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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.
用配对续写与激活修补分析工具智能体越狱后的安全路由
Published Sep 28
Evaluated models Qwen3-14B , Qwen3.5-9B , Qwen3.5-27B and others8 Abstract 越狱后的同一安全反馈在八个工具智能体上分成三条路由，因果集中在最后几层并可作预测基线。这篇工作把越狱上下文残留之后的运行时安全反馈，做成工具智能体上的三路路由问题，并用激活修补给这个路由一个晚层的因果位点。作者认为，反馈可能恢复合法执行、留下未授权动作，或误伤良性流程，而现有攻击、基准和静态护栏没有在固定历史上把这三种结局分开。配对续写因此从同一冻结检查点分别接中性提醒 N 和安全反馈 S，并交叉保留越狱 J+ 与中和越狱 J0。
分析量化、剪枝与 KV 缓存压缩对推理模型越狱鲁棒性的影响
Published Sep 20
Evaluated models QwQ-32B , DeepSeek-R1-Distill-Qwen-32B , DeepSeek-R1-Distill-Llama-8B Abstract 效率压缩常使 LRM 越狱 HR 下降，作者将其归因于推理退化而非对齐增强。作者分析量化、剪枝和 KV 缓存压缩如何同时改变 LRM 的显存、推理和越狱脆弱性。 。
检验权重谱度量能否代理成员推断泄漏
Published Sep 11
Evaluated models MLP-1×4096 , MLP-2×1024 , MLP-2×2048 and others11 Abstract 作者认为谱度量与泛化差距互补，有望用于无数据的模型级筛查。作者在 MLP 上检查 WeightWatcher 谱统计是否与 online LiRA 同向，以便不看数据、不训练 shadow 模型就做模型级成员风险筛查。放出前的审计要知道训练成员会不会被认出来。LiRA 依赖大量 shadow 模型，候选一多就难以逐个跑。
在写入点定位拒答方向，经刚性旋转跨架构迁移并门控放大
Published Sep 4
Evaluated models Falcon-Mamba-7B , Falcon3-Mamba-7B , Llama-3.1-8B and others8 Abstract 拒答方向可经刚性旋转跨架构迁移，但必须在各架构的写入点读取。作者研究拒答表示是否随架构改变，结论是表示共享、读取位置随架构变。
用配对归因图解释越狱如何改写模型内部计算
Published Jul 9
Evaluated models Llama-2-7B-chat-hf Propose LOCA, which explains single jailbreak successes via iterative activation patching along SAE directions
Published May 1
Evaluated models Llama-3.1-8B-Instruct , Gemma-3-27B-IT , Gemma-2-2B-IT and others4 Abstract LOCA uses a small number of activation patches along SAE directions to locally explain why a single jailbreak can bypass refusal.LOCA explains why a single jailbreak can bypass refusal using as few interpretable intermediate representation changes as possible.
分析续写触发越狱的注意力头机制并提出 HCS
Published Mar 9
Evaluated models LLaMA-2-7B-Chat , Qwen2.5-7B-Instruct Abstract 作者称越狱来自两类头竞争，HCS 加蒸馏可把该竞争用于推理时干预。作者用注意力头机制分析 continuation-triggered jailbreak：同一有害指令下，把 continuation 后缀从指令后面挪到提示词边界外，模型更容易顺着后缀继续生成。
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