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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.
构建 EpiReal-Bench，评测图像生成器将虚假主张渲染为可信证据
Published Oct 8
Evaluated models GPT-Image-2 , Nano Banana 2 , Grok Imagen 2.0 and others4 Abstract 四款商用图像模型能把假主张画成可信证据。EpiReal-Bench 用来量商用图像生成器会不会把虚假现实主张画成可信视觉证据。
评估应用决策层的输入注入与隐私推断风险
Published Oct 4
Evaluated models Jev , NanoJev Abstract Jev 的类型化决策可被输入与训练操纵，也会泄露信息，并同时支持检测与滥用。Jev 是 TypeSafe 的 System One 模型，把应用 state 和固定问题变成 Choice、Noul 或 Score 的类型化决策与概率。作者用官方 API 和可改训练的 NanoJev，考察它作为决策层时的操纵、泄露与双重用途。
提出 ASA 与 AttestMCP，度量并降低 LLM 对输入序列扰动的敏感
Published Oct 2
Evaluated models Gemma-3-27B-Instruct , Gemma-3-4B-Instruct , Llama-3.2-3B-Instruct and others12 提出内容不变样式包装，翻转安全评审器的判定
Published Sep 8
Evaluated models GPT-4o-mini (gpt4o) , GPT-4o-mini (strongreject) , DeepSeek-Chat and others7 Abstract 论文揭示特定安全评审器易受保持内容不变的外层包装诱导改变判定，并指出安全评估应将判定稳定性作为重要考量指标。这是一项关于大语言模型自动化安全评审系统表面样式脆弱性的评估研究。
提出 HPAA 攻击，用排版视觉线索绕过内容审核系统
Published Jun 9
Evaluated models Llama-Guard-3-8B , Perspective API , ShieldGemma-2B and others13 Abstract 揭示了排版视觉线索与分词差异带来的审核盲区，HPAA 以极低查询实现近乎完全规避。这篇论文研究了人类视觉感知与大模型分词处理机制之间的差异所导致的内容审核漏洞 。
提出 BITE，用上下文赌博机选择风格修改抬高 LLM 评委打分
Published May 24
Evaluated models o3-mini , Gemini-2.5-Flash , Llama-3.3-70B-Instruct and others8 Abstract BITE 用黑盒赌博机利用风格偏差抬分，作者称这对 LLM-as-a-judge 构成威胁。BITE 是黑盒风格操纵：在保持答案语义的同时，抬高 LLM 评审给出的分数。
提出 BadGraph，在黑盒下联合扰动文本属性图的拓扑与节点文本
Published Mar 22
Evaluated models DeepSeek-V3 , Mistral-7B , Qwen-Plus and others10 Abstract BadGraph 用 LLM 在黑盒下联合扰动 TAG 拓扑与文本，并在多种 reasoner 上降低准确率。BadGraph 是面向文本属性图节点分类的黑盒通用攻击，用来检查同一套拓扑加文本扰动能否把 GNN reasoner 和 LLM reasoner 带向预定错误标签。
提出 DECEIVE-AFC，以声明改写诱导搜索增强事实核查误判
Published Jan 31
Evaluated models HiSS , LEMMA , DEFAME and others4 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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