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Oct 12 · Monday

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Found 3 papers
Filter2

Impact

Harmful content3Unauthorized actions15Data and privacy leaks6Output manipulation3Availability and reliability6Oversight failure9Increased misuse capability5

System type

Models and APIs54LLM applications16AI Agent36coding agent3science agent4web agent3Multi-agent systems8
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Impact：Output manipulationSystem type：coding agentClear all

Another 261papers do not have category tags yet and are not included in filtered results.

  2. Evaluation and benchmarksarXiv 2605.17062

    研究复现五个前沿模型的包名幻觉：跨模型共同幻觉 127 个包名，53 个仍可被注册

    复评前沿模型代码生成中的包名幻觉与抢注攻击面

    arXiv
    2605.17062
    Published
    Layer
    Model layer
    Setting
    coding agent
    Evaluated models
    Claude Sonnet 4.6, Claude Haiku 4.5, GPT-5.4-mini and others5

    Abstract评估显示前沿模型包幻觉率收窄但威胁仍在，作者识别出跨模型通用攻击面并呼吁联合防御。

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  3. Evaluation and benchmarksarXiv 2601.22952

    Filtering the Noise: A Comparative Study of LLM Agents in Vulnerability False Positive Filtering

    评测代码 Agent 过滤静态告警误报的能力

    arXiv
    2601.22952
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    Claude Sonnet 4, DeepSeek Chat, GPT-5
    Abstract智能体可去掉大量 SAST 噪声，但收益随骨干和 CWE 变化，并会把部分真漏洞标成误报。

    这是对三种 LLM 智能体框架过滤 SAST 误报的比较研究。

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