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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.
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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。
用 MMPIBench 评测多模态提示注入对 Agent 框架的影响
Published Sep 9
Evaluated models Claude Opus 4.8 , GPT-5.4 , Gemini 3.1 Pro and others6 Abstract 论文提出 MMPIBench 评测多模态提示注入对智能体的影响，发现模型主导行为、尝试远超完成，音频通道缺乏防护。
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 ARENA 音频红队框架，自动构造文本安全且音频接地的越狱输入
Published Aug 16
Evaluated models Audio Flamingo 3 , Qwen2-Audio-7B-Instruct , MiMo-Audio-7B-Instruct and others4 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 提出音频叙事越狱，以嗓音投递风格绕过端到端语音模型拒答
Published Jan 31
Evaluated models GPT-4o Realtime , Gemini 2.0 Flash , Qwen2.5-Omni-7B Attacks arXiv 2511.10692 · Nov 12, 2025 提出 StyleBreak 风格化音频越狱，利用语音属性绕过音频语言模型对齐
Published Nov 12, 2025
Evaluated models Qwen2-Audio-7B-Instruct , MERaLiON-AudioLLM-Whisper-SEA-LION , Ultravox-v0.4.1-Llama-3.1-8B and others6 Attacks arXiv 2504.01094 · Apr 2, 2025 提出 MULTI-AUDIOJAIL，以多语多口音和声学扰动越狱音频模型
Published Apr 2, 2025
Evaluated models Qwen2-Audio , DiVA-llama-3-v0-8b , MERaLiON-AudioLLM-Whisper-SEA-LION and others5 Abstract 黑盒多语多口音音频加声学扰动可抬高 LALM 的 JSR，文本示范防御并非在每个模型上都下降。
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