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Oct 12 · Monday

All papers

Found 4 papers
Filter4

Attack type

Prompt injection4

Impact

Unauthorized actions3

System type

AI Agent4

Attack surface / entry point

User input21Web pages and interfaces8Documents and messages11Tool responses8Tool and skill descriptions4Memory writes10Inter-agent messages5Images, audio and video16Distribution channels3

Layer

Application layer4

System component

MCP and skills10Memory1Monitors2Vision1
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 232papers have no tags yet and are not included in filtered results.
Attack surface / entry point：Tool and skill descriptionsSystem component：MemorySystem component：MonitorsSystem component：VisionClear all

Another 232papers do not have category tags yet and are not included in filtered results.

  1. Evaluation and benchmarksarXiv 2610.03153

    EvoRiskBench: An evolutionary benchmark for runtime security risks of workspace agents

    构建 EvoRiskBench 评测工作区 Agent 的运行时安全风险

    arXiv
    2610.03153
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    GPT-5.6 Sol, DeepSeek-V4-Pro-0813, Claude Opus 5

    Abstract论文构建了工作空间智能体运行时风险基准 EvoRiskBench，发现高执行力智能体在 IPI 下面临较高安全风险。

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  2. Evaluation and benchmarksarXiv 2609.35912

    MMSkillRisk：多模态技能图像中的隐藏指令可诱导 Agent 执行未授权操作

    提出 MMSkillRisk 基准，评测多模态技能图像中的隐藏指令诱导 Agent 越权操作

    arXiv
    2609.35912
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    GPT-5.6-sol, Kimi-K2.6, Kimi-K3 and others6

    Abstract论文提出 MMSkillRisk 基准与 NCVA 攻击，揭示多模态技能中教学图像潜藏指令可引发未授权操作并与良性任务共存。

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  3. Evaluation and benchmarksarXiv 2605.17380

    Uber proposes ADR enterprise-level agent detection system, deployed on 7,200 hosts for ten months

    提出企业级检测系统 ADR，识别编程 Agent 的提示注入与恶意 MCP

    arXiv
    2605.17380
    Published
    Layer
    Application layer
    Setting
    coding agent

    Abstract提出了端点因果遥测、两级在线分流与离线红队闭环的 ADR 系统，并在生产环境中验证了有效性。

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  4. Evaluation and benchmarksarXiv 2601.05755

    VIGIL: defending LLM agents against tool-flow injection with "verify before submit"

    提出 VIGIL 先验证后提交，防御 Agent 工具流注入

    arXiv
    2601.05755
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    Qwen3-max, Gemini-2.5-pro
    AbstractVIGIL 用意图约束下的提交前验证处理工具流注入。

    VIGIL 用 verify-before-commit 处理智能体的工具流注入，并配有基准 SIREN。

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