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Oct 12 · Monday

All papers

Found 3 papers
Filter5

Attack type

Prompt injection3

Defense type

Detection and filtering3Monitoring and auditing3

System type

Models and APIs2AI Agent13coding agent0web agent1

Attack surface / entry point

User input22Tool responses3Tool and skill descriptions5Images, audio and video10Training data7Weights and gradients3Distribution channels5

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 233papers have no tags yet and are not included in filtered results.
Defense type：Detection and filteringSystem type：Models and APIsSystem type：coding agentSystem type：web agentAttack surface / entry point：Tool responsesClear all

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

  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
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