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Oct 12 · Monday

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Found 4 papers
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Harmful content4Unauthorized actions25Data and privacy leaks3Output manipulation15Availability and reliability5Oversight failure20Increased misuse capability6

System type

Models and APIs7AI Agent4
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Impact：Harmful contentSystem type：AI AgentClear all

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  1. Risky behaviorarXiv 2609.38971

    研究测量并预测具身 VLM 规划器中任务的拒答可变性

    测量并预测具身 VLM 规划器的任务拒答可变性

    arXiv
    2609.38971
    Published
    Layer
    Model layer
    Setting
    Embodied systems and robotics
    Evaluated models
    gemini-robotics-er-2-preview, gemini-robotics-er-1.6-preview
    Abstract日常物体能翻转一部分已给出的拒答，可翻转程度取决于任务且可被本地代理预测。

    作者测量 constitution 守护的具身 VLM 规划器：一次拒答在只往场景里放一个日常物体之后还保不保持得住，并把这种易翻转程度称为任务的 malleability。

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  2. Risky behaviorarXiv 2609.38296

    印第安纳大学 OSoMe 研究：LLM 之间顺着原有立场共振比硬说服更易激进化

    比较智能体对话中共振与说服对信念激进化的效果

    arXiv
    2609.38296
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems
    Evaluated models
    Llama-3.1-8B-Instruct, Qwen3-8B
    Abstract两智能体实验中，作者称共振比说服更能激进化，并会波及相关信念。

    作者用两个 LLM 智能体的对话，考察一方能否把另一方扮演的人格信念推向更极端。

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  3. Risky behaviorarXiv 2609.35291

    窄域多模态微调可诱发涌现性失配，覆盖 15 个视觉语言模型

    证明无显式危害的图文窄微调可诱发跨任务涌现未对齐

    arXiv
    2609.35291
    Published
    Layer
    Model layer
    Setting
    Browser and computer use
    Evaluated models
    GPT-4o, GPT-4.1, Gemini 2.5 and others15
    Abstract证实窄多模态微调可引发全面的多渠道未对齐行为转变，揭示了后训练阶段对齐被隐蔽重塑的安全隐患。

    论文系统探究了视觉-语言模型中由窄任务微调诱发的跨模态涌现未对齐（Emergent Misalignment, EM）现象。

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  4. Risky behaviorarXiv 2609.27900

    论文揭示多智能体委派结构放大 LLM 安全风险

    评测多智能体委托结构如何放大模型的安全拒答失效

    arXiv
    2609.27900
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems
    Evaluated models
    Claude-Sonnet-4.6, GPT-5, Gemini-3.1-Pro and others6

    Abstract论文揭示多智能体层级委托会导致单模型安全对齐失效，多项前沿模型有害执行大幅增加且单层防御难以奏效。

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