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Oct 12 · Monday

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  1. AttacksarXiv 2610.06093

    研究评测训练数据提取风险的跨语言迁移

    用翻译前缀做跨语言训练数据提取,恢复英语训练文本中的 PII

    Published
    Evaluated models
    GPT-Neo 1.3B, GPT-Neo 2.7B, GPT-J 6B and others7
    Abstract跨语言 TDE 能恢复英语记忆的 PII,转移随多语训练和措辞保真变化。

    这篇工作检验训练数据抽取是否跨语言:英语段落里记住的 PII,能否被其他语言的前缀逐字抽出。

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  2. AttacksarXiv 2608.09867

    Researchers steal proprietary LLM inference chains via cross-model compatibility of encrypted inference blocks

    利用加密思维块的跨模型重放,诱导弱模型逐字转写专有推理链

    Published
    Attacker
    Black-box
    Evaluated models
    Claude Opus 4.8, Claude Sonnet 4.6, Claude Haiku 4.5 and others13

    Abstract论文揭示了客户端加密思维块跨模型互换漏洞,提出了低成本提取方法并给出防御方案。

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  3. AttacksarXiv 2604.01039

    Evaluating and hardening LLM system instructions against encoding attack extraction

    Extract system instructions with encoding and structured requests, and harden them by rewriting with CoT

    Published
    Attacker
    Black-box
    Evaluated models
    GPT-4.1-mini, GPT-3.5-turbo, Gemini-2.5-flash and others7
    AbstractEncoded requests can elicit system instructions after a direct refusal.

    This work evaluates and hardens the confidentiality of LLM system instructions under encoded extraction. The authors are concerned with: when direct extraction has been rejected, whether the model still surrenders protected content when the same request is rewritten as a formatting or encoding task.

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  4. AttacksarXiv 2601.12983

    ChartAttack:测试大语言模型在图表生成中对恶意提示的脆弱性

    提出 ChartAttack,用恶意提示驱动代码模型生成误导图表

    Published
    Evaluated models
    Claude-4.6-Sonnet, GPT-4o, Gemini-2.5-Flash and others20
    AbstractChartAttack 生成不改数据的误导图,降低 MLLM 与人类的图表问答准确率。

    ChartAttack 研究恶意提示能否让 MLLM 在不改数据的情况下生成误导图表,并量化这些图对其他 MLLM 和人类图表问答的影响。

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  5. AttacksarXiv 2512.16059

    ContextLeak:审计私有上下文学习方法的隐私泄露

    提出 ContextLeak,用金丝雀成员推断审计私有 ICL 泄漏

    Published
    Attacker
    Black-box
    Evaluated models
    Llama3.3-70b, Qwen2.5-72b, GPT-4.1 and others6
    AbstractContextLeak 审计私有 ICL 的 worst-case 泄漏。

    ContextLeak 是一套用成员推断审计私有 ICL 的框架,目标是给 worst-case 泄漏一个经验下界,而不是只引用理论 ε。

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