Skip to content

Translation in progress Some entries still include text in the original language.

Oct 12 · Monday

All papers

Found 11 papers
Filter4
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 233papers have no tags yet and are not included in filtered results.

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

  1. 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

    Published
    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。

    Paper analysisRead analysis
  2. DIBench:面向移动 GUI 智能体多候选选择决策完整性的安全基准

    用 DIBench 评测移动 GUI 智能体被 UI 注入后的决策完整性

    Published
    Evaluated models
    Qwen2.5-VL(32B), Qwen3-VL(32B), GUI-Owl(32B) and others7

    Abstract论文提出移动智能体决策完整性基准 DIBench,发现欺诈 UI 注入诱导目标选择并推高完成率,通用防御收益不稳定。

    Paper analysisRead analysis
  3. 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

    Published
    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。

    Paper analysisRead analysis
  4. DefensesarXiv 2606.08234

    SciTrace:面向科学发现智能体的轨迹感知安全推理框架

    提出 SciTrace,为科学发现智能体加入跨阶段安全推理与工具轨迹核验

    Published
    Evaluated models
    Llama-3.1-70B-Instruct, Qwen2.5-72B-Instruct, DeepSeek-V3 and others4
    AbstractSciTrace 把安全推理织入科学智能体各阶段,在 SciSafetyBench 上提高安全指标并保持输出质量。

    SciTrace 是加在科学发现智能体流水线上的安全框架,不是再在各阶段出口放一个独立过滤器。

    Paper analysisRead analysis
  5. 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

    Published
    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.

    Paper analysisRead analysis
  6. DefensesarXiv 2602.10915

    Blind Gods and Broken Screens: Building a Secure, Intent-Centric Mobile Agent Operating System

    提出 Aura,用可信内核中介移动 Agent 的身份、感知与动作控制

    Published
    Evaluated models
    Gemini-3-Flash
    AbstractAura mediates structured interactions via the Agent Kernel.

    Aura uses kernel-mediated structured agent interaction, replacing screen-interface mobile assistants..

    Paper analysisRead analysis
  7. 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

    Published
    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。

    Paper analysisRead analysis
  8. DefensesarXiv 2505.23559

    SafeScientist:让 LLM 智能体的科学发现具备风险意识

    提出 SafeScientist 四层监控,阻止科研智能体执行高风险或不伦理任务

    Published
    Evaluated models
    GPT-4o, GPT-4.1, GPT-3.5-turbo and others5
    Abstract四层监控加科研安全基准。

    SafeScientist 是带四层监控的科研智能体框架,并用 SciSafetyBench 评测科学场景中的任务安全。

    Paper analysisRead analysis
You have reached the end