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Oct 12 · Monday

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  1. DefensesarXiv 2610.12432

    FAITH: A feasibility-aware safety filtering reinforcement learning framework for high-dimensional systems

    Proposes the FAITH safe filtering reinforcement learning framework, training high-dimensional systems to optimize task reward under constraints

    Published
    AbstractFAITH separates safety filtering from reward optimization.

    FAITH is a model-free safe filtering reinforcement learning framework for high-dimensional systems: safety correction and task reward reside in separate modules.

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  2. DefensesarXiv 2610.07935

    SIGMA:让模型依据 Model Spec 自我改进安全对齐

    提出 SIGMA,让模型依据 Model Spec 自造对齐数据并改进安全对齐

    Published
    Setting
    AI Agent
    Evaluated models
    Qwen3.6-27B
    AbstractSIGMA 用 Model Spec 自造监督,单轮训练可迁到多项智能体安全指标。

    SIGMA 让 Qwen3.6-27B 只凭 Model Spec 和高层领域,自己生产对齐任务、评分细则和奖励。

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  3. AttacksarXiv 2609.14060

    AgentQ:针对 LLM Agent 的量化条件后门攻击

    提出量化条件后门 AGENTQ,使智能体仅在量化后触发恶意工具调用

    Published
    Setting
    AI Agent
    Evaluated models
    Qwen3.5-2B, Qwen3.5-4B, Qwen3.5-9B and others6

    Abstract论文研究了智能体量化条件后门风险,提出 AGENTQ 在保持效用的同时实现全精度隐蔽与量化后生效。

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  4. AttacksarXiv 2609.07051

    TrojanWorld:通过想象引导对世界模型智能体植入后门

    提出 TrojanWorld,用物理触发器对世界模型智能体植入想象引导后门

    Published
    Evaluated models
    TD-MPC2, DreamerV3, R2-Dreamer

    AbstractTrojanWorld 揭示了世界模型智能体的供应链后门风险,通过在世界模型中操纵想象可实现对闭环动作的持续控制。

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  5. AttacksarXiv 2604.03890

    From prompts to physical actions: structured backdoor attacks against LLM-mediated robot control systems

    Propose a structured JSON backdoor that triggers malicious actions in robot control via a poisoned LoRA

    Published
    Evaluated models
    Llama-3.1-8B-Instruct, Gemma-2-9B-IT, DeepSeek-R1-Distill-Llama-8B and others4

    Abstract推理层后门传不到执行。

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