Translation in progress Some entries still include text in the original language.
Oct 13 · Tuesday
All papers Found 13 papers Another 215 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.
提出 PEV 攻击，在输入嵌入上加高斯噪声越狱开源模型
Published Oct 6
Evaluated models Llama-3.1-8B-Instruct , Qwen2.5-1.5B-Instruct , Phi-4-mini-3.8B and others6 Abstract PEV 用 embedding 高斯噪声，在六个模型上越狱 JailbreakBench 全部提示。PEV 是对 open-weight LLM 的 embedding 噪声越狱。
提出 CRIME 框架，组合良性 Agent 技能诱发恶意行为
Published Oct 5
Evaluated models DeepSeek-V4-Flash , GPT-4o-mini , GPT-4o and others6 Abstract 良性技能经 DCA 或 ACA 组合后可产生恶意环境后果，单独审查对不上。CRIME 把公共技能库中各自通过审查的技能配成两两组合，看联合执行能否实现指定恶意任务。 。
提出 SEAL，在 MoE 共享专家上做 DPO-LoRA 对齐以抵御越狱与权重篡改
Published Sep 2
Evaluated models Qwen1.5-MoE-A2.7B , DeepSeekMoE-16B , GLM-4.7-Flash and others4 Abstract SEAL 把 DPO-LoRA 限制在混合 MoE 共享专家上，SEAL++再保护已有安全神经元方向。SEAL 是面向混合 MoE 的训练时防御：只对齐始终执行的共享专家，使安全不依赖路由器是否把 token 送到安全专家。
提出 SN-Guided Diffusion，利用安全神经元实现离线越狱
Published Aug 8
Evaluated models Qwen2.5-7B-Instruct , LLaDA-8B-Instruct , Dream-Instruct-7B and others15 Abstract 论文揭示了扩散语言模型对自回归安全神经元的继承性，并提出纯离线黑盒越狱框架 SN-Guided Diffusion。论文系统评估了扩散大语言模型（DLLM）在安全对齐中的双重角色——既作为白盒攻击目标，又作为黑盒越狱生成的对抗工具。
提出 AHA 自动科研循环，发现生产 Agent 的可复用漏洞概念
Published Jul 13
Evaluated models Minimax-M2.7 , Kimi-K2.6 , Deepseek-V4-Pro and others5 Abstract 论文提出基于可证伪科研循环的 AHA 框架，从生产级智能体中挖掘并沉淀漏洞概念图，提升攻击复用率并指导防御加固。AHA 是一项针对生产级 LLM 智能体自动化红队测试的研究，旨在解决传统方法仅保留具体攻击载荷而缺乏成因解释、导致跨版本复用困难且无法指导修复的问题。
提出 SMT 框架，用模拟审核轨迹越狱函数调用 LLM
Published Jul 1
Evaluated models GPT-4o , GPT-5.4 , Qwen3-Max and others7 提出 HMNS，通过注意力头掩码与零空间注入实现机制级越狱
Published Apr 12
Evaluated models LLaMA-2-7B-Chat , Phi-3-Medium-4K-Instruct , LLaMA-3.1-70B and others6 Proposes ShallowJail, which achieves jailbreaks by rewriting hidden states with steering vectors at inference time
Published Feb 7
Evaluated models Qwen3-4B-Instruct-2507 , Qwen2.5-7B-Instruct , Llama-3.1-8B-Instruct 提出 Tool Obfuscation 并评测函数调用 Agent 的攻防
Published Jan 14
Evaluated models Qwen3:8B , Llama-3.2:3B , Granite3.2:8B and others5 提出 iMIST，用工具伪装和 PPO 迭代越狱大语言模型
Published Jan 9
Evaluated models DeepSeek-V3 , Qwen3-32B , GPT-OSS-120B Abstract iMIST 用工具伪装和在线 PPO 做黑盒越狱，Table I 中 JADES 高于同格基线。iMIST 是黑盒越狱方法：把恶意查询伪装成合法工具调用，再用在线强化学习把响应的有害性分数往上推。
提出 JAWS-Bench，评测代码 Agent 在三级工作区中的越狱与恶意代码可执行性
Published Oct 2, 2025
Evaluated models GPT-4.1 , GPT-o1 , DeepSeek-R1 and others8 Abstract 论文提出了评估代码智能体端到端恶意代码越狱的 JAWS-Bench，发现多文件与智能体推理会明显放大可部署危害。
Attacks arXiv 2509.22067 · Sep 26, 2025 提出激活转向越狱，用随机向量在推理时绕过安全拒答
Published Sep 26, 2025
Evaluated models Llama3.1-8B , Llama3.1-70B , Qwen2.5-3B and others8 Abstract 作者称激活转向会系统性破坏 LLM 对齐，良性特征与随机向量均能诱发有害合规，聚合随机向量可构建通用攻击。
Attacks arXiv 2508.10029 · Aug 9, 2025 提出 Latent Fusion Jailbreak，插值有害与无害隐藏状态实施白盒越狱
Published Aug 9, 2025
Evaluated models Vicuna-7B-v1.5 , LLaMA-2-7B-Chat , Guanaco-7B and others5 You have reached the end