject has been released, featuring over 200,000 human-written prompts designed to test adaptive prompt injection attacks against large language models. This dataset aims to address the limitations of existing benchmarks by capturing the complexity and adaptability of real-world adversaries. Initial evaluations show that even models specifically trained against prompt injection, such as Meta SecAlign, and advanced models like GPT-4o and GPT-5 mini, exhibit significant success rates in executing unauthorized tasks from untrusted sources. AI

IMPACT Highlights critical vulnerabilities in LLMs, potentially driving new security research and defense strategies.

RANK_REASON The cluster describes a new academic paper introducing a dataset for evaluating LLM security against prompt injection attacks. [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 LLMail-Inject dataset reveals high success rate for adaptive prompt injection attacks

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The cluster describes a new academic paper introducing a dataset for evaluating LLM security against prompt injection attacks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sahar Abdelnabi, Aideen Fay, Ahmed Salem, Egor Zverev, Kai-Chieh Liao, Chi-Huang Liu, Chun-Chih Kuo, Jannis Weigend, Danyael Manlangit, Alex Apostolov, Haris Umair, Jo\~ao Donato, Masayuki Kawakita, Athar Mahboob, Tran Huu Bach, Tsun-Han Chiang, Myeongji… ·

    LLMail-Inject: A Dataset from a Realistic Adaptive Prompt Injection Challenge

    arXiv:2506.09956v2 Announce Type: replace-cross Abstract: Indirect prompt injection attacks exploit a fundamental weakness of LLMs: executing unauthorized tasks from untrusted sources. Despite the practical significance of this attack vector, existing benchmarks rely on small set…

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