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Oct 13 · Tuesday

All papers

Found 14 papers

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  1. DefensesarXiv 2610.07657

    DEFER1 多智能体防御研究报告受控测试中的攻击成功率下降

    实现 DEFER,以确定性检查优先拦截多智能体流水线的内容攻击

    Published
    Evaluated models
    Qwen3-235B-A22B-Instruct-2507, gpt-oss-120b, Llama-4-Scout-17B-16E-Instruct and others5
    AbstractDEFER 把确定性检查放在 judge 之前。

    DEFER 用来测量多智能体授权流水线里,确定性规则在何处结束、LLM judge 从何处开始。

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  2. Study retests 15 prompt injection detectors: benchmark scores poorly predict agent deployment performance

    重测提示注入检测器在 Agent 工具输出上的检出与任务阻断

    Published
    Setting
    AI Agent
    Evaluated models
    Horizon-Labs, Wolf Defender, Prismor-1.5B and others15

    Abstract论文揭示公开基准无法反映智能体检测器表现,检测能力源于输入格式相似而非通用防御,建议基于工具输出与低误报评测。

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  3. Agentic Commerce Bench:衡量会花钱的智能体的欺诈检测能力

    构建支付欺诈基准 ACB,评测花钱智能体的欺诈检测

    Published
    Setting
    AI Agent
    Evaluated models
    gordonguard, pattern judge
    AbstractACB 把支付欺诈与智能体操纵分开测量,作者强调观察权限、误报预算和微支付复核成本。

    ACB 是一套智能体支付欺诈基准与检测参考栈,目标是衡量钱是否被不当提取,而不仅是智能体是否接受了恶意指令。

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  4. DefensesarXiv 2609.33039

    TACIT: detecting agent trajectory harm from LLM internal states

    提出 TACIT,从冻结内部状态检测智能体有害轨迹

    Published
    Setting
    AI Agent
    Evaluated models
    Qwen3-4B, Llama-3.1-8B, Qwen3-4B-Instruct and others4

    Abstract提出基于内部表征的智能体轨迹安全检测方法 TACIT,揭示工具风险表征特性并在多项基准上提升检测表现与效率。

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  5. AttacksarXiv 2607.06595

    When Agents Remember Too Much: Memory Poisoning Attacks against Large Language Model Agents

    Proposed GhostWriter memory poisoning, which writes into the agent's long-term memory via email and calendar and activates across sessions

    Published
    Setting
    AI Agent
    Evaluated models
    GPT-5.4-mini, DeepSeek-V4-Flash, Gemini-2.5-Flash and others4
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  6. 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.

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  7. DefensesarXiv 2601.10156

    ToolSafe 提出步骤级护栏 TS-Guard 与 TS-Flow,降低 Agent 有害工具调用

    提出 TS-Guard 与 TS-Flow,在执行前检测并引导 Agent 修正有害工具调用

    Published
    Setting
    AI Agent
    Evaluated models
    GPT-4o, TS-Guard, Qwen2.5-14B-Instruct
    AbstractTS-Bench 标步骤级风险,TS-Guard 用多任务 RL 做执行前判定,TS-Flow 用反馈代替直接中止。

    ToolSafe 研究 LLM 智能体在工具执行前的步骤级安全:基准 TS-Bench、护栏 TS-Guard,以及反馈驱动框架 TS-Flow。

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