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Oct 12 · Monday

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Found 3 papers
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Impact

Harmful content11Data and privacy leaks9Output manipulation16Availability and reliability3

System component

MCP and skills5Memory3Guardrails and judges5Reasoning chains4Vision5Audio3Embeddings0Multimodal10
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Impact：Availability and reliabilitySystem component：AudioSystem component：EmbeddingsClear all

Another 254papers do not have category tags yet and are not included in filtered results.

  1. AttacksarXiv 2610.09819

    FAB 攻击：给 HuBERT/WavLM 植入后门，背景警笛声即可让下游语音任务失灵

    提出 FAB 攻击，向声学基础模型植入可由环境声触发的后门

    arXiv
    2610.09819
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    HuBERT-base, WavLM-base

    Abstract论文提出了针对声学基模的 FAB 后门攻击，在弱威胁模型下实现了任务无关、物理可触发的后门植入。

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  2. Evaluation and benchmarksarXiv 2609.38106

    剪枝提效却牺牲公平：剪枝语音 LLM 中的群体差异

    测量剪枝对语音 LLM 群体转写公平性的影响

    arXiv
    2609.38106
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Whisper Small (12 layers), Whisper Medium (24 layers), Whisper Large-v2 (32 layers)

    Abstract剪枝后的 Speech-LLM 不能只看总体 WER：Fair-Speech 上族群差扩大，作者建议看最差组。

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  3. AttacksarXiv 2602.07379

    Aegis: Governance, Integrity, and Security for AI Voice Agents

    提出 Aegis 红队框架，多轮攻击高风险语音 Agent

    arXiv
    2602.07379
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    GPT-4o, GPT-4o-mini, Gemini-1.5 Pro and others7
    AbstractAegis red-teams three types of voice agents: query-based access eliminates authentication and leakage successes, but behavioral attacks remain.

    Aegis uses multi-turn red-teaming evaluation on ALLM voice agents already integrated into business systems. The authors call this the first systematic red-teaming framework in this setting.

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