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Oct 12 · Monday

All papers

Found 3 papers
Filter5

Attack type

Prompt injection3

Model risks

Misalignment and value divergence1Hallucination and fabrication2

Impact

Unauthorized actions3

System type

AI Agent5coding agent2science agent1

Layer

Application layer3
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Attack type：Prompt injectionModel risks：Misalignment and value divergenceModel risks：Hallucination and fabricationSystem type：coding agentSystem type：science agentClear all

Another 233papers do not have category tags yet and are not included in filtered results.

  1. Evaluation and benchmarksarXiv 2609.32616

    Cave-Bench：虚假指控下 LLM 智能体最高 60.06% 的运行会破坏正确工作

    提出 CAVE-BENCH，评测智能体在虚假指责下破坏已完成的正确工作

    arXiv
    2609.32616
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    Claude-Sonnet-5, Claude-Opus-5, GPT-5.6-Sol and others14

    Abstract论文提出了评估智能体在虚假指责下破坏已有正确成果的 CAVE-BENCH，发现强模型易推翻已知证据，提出了门控缓解方案。

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  2. DefensesarXiv 2609.24165

    APEXA 为同步辐射数据处理的 LLM 多智能体加装执行完整性护栏

    提出 APEXA，在工具层拒绝无执行支撑的结果并拦截电机命令

    arXiv
    2609.24165
    Published
    Layer
    Application layer
    Setting
    science agent
    Evaluated models
    GPT-5-mini, GPT-5.4, Claude Opus 4.7 and others4
    Abstract作者称执行是否发生要由工具层核对，而不能只靠提示词。

    APEXA 是先进光子源上的数据还原框架，用工具层代码约束语言模型智能体的工具调用。

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  3. AttacksarXiv 2607.12340

    Research: LLM agent skill-name hallucination rates reach up to 62%, which can be exploited via squatting for supply-chain attacks

    提出技能名称抢注攻击，利用 Agent 虚构技能名实施供应链劫持

    arXiv
    2607.12340
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    GLM-4.5-Air, GPT-5.4-mini, Claude Sonnet 4.6 and others12
    Abstract研究证实智能体虚构技能名具有高发生率与可预测性，现有防御难以兼顾安全与效用，亟需注册表结构性防护。

    本研究系统揭示了大模型智能体技能推荐场景下的虚构技能名称安全风险，将其形式化为一种新型供应链抢注攻击。

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