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Oct 12 · Monday

All papers

Found 1 paper
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System type

Models and APIs0LLM applications1coding agent0

Attacker access

Black-box3White-box1

Attack surface / entry point

User input25Documents and messages1Images, audio and video12Training data7Weights and gradients25
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System type：Models and APIsSystem type：LLM applicationsSystem type：coding agentAttacker access：White-boxAttack surface / entry point：Documents and messagesClear all

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

  1. DefensesarXiv 2609.08394

    Windows 恶意软件检测器作为复合 AI 系统：准确率、效率与对抗鲁棒性的权衡

    提出 OBELISK 三级流水线，权衡恶意软件检测的准确率、开销与鲁棒性

    arXiv
    2609.08394
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Attacker
    White-box, Black-box
    Evaluated models
    GBDT-ALL, GBDT-YARA, NEBULA-ALL and others4
    Abstract三级过滤以不大的检测损失换速度，同一机制扩大攻击面，部署应按约束选配置。

    OBELISK 把 Windows PE 恶意软件检测做成 YARA、EMBER-GBDT 与 Nebula 的顺序流水线，用来同时看检出、计算开销和对抗鲁棒性。

    Paper analysisRead analysis
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