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Oct 12 · Monday

All papers

Found 5 papers
Filter5

Attack type

Poisoning and backdoors3

Defense type

Model hardening12Detection and filtering5Monitoring and auditing6

System type

Models and APIs5AI Agent13coding agent0

Attack surface / entry point

User input21Tool responses2Tool and skill descriptions5Images, audio and video9Training data7Weights and gradients3Distribution channels5

Layer

Training and data layer4
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 agentAttack surface / entry point：Tool responsesAttack surface / entry point：Weights and gradientsClear 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
  3. AttacksarXiv 2609.27124

    Fed-ADR：用恶意编排服务器协同客户端梯度操纵攻击联邦学习

    提出 Fed-ADR，用编排服务器协同操纵梯度攻破联邦学习并检测恢复

    arXiv
    2609.27124
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Attacker
    Gray-box
    Evaluated models
    CNN（MNIST：2 Conv + 3 FC）, CNN（Fashion-MNIST：6 Conv + 1 FC）, ResNet18（CIFAR-10）
    AbstractFed-ADR 用 gray-box 编排攻击绕过多种聚合防御，再用 Hessian 一致性检测和窗口内恢复把准确率拉回。

    Fed-ADR 是一套面向联邦学习的攻击、检测与恢复框架：编排服务器改写合谋客户端的梯度，使参数服务器的现有防御把投毒更新当成良性，同时在发现后用历史梯度把全局模型原地修回来。

    Paper analysisRead analysis
  5. DefensesarXiv 2608.02271

    Z-PEFT: Zero-shot backdoor detection for parameter-efficient fine-tuning based on canonical spectral features

    Propose Z-PEFT, which detects PEFT backdoors in a zero-shot manner using per-head spectral features.

    arXiv
    2608.02271
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    Z-PEFT, Llama-2-7B, Llama-2-13B and others6
    Paper analysisRead analysis
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