Translation in progress Some entries still include text in the original language.
Oct 12 · Monday
Another 233 papers do not have category tags yet and are not included in filtered results.
“
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.
重测并扩展对 AI Agent 的提示注入，并用 Agent Card 欺骗操纵 A2A 选择
Published Oct 8
Evaluated models Ollama Mistral 0.9.5 , llama3-8b-8192 , gemini-2.0-flash-lite and others5 Abstract 单智能体过滤与分类可被格式或措辞绕过，A2A 选卡还会偏向名称和描述。作者在自建的 LangChain、AutoGen 和 A2A 系统上重测提示注入，并改 Agent Card 看选择是否偏移。摘要称试验主要凸显单智能体弱点，同时把攻击扩到多智能体。
提出 BAM 反馈编码，在 LLM 生成文本中实现隐蔽通信
Published Sep 22
Evaluated models Llama-3.1-8B , Qwen-3.5-9B-Base , Mistral-7B-v0.3 and others6 Propose Thought Virus, which propagates preferences along multi-agent messages via subliminal prompts.
Published Feb 23
Evaluated models Qwen2.5-7B-Instruct , Llama-3.1-8B-Instruct Abstract Thought Virus spreads a single-point bias downstream.Thought Virus puts subliminal prompting into multi-agent communication, to see whether a single-point deviation spreads across the network without naming the target concept.
用 OpenRAG-Soc 评测社交网页 RAG 的间接提示注入与检索投毒
Published Jan 16
Evaluated models Llama-3-8B-Instruct , Mistral-7B-Instruct , Qwen2.5-14B and others8 提出面向渗出的权重压缩，评估推理服务器权重窃取可行性
Published Jan 4
Evaluated models Qwen2-1.5B , Qwen2-7B , Qwen2.5-1.5B and others8 Abstract 作者用残差量化加微调压缩权重，并称法医水印便宜且微调后仍可解码。这篇工作把 LLM 权重渗出看成 APT：攻击者已有未加密权重，但必须把完整权重藏进推理 API 的合法输出。作者认为可压缩性是被忽略的因素，因为推理量化要求解压后仍能低开销前向，而偷权重的人可以接受事后微调。
You have reached the end