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English(EN) PIDS-Bench: Evaluating Prompt-Injection Detectors Under Over-Defense, Obfuscation, and Distribution Shift

新基准 PIDS-Bench 揭示提示注入检测器的缺陷

研究人员推出了 PIDS-Bench,这是一个旨在更严格地评估提示注入检测器的新基准。与依赖聚合 F1 分数的前几种方法不同,PIDS-Bench 在多个维度上评估检测器,包括过度防御、混淆和分布偏移。这种方法表明,即使是 F1 分数很高的检测器,也可能错误分类相当一部分良性提示,特别是那些模仿注入结构或来自外部来源的提示。 AI

影响 强调了为 AI 安全工具(尤其是在检测提示注入方面)需要更强大的评估方法。

排序理由 该集群描述了一篇介绍用于评估 AI 安全模型基准的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新基准 PIDS-Bench 揭示提示注入检测器的缺陷

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该集群描述了一篇介绍用于评估 AI 安全模型基准的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准

报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yusuf Khalid Shire, Sang-Chul Kim ·

    PIDS-Bench:在过度防御、混淆和分布偏移下评估提示注入检测器

    arXiv:2609.15017v1 Announce Type: cross Abstract: Prompt-injection detectors are typically evaluated using aggregate F1 on in-distribution test data, which offers limited insight into behavior under distribution shift, particularly on the benign side of the decision boundary, whe…