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Oct 12 · Monday
Another 232 papers do not have category tags yet and are not included in filtered results.
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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 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 AlcaTRAz，用锚定字符规则树改写提示以防御越狱
Published Sep 3
Evaluated models LLaMA 2 , LLaMA 3.1 , LLaMA 3.2 and others14 提出 DASE，以对抗自演化知识库在推理时加固角色扮演抗越狱
Published Jan 29
Evaluated models Kimi-K2-Instruct , GPT-5.2 Abstract DASE 用双循环知识库，在两家闭源模型上同时提高保真分与拒答率。DASE 是给专有闭源角色扮演智能体用的训练自由防御，目标是人设保真与安全拒答一起成立，而不是改模型权重。问题在于人设沉浸，尤其负面人设，会压过原有护栏。SFT、数据筛选和正则又跟不上新人设与新攻击，也不能用于没有梯度的 black-box API。
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