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Oct 12 · Monday

All papers

Found 5 papers
Filter3

Attack type

Prompt injection4

System type

Models and APIs5LLM applications3AI Agent129coding agent10web agent4Browser and computer use3Multi-agent systems10

Attack surface / entry point

User input278Documents and messages8Tool responses5Retrieval corpus3Memory writes0Images, audio and video97Training data63Weights and gradients61Distribution channels12Inference services and hardware17

Layer

Prompt layer3
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System type：Models and APIsAttack surface / entry point：Tool responsesAttack surface / entry point：Memory writesClear all

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  1. DefensesarXiv 2610.03073

    SecJev：为 System One 决策模型引入安全专业知识

    提出 SecJev，用场景加权训练把决策模型适配为安全类型化判断

    arXiv
    2610.03073
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    SecJev-0.8B, SecJev-2B, SecJev-4B and others4
    AbstractSecJev 用共享打分器做安全决策，0.8B 的 task-macro 超过 Kev-9B。

    SecJev 是 0.8B 到 9B 的安全决策模型族，作者称据其所知是首个面向安全的 Jev-like 家族。

    Paper analysisRead analysis
  2. Evaluation and benchmarksarXiv 2609.29429

    Jev 被用于零样本检测十类对齐失败，中位 AUROC 达 0.886

    提出 RLCDAlignBench，零样本检测多种对齐失败

    arXiv
    2609.29429
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Evaluated models
    Jev (jev-1.13.0)
    Abstract一次通用问题已能给多数对齐失败排序，大的误差来自状态和标签。

    RLCDAlignBench 测量用强化学习做校准决策的 Jev，能否当作对齐失败的零样本检测器。

    Paper analysisRead analysis
  3. AttacksarXiv 2609.28613

    研究：提示注入可改变 Jev 决策模型的行动概率

    测量间接提示注入对类型化概率决策的动作劫持

    arXiv
    2609.28613
    Published
    Layer
    Application layer
    Setting
    Models and APIs
    Evaluated models
    jev-1.13.0
    Abstract封闭动作集改变提示注入的表现，但没有去掉概率被不可信内容移动的风险。

    作者在 jev-1.13.0 上研究 decision hijacking：不可信内容能否在用户任务和可选动作都不变时，把类型化决策推向攻击者目标。

    Paper analysisRead analysis
  5. AttacksarXiv 2606.06244

    研究者提出 Ghostwriter 攻击：用伪造证据注入操纵 LLM 观点

    提出 Ghostwriter 攻击，用伪权威证据条件注入操纵模型观点

    arXiv
    2606.06244
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Evaluated models
    GPT-4o, Claude-3.7-Sonnet, DeepSeek-V3 and others10
    Abstract作者称攻击未被消除。

    Ghostwriter 是一种针对上下文窗口的观点引导攻击：把误导陈述改写成伪权威文本，再在相关查询中让目标模型把该观点写进回答。

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