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Oct 12 · Monday
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About this collection Collection in progress We are building a large AI safety paper collection to help researchers find work related to their interests. Papers are collected automatically andfiltered only for relevance to AI safety , without judging quality, novelty or reading value. The default “Annotated first” shows papers with tags or published paper analyses first. Each group is ordered by original publication date, newest first, with undated papers last. “Newest first” uses publication dates only. This order is not a quality ranking.
One-line summaries, tags and paper analyses are prepared by AI from the papers. Language models may have systematic biases and produce hallucinations.We plan to carry out human annotation and review to reduce errors in this content.
提出 LiBRA，在潜空间双向优化以规避图像水印双侧检测
Published Oct 2
Evaluated models Stable Signature , YU1 , HiDDeN and others4 Abstract LiBRA 用双向潜空间优化规避双侧水印检测，并提高相对反转基线的保真。
提出 UNTAG，绕过开源权重模型的 trigger-tag 滥用检测
Published Oct 2
Evaluated models Llama2 (7B) , Llama3 (8B) , Qwen2.5 (7B) Abstract 作者用 UNTAG 评估 trigger-tag，认为其不能充当开放权重滥用检测的主要边界。UNTAG 是针对开放权重 LLM 上条件滥用检测 trigger-tag 的统一攻击评估：信号可以在解码期注入，也可以写进权重，但攻击者若能改输出或改已发布权重，检测可以失效。
复现 Tree-Ring 语义水印的黑盒伪造并恢复检测统计量
Published Sep 11
Evaluated models Stable Diffusion XL base 1.0 提出黑盒攻击 DRIFT，偏转生成轨迹以去除扩散水印
Published Sep 8 用通用扰动对恶意软件分类器及漂移检测器做规避与后门投毒
Published Aug 4
Evaluated models MLP 100-100 , CADE (CAE 512-384-256-128) , CADE (CAE 512-128-32-8) and others6 提出 GhostPrint，用低秩微调让弱模型骗过指纹审计
Published Jun 15
Evaluated models Gemma-1.1-2B , Qwen2-1.5B , Phi-3-mini Abstract GhostPrint 以 PEFT 让弱模型骗过部分指纹。GhostPrint 讨论恶意推理提供方能否只用低秩更新，让更弱的模型在用户的 black-box 指纹审计里看起来像宣传中的强模型。
提出 Adaptive Stealing，自适应窃取印章以擦除或伪造水印
Published Apr 13
Evaluated models OPT-2.7b , Llama3-8b Abstract AS 用逐步选印章做水印伪造与擦除，作者称其攻击效果高于固定策略的 WS。AS 是一种窃取生成式 LLM 水印的算法，用来在密钥和检测接口不可用时伪造或擦除水印。
提出 TFA 与 SVA，抑制 ensemble 后门指纹验证
Published Jan 7
Evaluated models LLaMA2-7B , LLaMA3.1-8B-It , Qwen2.5-7B and others11 Abstract 两种 ensemble 攻击 TFA/SVA 抑制后门指纹。TFA 与 SVA 是针对后门式 LLM 指纹的 ensemble 抑制攻击，不改模型参数。所有者用 SFT 写入秘密对，只通过 API 验证版权。攻击者未授权取得模型，不知道触发和指纹，却要把每个模型的验证变成失败，同时让 ensemble 至少不差于性能最高的单模型。
Attacks arXiv 2510.10987 · Oct 13, 2025 提出 DITTO 框架，蒸馏提取水印信号并注入外部模型以伪造归属
Published Oct 13, 2025
Evaluated models Llama3.1-8B , Llama3.2-3B , GPT-OSS 20B and others4 Abstract 论文针对大模型水印归属假设的脆弱性，提出黑盒仿冒攻击 DITTO，实现跨架构和采样方案的高检测率与低困惑度仿冒。
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