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Oct 12 · Monday

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Found 3 papers
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Impact

Harmful content15Unauthorized actions78Data and privacy leaks28Output manipulation42Availability and reliability3Oversight failure43

System component

MCP and skills6Memory3RAG1Guardrails and judges5Monitors1Reasoning chains4Permissions and sandboxing1Vision5Audio3Multimodal10
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Impact：Availability and reliabilitySystem component：RAGSystem component：MonitorsSystem component：Permissions and sandboxingClear all

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

  1. Evaluation and benchmarksarXiv 2610.04691

    RAGStress：评测知识库退化下 RAG 鲁棒性的受控基准

    提出 RAGStress 受控基准，评测知识库退化下的 RAG

    arXiv
    2610.04691
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Evaluated models
    Claude Sonnet 4, Claude Opus 4.6, DeepSeek R1 and others5
    Abstract作者用 RAGStress 比较五系统。

    RAGStress 是知识库损坏下的 RAG 受控压力测试。作者明确不把它当作模型排行榜。

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  2. AttacksarXiv 2605.30040

    研究揭示 LLM 按 token 计费可被服务商系统性虚报

    构造可通过计费审计的 token 虚报，使服务商系统性超收

    arXiv
    2605.30040
    Published
    Layer
    Infrastructure layer
    Setting
    Models and APIs
    Evaluated models
    PALACE domain-adapted auditor
    Abstract不诚实提供方可在三种 token 审计上虚报并通过检查，作者认为需改用提供方不控制的证据。

    不透明 LLM 的按 token 计费里，现有审计核验的是提供方可控的证据，不诚实提供方可以虚报并通过检查。。

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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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