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Oct 13 · Tuesday

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  1. AttacksarXiv 2610.13928

    AI models can provably hide arbitrary capabilities

    Proposes digital lock compilation, encrypting executable networks into host weights to hide arbitrary capabilities

    Published
    Evaluated models
    Gemma 3 270M
    AbstractThe authors use a digital lock to hide an executable capability and state that elicitation on the chemistry and parity tasks did not recover the capability.

    The authors construct a class of backdoors that encrypt an executable network into the weights, used to discuss the limits of white-box elicitation audits under supply chain attacks.

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  2. DefensesarXiv 2610.08316

    MARCO:面向蛋白质生成模型的放射性水印框架

    提出 MARCO 放射性水印,冻结蛋白质生成模型并在去噪时嵌入签名

    Published
    Attacker
    Black-box
    Evaluated models
    RFdiffusion, ESMFold, Chroma and others6
    AbstractMARCO 在冻结的 PGM 上去噪时嵌构象水印,作者称能兼顾保真与抗扰动,并使盗版模型继承水印。

    MARCO 是加在蛋白质生成模型推理过程上的构象水印,用来标记知识产权,并为可能的生物安全滥用保留可追溯信号。作者还把它说成数字权利管理工具,以及符合生物武器公约的数字监管链。问题在于:专有 PGM 的三维输出可能被拿去训练盗版模型,也可能在来源被抹掉后绕过筛查。

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  3. AttacksarXiv 2610.06049

    研究者提出仅修改 SAE 解码器的后门攻击,可在不改动语言模型的情况下植入行为

    提出只改 SAE 解码器的后门,在冻结语言模型上植入代码插入

    Published
    Evaluated models
    Gemma 3 4B Instruct, Qwen3 1.7B, Llama 3.1 8B Instruct
    Abstract只改解码器的 SAE 可带上代码插入后门,且若干常规质量指标变化不大。

    只改 SAE 解码器、冻结编码器与语言模型,即可把代码插入行为放进推理时的辅助组件。作者把这视为 SAE 供应链问题:checkpoint 一旦替换某一层激活,就参与后续计算,而不只是解释工具。

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  4. DefensesarXiv 2609.39548

    NDDL:用正常扩散动力学为文生图模型做后门防御

    提出 NDDL,用良性扩散轨迹检测文生图后门提示并定位触发 token

    Published
    Evaluated models
    Stable Diffusion v1.5, Stable Diffusion XL, Stable Diffusion v3.5 and others4
    AbstractNDDL 从良性扩散轨迹学习正常转移,用动态不一致检测后门并定位触发 token。

    NDDL 是面向文生图扩散模型的后门防御:防御者只有白盒模型访问和良性提示词,没有后门先验,需要区分后门提示词并找出触发 token。

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  5. DefensesarXiv 2610.00348

    NEEDLE:通过权重正交化移除 LLM 后门

    提出 NEEDLE,用权重正交化在已知触发器时去除大模型后门

    Published
    Evaluated models
    Gemma-3-4B-IT, Qwen3-4B-Instruct-2507, Gemma-3-12B-IT

    AbstractNEEDLE 用闭式权重编辑去掉已知触发器的后门,并保住拒绝子空间。

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  6. DefensesarXiv 2609.27399

    GUARD:在 CosyVoice2 上做说话人身份遗忘以阻止重识别

    提出 GUARD,在零样本 TTS 上用激活转向遗忘说话人身份以阻止再识别

    Published
    Evaluated models
    CosyVoice2-0.5B, F5-TTS, FireRedTTS
    AbstractGUARD 用门控激活转向把退出说话人推向冒充相似度,以抑制再识别并保住音质。

    GUARD 用门控激活转向做说话人身份遗忘,使零样本 TTS 对退出说话人难以再识别。

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  7. LLM Forensics:用稀疏自编码器定位并控制后门触发机制

    用 SAE 定位后门触发机制并分离检测与因果控制

    Published
    Evaluated models
    Gaperon-1B, Gaperon-8B, Gaperon-24B
    AbstractGaperon 语言切换后门可分成检测、残差路由和后期语言跟踪三类 SAE 特征,只有部分能改行为。

    作者用稀疏自编码器分解 Gaperon 中无害的语言切换后门,区分识别触发的特征与真正能抑制或诱发该行为的特征。固定触发使英语提示在 Gaperon-1B 与 8B 上续写成法语或德语。

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  8. DefensesarXiv 2609.03629

    EraseSAE:用稀疏自编码器在文生视频扩散模型中实现精准概念擦除

    提出 EraseSAE,在文生视频模型中局部擦除指定概念

    Published
    Evaluated models
    CogVideoX-5B, HunyuanVideo, Flux.1 [dev]
    AbstractEraseSAE 用单义特征局部擦除。

    EraseSAE 把 SAE 用于 DiT 文生视频模型的概念擦除。作者要解决的是:全局改权重或表面负提示对不齐细粒度、分布式的概念,而且会改到无关时空区域。

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  9. DefensesarXiv 2609.00786

    MROP:针对 Deep JSCC 后门攻击的掩码区域优化净化方法

    提出 MROP,在发射端推理时纯化输入以抵御训练投毒后门

    Published
    Evaluated models
    DeepJSCC, SwinJSCC
    AbstractMROP 不重训练,在发射端纯化补丁后门。

    MROP 是 deep JSCC 无线图像传输上的推理时输入纯化,对象是矩形补丁后门。

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  10. DefensesarXiv 2608.00716

    生成图像更易被遗忘:面向合成图像检测的机器遗忘视角

    提出机器遗忘式生成图像检测,用遗忘前后特征相似度区分自然图与生成图

    Published
    Evaluated models
    DINOv2 ViT-S/14, DINOv2 ViT-B/14, DINOv2 ViT-L/14 and others9
    Abstract作者用剪枝或 LoRA 遗忘拉开生成图与自然图的特征相似度,并在多个检测基准上报告结果。

    这篇工作把 AI 生成图像检测写成对预训练视觉模型的机器遗忘:原模型对两类图像损失都低,剪枝后生成图特征变得更不像原模型。

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  11. DefensesarXiv 2603.03206

    Understanding and mitigating dataset contamination in large language model steering

    Mitigating steering dataset contamination with Lee-Valiant robust means

    Published
    Evaluated models
    Llama-3.2-3B-Instruct, Mistral-7b-Instruct-v0.3, OLMo-2-1124-7B-Instruct
    AbstractData poisoning can alter steering; coordinated injection is strongest.

    The authors study how much behavioral control remains at inference time when contrastive steering's training data is contaminated, and whether the robust mean can catch it.

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  12. DefensesarXiv 2602.03085

    Triggers in the Haystack: Extracting and Reconstructing LLM Backdoor Triggers

    提出推理扫描器,在仅有模型文件时检测后门并重构触发词

    Published
    Evaluated models
    gemma-3-270m-it, DeepSeek-R1-Distill-Qwen-1.5B, Phi-4-mini-instruct and others8
    Abstract用记忆泄漏和内部信号重构触发词。

    这是一个只做推理的 sleeper agent 扫描器:在不知触发词和目标行为时,检测因果语言模型里的条件后门并重构触发词。。问题在于,后门只在触发词出现时偏离基线,而开放仓库和网页投毒使检测方通常没有触发词、目标或激活语料。

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