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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 FAB 攻击，向声学基础模型植入可由环境声触发的后门
Published Oct 7
Evaluated models HuBERT-base , WavLM-base Abstract 论文提出了针对声学基模的 FAB 后门攻击，在弱威胁模型下实现了任务无关、物理可触发的后门植入。
提出 GhostWord 词级触发后门，对自动语音识别做数据投毒
Published Sep 2
Evaluated models Whisper-Small , Whisper-Medium , MMS and others4 提出 PaperGuard，用图文注入与梯度扰动抬高自动审稿分数并检测恶意指令
Published Jun 11
Evaluated models Claude-sonnet-4.5 , GPT-4o , GPT-4.1 and others11 Abstract PaperGuard 测到提示注入与白盒视觉抬分，并比较分块检索防御。PaperGuard 同时评测并检测多模态 AI 审稿中的评分操纵。
提出 CodecAttack，在编解码器隐空间构造可穿透有损压缩的对抗音频
Published May 20
Evaluated models Qwen2-Audio-7B-Instruct , Audio Flamingo 3 , Qwen2.5-Omni 提出 AudioHijack，用不可感知对抗音频对语音模型做上下文无关提示注入
Published Apr 16
Evaluated models SpeechGPT , GLM-4-Voice , VITA-Audio and others15 Abstract 提出了 AudioHijack 框架，揭示了 LALM 对听觉提示注入的脆弱性与传统防御的局限。
提出 SWhisper，用近超声波向语音驱动 LLM 隐蔽注入越狱提示
Published Mar 14
Evaluated models Meta-Llama-3.1-8B-Instruct , Gemma-3-4B , Qwen2.5-7B-Instruct and others9 提出查询无关黑盒攻击 DQ-A，用可迁移文末后缀降低文档检索排名
Published Jan 31
Evaluated models Qwen-1.5-7B-Instruct , SFR-Embedding-Mistral , E5-Mistral-7B-Instruct and others10 Abstract DQ-A 以无查询黑盒后缀压低 LLM 检索排名，并在 BRIGHT 上比较三种基线。DQ-A 是一种 query-agnostic、complete black-box 的文档隐藏攻击，对象是用最后隐层相似度匹配的 LLM 检索。
提出 SOUNDBREAK，用仅音频共享对抗扰动使三模态模型答错
Published Jan 21
Evaluated models VideoLLAMA2 , Qwen 2.5 Omni , Qwen 3 Omni and others4 Attacks arXiv 2512.01335 · Dec 1, 2025 提出 EmoRAG，向查询与知识库注入同一表情以劫持 RAG 检索
Published Dec 1, 2025
Evaluated models SPECTER , Contriever , Qwen2-7B and others11 Abstract EmoRAG 用表情联动查询与库文本。EmoRAG 测量 RAG 对符号扰动的反应：同一表情同时出现在查询和库文本里时，检索会偏离语义匹配。作者要检查的是，检索是否真由语义相关性决定。
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