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    <abstract>Prompt injection attacks pose a serious security risk to large language models in real-world applications. However, existing prompt injection benchmarks primarily focus on short-context inputs, leaving the attacks and defenses in long-context settings largely unexplored. This gap leads to a substantial overestimation of the effectiveness of current defenses. In this paper, we bridge the gap by introducing LongPIBench, a long-context benchmark for prompt injection covering 4 realistic application scenarios: paper peer review, resume screening, code review, and email summary. For each scenario, we construct a synthetic dataset and a real-world dataset, with context lengths ranging from thousands to tens of thousands of tokens. The evaluation results on LongPIBench reveal significant vulnerabilities of prompt injection defenses under long-context settings: even simple heuristic prompt injection attacks achieve high success rates and frequently bypass state-of-the-art defenses. We hope LongPIBench can serve as a practical benchmark for systematically evaluating prompt injection defenses in realistic long-context scenarios.</abstract>
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%0 Conference Proceedings
%T LongPIBench: A Long-Context Benchmark for Prompt Injection
%A Liu, Yupei
%A Jia, Yuqi
%A Gong, Neil Zhenqiang
%A Jia, Jinyuan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-longpibench
%X Prompt injection attacks pose a serious security risk to large language models in real-world applications. However, existing prompt injection benchmarks primarily focus on short-context inputs, leaving the attacks and defenses in long-context settings largely unexplored. This gap leads to a substantial overestimation of the effectiveness of current defenses. In this paper, we bridge the gap by introducing LongPIBench, a long-context benchmark for prompt injection covering 4 realistic application scenarios: paper peer review, resume screening, code review, and email summary. For each scenario, we construct a synthetic dataset and a real-world dataset, with context lengths ranging from thousands to tens of thousands of tokens. The evaluation results on LongPIBench reveal significant vulnerabilities of prompt injection defenses under long-context settings: even simple heuristic prompt injection attacks achieve high success rates and frequently bypass state-of-the-art defenses. We hope LongPIBench can serve as a practical benchmark for systematically evaluating prompt injection defenses in realistic long-context scenarios.
%U https://aclanthology.org/2026.findings-emnlp.1510/
%P 32132-32149

Markdown (Informal)

[LongPIBench: A Long-Context Benchmark for Prompt Injection](https://aclanthology.org/2026.findings-emnlp.1510/) (Liu et al., Findings 2026)

• LongPIBench: A Long-Context Benchmark for Prompt Injection (Liu et al., Findings 2026)

ACL

• Yupei Liu, Yuqi Jia, Neil Zhenqiang Gong, and Jinyuan Jia. 2026. LongPIBench: A Long-Context Benchmark for Prompt Injection. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 32132–32149, Budapest, Hungary. Association for Computational Linguistics.