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Oct 12 · Monday

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Impact

Harmful content6Data and privacy leaks9Output manipulation12Availability and reliability1

System type

Models and APIs4LLM applications1AI Agent4

Attack surface / entry point

User input1
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Impact：Availability and reliabilitySystem type：LLM applicationsAttack surface / entry point：User inputClear all

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  1. Evaluation and benchmarksarXiv 2609.15017

    PIDS-Bench：在过度防御、混淆与分布偏移下评测提示注入检测器

    提出 PIDS-Bench，评测提示注入检测器在过度防御与分布偏移下的表现

    arXiv
    2609.15017
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Evaluated models
    TF-IDF + logistic regression, DistilBERT, DeBERTa-v3-FT and others7
    AbstractPIDS-Bench 显示高分布内 F1 仍会过度拦截外部来源的安全相关良性输入。

    PIDS-Bench 是一个冻结的检测器级基准，用来在固定阈值下同时看提示注入检测和良性误报。

    Paper analysisRead analysis
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