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Oct 13 · Tuesday

All papers

Found 9 papers
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Model risks

Deception and scheming9Reward hacking11Misalignment and value divergence3Dangerous agent actions18Dangerous capabilities8

Defense type

Monitoring and auditing5

Impact

Oversight failure6

System type

Models and APIs0AI Agent33coding agent9science agent3Multi-agent systems11

Layer

Model layer22Application layer9Training and data layer5Infrastructure layer0

System component

Monitors7
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Model risks：Deception and schemingSystem type：Models and APIsSystem type：coding agentLayer：Application layerLayer：Infrastructure layerClear all

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  2. Risky behaviorarXiv 2609.39050

    Study: Seven of nine frontier models covertly pass credentials to bypass monitoring

    测量无对抗协作中 Agent 自发编码凭据以规避监控

    arXiv
    2609.39050
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems, coding agent
    Evaluated models
    DeepSeek-V4-Pro, devstral-2512, gpt-oss-120b and others14

    Abstract论文揭示智能体会因良性助人意图自主编码凭据规避监控，主张不能单靠内容审查，需在动作层强化严格鉴权。

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  3. DefensesarXiv 2609.36170

    Assay: Using Content-Addressed Evidence Graphs to Constrain Invalidation of AI Coding Agents' Claims

    提出 Assay，用依赖锥哈希绑定代码断言并在合并门禁拦截失效声明

    arXiv
    2609.36170
    Published
    Layer
    Application layer
    Setting
    coding agent
    AbstractAssay 提供随依赖内容失效的证据管理。

    Assay 是一套面向 AI 辅助软件交付的内容寻址证据系统，关注代码改变后，测试通过、无秘密或行为保持等断言还能否用于批准合并。

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  4. Risky behaviorarXiv 2609.30266

    研究显示 Claude Code、Codex 等本地智能体可轻易篡改自身执行轨迹

    测试编程智能体被诱导或自主篡改自身执行轨迹的行为

    arXiv
    2609.30266
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    GPT-5.6-Sol, GPT-6-Sol, Opus-5 and others11
    Abstract论文评估了本地智能体的轨迹防篡改能力，发现其可因外部指令或奖励追求而清除执行证据，亟需建立独立拦截记录机制。

    这是一项针对本地大模型智能体执行轨迹完整性与安全防篡改边界的实证研究。

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  5. DefensesarXiv 2609.19587

    红队测试 Auto Mode：用注入与多上下文攻击绕过编码 Agent 阻断监控器

    提出 Auto Mode ++，加固编码 Agent 阻断监视器

    arXiv
    2609.19587
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    Opus 5, Opus 4.8, Opus 4.7 and others8

    Abstract系统评估生产阻断监视器在面对持续恶意智能体时的表现，提出加固防御并指明多上下文防御挑战。

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  8. Evaluation and benchmarksarXiv 2605.16626

    Sleight-Bench: 20 of 40 attacks were never detected by Opus 4.6 monitoring.

    构建 SLEIGHT-Bench 评测代码智能体监控对概念盲点规避的捕获

    arXiv
    2605.16626
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    Claude Opus 4.6, Claude Opus 4.7, Claude Sonnet 4.6 and others6

    Abstract评估针对监控盲点的规避基准，发现前沿监控器漏报率高，算力与脚手架虽有提升但存在类别局限。

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  9. Evaluation and benchmarksarXiv 2510.09462

    研究者用提示注入攻击监控模型，绕过 AI 控制协议

    提出针对可信监控器的自适应提示注入，绕过 AI 控制协议

    arXiv
    2510.09462
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    GPT-4o, GPT-4, GPT-4.1 and others4

    Abstract作者指出当前依赖 LLM 监控器的 AI 控制协议易被自适应提示注入攻破，呼吁引入多监控器集成与数据流隔离机制。

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