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Oct 13 · Tuesday
Another 213 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.
Uses premise permutation and hidden-layer tracing to audit whether legal chain-of-thought truly relies on the cited legal provisions.
Published Oct 9
Evaluated models Qwen3-8B , Qwen3-14B , Llama-3.1-8B and others7 Abstract Using counterfactual substitution of authorities and internal commitment tracing, the authors find that legal CoT rulings depend on authorities only to a limited extent and are vulnerable to prompt injection manipulation.This study conducts an empirical audit of the causal truthfulness of legal citations in chain-of-thought reasoning of large language models in the legal domain.
测量有损投机解码的安全代价，并提出 SecureSD 收紧早期验证
Published Oct 7
Evaluated models Qwen3-8B , Qwen3-32B , Llama-3.1-8B-Instruct and others6 Abstract 有损推测解码的早期偏离更快抬高越狱与注入风险。作者研究有损推测解码在换取速度时，是否会比效用更快地损害越狱与提示注入防护，并给出按位置校正的 SecureSD。
提出 SecJev，用场景加权训练把决策模型适配为安全类型化判断
Published Oct 2
Evaluated models SecJev-0.8B , SecJev-2B , SecJev-4B and others4 Abstract SecJev 用共享打分器做安全决策，0.8B 的 task-macro 超过 Kev-9B。SecJev 是 0.8B 到 9B 的安全决策模型族，作者称据其所知是首个面向安全的 Jev-like 家族。
用分类后缀提升激活探针对分布外恶意输入的检测
Published Oct 2
Evaluated models Llama-3.1-8B , Qwen3.5-9B , Gemma-4-12B Abstract 作者称排序增益来自分类格式，低 FPR 精度来自点名准则。用户轮后的分类后缀，被作者用作激活探针的分布外检测干预：排序看格式，低 FPR 精度看是否点名恶意/良性。
用因果激活修补定位模型服从提示注入的决策层
Published Sep 29
Evaluated models Qwen3-4B , Qwen3-14B , Qwen3-32B and others7 Abstract 合规的因果杠杆集中在网络后三分之一的低秩子空间，该层也是混淆下检测最好的位置。这篇工作用逐层激活修补定位提示注入合规发生在网络何处，并把该位置用于检测验证。 。
比较用于审计提示注入与隐瞒的 token 位置选择信号
Published Sep 29
Evaluated models Qwen2.5-7B , Gemma-3-12B , Gemma-3-27B and others4 提出 LODO 评测恶意提示探针的跨数据集泛化
Published Feb 15
Evaluated models Llama-3.1-8B-Instruct , Gemma-3-27B , Qwen-3.5-4B and others5 Abstract 作者称 LODO 露出 CV 高估，数据集身份与安全方向部分重叠，标准域泛化干预关不上。LODO 用来检查恶意提示词分类器换成没见过的数据集后，还剩多少检测能力。 。
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