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Oct 12 · Monday

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Found 1 paper
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Attack technique

Multi-turn escalation0Encoding and obfuscation1

Model risks

Inappropriate refusal1
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Attack technique：Multi-turn escalationAttack technique：Encoding and obfuscationModel risks：Inappropriate refusalClear all

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  1. Evaluation and benchmarksarXiv 2601.00588

    CSSBench: Evaluating the safety of lightweight LLMs against Chinese-specific adversarial patterns

    提出 CSSBench 评测轻量模型对中文表面对抗改写的失守与过度拒绝

    arXiv
    2601.00588
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Evaluated models
    Qwen3-0.6B, Qwen3-1.7B, Qwen3-8B and others10
    AbstractCSSBench 用表面扰动和边界查询评轻量模型。

    CSSBench 评测轻量 LLM 在中文特有表面改写下的安全，并同时测量过度拒绝。要处理的缺口是：英文向护栏和只测干净查询的中文基准，看不清同音、拼音、符号混写、零宽字符这些写法下的失守，也分不清较低风险来自细粒度判断还是粗粒度拒答。作者称轻量模型更需要这种评测。

    Paper analysisRead analysis
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