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Oct 13 · Tuesday

All papers

Found 12 papers
Filter5

Attack type

Prompt injection12

Defense type

Detection and filtering3Instruction–data separation4

Impact

Unauthorized actions9

System type

Models and APIs14LLM applications23AI Agent165coding agent14web agent8Browser and computer use4Multi-agent systems16

Attack surface / entry point

User input6Web pages and interfaces29Documents and messages6Tool responses5Memory writes2Images, audio and video7

Layer

Application layer12

System component

Guardrails and judges3
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 201papers have no tags yet and are not included in filtered results.
System type：web agentSystem type：Browser and computer useAttack surface / entry point：Documents and messagesAttack surface / entry point：Tool responsesAttack surface / entry point：Memory writesClear all

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  2. DefensesarXiv 2610.03089

    COBRA: A Dual-LLM Architecture for Defending Computer-Using Agents Against Branch-Jumping Attacks

    提出 COBRA 双 LLM 架构，防御计算机使用 Agent 的分支转向攻击

    arXiv
    2610.03089
    Published
    Layer
    Application layer
    Setting
    Browser and computer use
    Evaluated models
    gpt-5, UI-TARS-1.5-7B, claude-sonnet-5 and others6

    Abstract提出分支转向防御架构 COBRA，通过事前能力约束与边界代理消除分支转向攻击并兼顾效用。

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  4. DefensesarXiv 2608.24017

    WebMCP-Phalanx：为浏览器内 LLM Agent 工具调用建立信任边界

    提出 WebMCP-Phalanx，用能力凭证与双智能体隔离浏览器工具调用

    arXiv
    2608.24017
    Published
    Layer
    Application layer
    Setting
    web agent
    AbstractPhalanx 用浏览器凭证绑定工具所有权，Q/P 分离拦住描述注入，工具名与返回值仍有残留。

    WebMCP-Phalanx针对浏览器里的 WebMCP 智能体：在 SOP 下补工具所有权，并把语义检查从有工具权的执行中分开。

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  5. AttacksarXiv 2608.04565

    Researchers propose Breadcrumbing long-range attack that can hijack the evidence chain of DeepResearch-style search agents

    提出权威链劫持与轨迹引导策略演化，劫持搜索 Agent 的证据链

    arXiv
    2608.04565
    Published
    Layer
    Application layer
    Setting
    web agent
    Evaluated models
    Qwen3.5-9B, Gemma4-31B, DeepResearch and others7

    Abstract论文揭示了多步搜索智能体在受限中间人操纵下的脆弱性，提出 ACH 策略与 TGSE 演化方法并完成系统验证。

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  6. AttacksarXiv 2605.08310

    WebTrap：针对浏览器 Agent 导航过程的中途劫持注入攻击

    提出 WebTrap 三阶段陷阱，中途劫持浏览器 Agent 完成攻击后再返回用户任务

    arXiv
    2605.08310
    Published
    Layer
    Application layer
    Setting
    web agent
    Evaluated models
    DeepSeek-V3.1-Terminus, Gemini-2.5-Flash, GLM-4.5-Air and others5
    AbstractWebTrap 把攻击目标嵌进用户流程中途执行再返回。

    WebTrap 是针对长程网页与文件导航的中途劫持注入，目标是在完成攻击动作后仍回到用户任务。

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  7. AttacksarXiv 2604.02623

    论文提出 eTAMP 攻击：网页内容注入可跨会话污染 Web Agent 记忆

    提出 eTAMP，用网页内容跨会话投毒 Web Agent 轨迹记忆并诱导越权操作

    arXiv
    2604.02623
    Published
    Layer
    Application layer
    Setting
    web agent
    Evaluated models
    GPT-5-mini, GPT-5.2, GPT-OSS-120B and others6
    Abstract论文提出了基于环境注入的跨任务记忆投毒攻击 eTAMP，作者报告智能体在环境压力下易感性增加且能力与安全并不协同。

    该论文评估了基于大语言模型的 Web 智能体在利用历史交互轨迹进行记忆增强时的安全脆弱性。

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  8. DefensesarXiv 2603.14707

    Visual Confusion Agents: Exploiting and Defending Against Perception Failures in Computer-Use Agents

    Proposes out-of-process dual-channel comparison classification to intercept erroneous clicks by computer-use agents

    arXiv
    2603.14707
    Published
    Layer
    Application layer
    Setting
    Browser and computer use
    Evaluated models
    multi-modal-embed-small
    AbstractPerception errors can be exploited by swapping screen pixels.

    The authors frame CUA's perception failure as a security vulnerability, visual confused deputy: the agent authorizes click(x,y) based on a misread screen, and the actual landing point differs from the element it believes it is clicking.

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  9. Evaluation and benchmarksarXiv 2512.23128

    牛津等发布 TRAP 基准

    构建 TRAP 基准，评测网页 Agent 被界面提示注入劫持任务的情况

    arXiv
    2512.23128
    Published
    Layer
    Application layer
    Setting
    web agent
    Evaluated models
    GPT-5, Claude Sonnet 3.7, Gemini 2.5 Flash and others6
    Abstract论文提出了基于五维模块化攻击空间的 TRAP 基准，评估了六个模型在真实网页环境中的注入风险。

    研究定位：针对网页自主智能体易受外部界面提示注入攻击的问题，构建了基于真实网站克隆的模块化评测基准 TRAP。

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  11. Evaluation and benchmarksarXiv 2510.27140

    MobiRed 框架测评八款移动 LLM Agent 的第三方渠道提示注入攻击

    用 MobiRed 评测移动 LLM Agent 面对第三方渠道的提示注入

    arXiv
    2510.27140
    Published
    Layer
    Application layer
    Setting
    Browser and computer use
    Evaluated models
    Mobile-Agent-E, AppAgent, AutoGLM and others8

    Abstract论文评估了移动 LLM 智能体作为 confused deputy 的易受攻击性，作者称非应用控制通道可诱导智能体执行多步越权危害。

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  12. Evaluation and benchmarksarXiv 2510.01354

    WAInjectBench 系统评测网页 Agent 的提示注入检测方法

    提出 WAInjectBench 评测网页 Agent 的提示注入检测

    arXiv
    2510.01354
    Published
    Layer
    Application layer
    Setting
    web agent
    Evaluated models
    GPT-4o, Mistral-7B-Instruct-v0.1, Llama-Prompt-Guard-2-86M and others8
    AbstractWAInjectBench 比较网页智能体的提示注入检测：显式线索可被部分检出，隐蔽攻击大多漏检。

    WAInjectBench 是网页智能体提示注入检测的基准。作者要看现有检测器能否发现这类攻击：攻击者改网页，使智能体执行目标动作，而不是用户任务。作者称这是首个此类综合基准。

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