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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.
Trace attention to identify harmfulness to the MLP refusal write pathway and propose Sentinel
Published Oct 9
Evaluated models Llama-3.1-8B-Instruct , Qwen2.5-7B-Instruct , Qwen3-8B-Instruct and others18 Abstract Attention updates are more readable for harmfulness, MLP updates write refusal, and Sentinel routes alerts using two-path signals.The authors reframe jailbreaking and excessive refusal of aligned models as a pathway problem on the residual stream from recognition to writing, and accordingly provide conditional reminders before decoding.
提出 Safety Operator，用谱优化调节安全指令表达强度
Published Sep 29
Evaluated models Gemma 3 1B , Gemma 3 4B 用后验重加权解释多模态上下文越狱，并提出风险门控的良性示例注入防御
Published Sep 8
Evaluated models Qwen3-VL-2B , Qwen3-VL-4B , Qwen3-VL-8B and others8 Abstract 后验重加权解释上下文越狱的缩放，并支持风险门控的良性示例防御。Zhang 等把多模态 ICL 越狱写成对齐模型在安全与有害隐模式之间的推理时后验漂移，并据此做风险门控防御。
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 GIFT，用加密与 GPU 信息流跟踪隔离共享 LLM 服务中的用户数据
Published Aug 26
Evaluated models Qwen2.5-14B , Qwen2.5-32B , Qwen2.5-72B and others6 Abstract GIFT 以加密隔离加 GPU 信息流规则，在 vLLM 与 DistServe 上强制用户数据隔离，并报告了吞吐开销。GIFT 是作者为共享 LLM serving 做的用户数据隔离系统，并有启用机密计算的 GIFT-CC。作者在结论中称它是该场景的首个 IFT 设计。
分析少样本示范对 RoP 与 ToP 提示防御抗越狱效果的影响
Published Feb 4
Evaluated models Pangu-Embedded-1B , Pangu-Embedded-7B , Pangu-Embedded-7B-Think and others9 Abstract few-shot 可抬高 RoP、压低 ToP，但表内有反号，Think 模型更低。作者评估 few-shot 示范如何改变 RoP 与 ToP 对越狱的防御效果。
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。
分析按 Schwartz 价值对齐后的意外危害，并提出上下文对齐方法
Published Jun 6, 2025
Evaluated models Llama-2 7B , Llama-3.1 8B , Gemma-2 9B and others6 Abstract 价值对齐会随 Schwartz 价值改变有害倾向。作者研究按 Schwartz 价值对齐的 LLM 为何在安全评测里更易有害，并用提示做了一个不直说要安全的缓解试验。
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