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Oct 12 · Monday

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  1. Space beats statistics, retraining beats ranking: auditing training labels under label-only backdoors

    Auditing training labels for label-only backdoors, comparing spaces and statistics, and judging by retraining

    Published
    Evaluated models
    ResNet-32
    AbstractLabel inspection must be done in a space that has not been fitted to these labels, and judged by retrained models rather than detection scores.

    The authors ask: can a defender without trusted data decide which training labels to keep, and how much clean data this destroys, and what the final model accuracy is.

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  2. AgentSpy: Observing AI agent behavior at the system call layer and detecting skill injection attacks

    提出 AgentSpy,在系统调用层观测 Agent 行为并检测技能注入

    Published
    Evaluated models
    gpt-5.6-terra, deepseek-v4-flash, glm-5.3-flash and others4
    Abstract提出了系统级智能体监控框架 AgentSpy,揭示出通过测试背后的非预期行为与注入风险。

    本研究提出了面向 AI 智能体的系统级外部监控与行为分析框架 AgentSpy。

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  3. 研究:工具型智能体判断结果无用却仍继续检索,强制整合步骤可纠正

    揭示工具型智能体判定无用后仍不停止检索,并检验强制整合纠正效果

    Published
    Setting
    AI Agent
    Evaluated models
    Qwen2.5-7B, Llama-3.1-8B, Qwen3-8B and others7
    Abstract论文揭示智能体判定工具失效却无法转化为停止行动,证实通过框架规则强制整合判定可提升表现。

    本文针对智能体在工具静默失效时过度检索的问题,系统分析了判定、信念与停止行动之间的解耦现象,并提出了外部规则整合方案。

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  4. Paper reveals that LLM agent tool calls are rewritten during execution paths, and proposes IntAct as a fix

    提出 IEC 与 IntAct,测量并修复 Agent 工具调用的路径篡改

    Published
    Evaluated models
    qwen3-coder-plus, Qwen2.5-Coder-32B, Qwen2.5-72B and others4

    Abstract论文界定工具调用在执行路径中的意图偏离问题,提出无执行归因协议与跳级修复,为智能体工程提供评测与设计准则。

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  5. 研究发现护栏模型在基座模型不确定处过度自信

    诊断护栏相对基座在对抗提示上的置信失校

    Published
    Evaluated models
    Llama-Guard 7B, Llama-Guard-2 8B, Llama-Guard-3 8B and others10
    Abstract对抗包装使 guard 高置信漏检,而对应 base 在同一输入上常更不确定。

    诊断性分析:五个提示词分类 guard 的置信度,在清洁输入上可以较校准,在越狱包装后则变成高置信度的错误判定。

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  6. 研究揭示价值对齐 LLM 的意外危害并提出上下文对齐方法

    分析按 Schwartz 价值对齐后的意外危害,并提出上下文对齐方法

    Published
    Evaluated models
    Llama-2 7B, Llama-3.1 8B, Gemma-2 9B and others6
    Abstract价值对齐会随 Schwartz 价值改变有害倾向。

    作者研究按 Schwartz 价值对齐的 LLM 为何在安全评测里更易有害,并用提示做了一个不直说要安全的缓解试验。

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