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Oct 12 · Monday

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Found 2 papers
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Attack type

Jailbreaks2Poisoning and backdoors19Model tampering13

Attack surface / entry point

User input4Training data2

System component

Fine-tuning and open weights2
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Attack type：JailbreaksAttack surface / entry point：Training dataSystem component：Fine-tuning and open weightsClear all

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  1. AttacksarXiv 2601.22169

    In Vino Veritas and Vulnerabilities：用醉酒语言诱导检验 LLM 安全

    用醉酒语言诱导改编对话模型并评测越狱与隐私泄露

    arXiv
    2601.22169
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    LLaMA2-7B, LLaMA3-8B, Mistral-7B and others5
    Abstract醉酒语言诱导在五个 LLM 上抬高越狱与部分隐私错误，作者将其视为攻击向量。

    作者把醉酒语言当作攻击向量，检验把 LLM 适配成醉酒发短信的模式后，越狱和情境隐私是否变差。动机是人类醉酒时的失范与泄密，以及 LLM 模拟人设的能力。

    Paper analysisRead analysis
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