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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.
提出 BLINDBIAS，通过纯文本续写与前缀控制越狱黑盒模型
Published Sep 29
Evaluated models GLM-5 , Gemini-3.5-Flash , Qwen3-32B and others4 Abstract 作者认为，隐藏数值概率并不足以消除支持重复采样和前缀续写的解码攻击面。BLINDBIAS 是一种针对 sample-only 续写接口的 black-box 解码时越狱框架 ，研究没有目标数值概率时，采样能否支持逐步控制。
提出推理通道预填与输出前缀组合攻击以越狱推理模型
Published Sep 24
Evaluated models Gemini 3 Flash Preview , DeepSeek V4 Flash , Claude Haiku 4.5 消融比较方言越狱中表层形式、文化框架与策略库的作用
Published Sep 15
Evaluated models GPT-4o , DeepSeek-Reasoner , DeepSeek-Chat 提出 SAST-IR，用有状态攻击者迭代说服无记忆模型接受反事实
Published Sep 15
Evaluated models DeepSeek-Chat 检验结构越狱 IICL 能否跨厂商泛化并与语言约束叠加
Published Sep 8
Evaluated models gemini-2.5-flash , gemini-2.5-flash-lite 提出 MIPC 与 CFA，观察模型自拟测验后接受开发者身份
Published Sep 3
Evaluated models ChatGPT , Claude , Qwen3.7-Plus and others5 Abstract 自拟测验可造成对话层虚假认证，但所测授权边界并未因此改变。这项工作把“模型自己出题、自己批改、再宣布身份已核验”定义成对话层认证失败，并与后端提权分开。
提出多轮越狱框架 BLUEPRINT，用世界观模拟与心理因素诱导有害输出
Published Sep 2
Evaluated models Qwen3-Next-80B , DeepSeek-V3.2 , GLM-4.7 and others6 Abstract BLUEPRINT 解耦多轮越狱策略与情境模拟，揭示任务具象化是摆脱拒绝的核心机制，为多轮安全防御提供新视角。BLUEPRINT 是一个将因子化社会影响力策略空间与跨轮世界观模拟解耦的多轮大语言模型安全评测框架。
提出 Concept2Scenario，借助内部概念归因发现可迁移的越狱场景
Published Jul 26
Evaluated models Qwen3.5-9B , Llama-3.1-8B , Ministral-3-8B and others6 Abstract 该研究从表征空间因果归因揭示场景削弱拒绝的机制，发现可复用脆弱场景先验，提升多模型黑盒越狱效能与探索速度。
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 评测多语言与编码混淆提示下模型生成钓鱼与键盘记录内容的遵从
Published Jun 29
Evaluated models DeepSeek-V3.2 , Gemini 2.5 Flash , GPT-5 Mini and others6 解释自回归一致性如何导致浅层安全对齐，并提出随机插入攻击
Published Jun 3 Abstract 作者用自回归一致性串起浅层对齐、插入攻击和最坏延续对齐。作者把浅层安全对齐和一类续写攻击都归到自回归一致性上。
构建 MultiTurnPSB，评测四轮医疗越狱与分类器输入防御
Published May 30
Evaluated models GPT-4.1-mini , Claude Sonnet 4.5 Abstract 多轮攻击加剧医疗模型风险，分类器干预有效但受高误报率制约。MultiTurnPSB 针对患者端医疗大模型在持续交互中的安全风险，构建了包含三种攻击模态的四轮对抗评测基准。
提出 Persona Attack，分步写入对话记忆指令以越狱
Published May 29
Evaluated models GPT-4o , Llama-3.2-3B-Instruct , ChatGPT-4o and others5 Propose PCSA, which uses persona-based multi-turn simulation of counseling clients to induce harmful empathetic responses.
Published Apr 7
Evaluated models Llama-3.1-8B , Llama-3.1-70B , GPT-3.5-Turbo and others8 Proposes composite jailbreaks, stacking contrast structure, authoritative persona, and self-evaluation to saturate the instruction hierarchy
Published Apr 3
Evaluated models OpenAI gpt-oss-20b Propose FoodGuardBench and the guardrail FoodGuard-4B to evaluate and reduce food hazard jailbreaks
Published Apr 2
Evaluated models Claude-3.7-Sonnet , GPT-4o , GPT-4.1 and others10 Propose S2C jailbreak, which delays harmful intent integration using contextual rewriting, fragmentation, and clue-guided camouflage.
Published Mar 17
Evaluated models Llama3.3-70B-Instruct , DeepSeek-r1 , DeepSeek-v3 and others16 Proposes the STAR multi-turn role-playing framework, which bypasses safety alignment through dialogue state evolution
Published Mar 15
Evaluated models GPT-4o , Claude 3.5 Sonnet , Gemini 2.0-Flash and others5 Analyzing the attention head mechanism of continuation-triggered jailbreaks and proposing HCS
Published Mar 9
Evaluated models LLaMA-2-7B-Chat , Qwen2.5-7B-Instruct Abstract The authors state that jailbreaks arise from competition between two types of heads, and that HCS plus distillation can use this competition for inference-time intervention.The authors use attention head mechanism analysis for continuation-triggered jailbreaks: under the same harmful instruction, moving the continuation suffix from after the instruction to outside the prompt boundary makes the model more likely to continue generating along the suffix.
提出黑盒 context nesting，用结构化补全任务嵌套有害请求越狱扩散 LLM
Published Jan 31
Evaluated models LLaDA-8B-Instruct , LLaDA-1.5 , Dream-v0-Instruct-7B and others5 Abstract 去噪抑制多种越狱。作者把 D-LLM 相对 AR-LLM 越狱的稳健性称为 safety blessing，并把 context nesting 当作它的 black-box 失效方式。