Translation in progress Some entries still include text in the original language.
Oct 13 · Tuesday
Another 213 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 FRA-Attack，用频域对齐与梯度正则做可迁移定向视觉攻击
Published May 20
Evaluated models GPT-5.2 , GPT-5.4 , Claude-Opus-4.6 and others15 Abstract FRA-Attack 以频域对齐和梯度低通做可迁移定向攻击，闭源面板上作者称整体更强，Gemma 除外。FRA-Attack 是面向闭源 MLLM 的迁移式定向图像攻击。作者要解决的是：空间域局部对齐重复全局低频，代理梯度的高频成分又把更新拉向代理自身。
提出 GPO-V，用全局概率优化的视觉扰动越狱扩散视觉语言模型
Published May 8
Evaluated models LLaDA-V , LaViDA Abstract 作者用视觉扰动改 dVLM 的全局生成概率，以绕过两类拒绝。GPO-V 把越狱从“开头必须顺从”改成“整段去噪概率朝肯定方向走”。 。
提出 SWhisper，用近超声波向语音驱动 LLM 隐蔽注入越狱提示
Published Mar 14
Evaluated models Meta-Llama-3.1-8B-Instruct , Gemma-3-4B , Qwen2.5-7B-Instruct and others9 提出 MM-Plan 多模态越狱框架，自动规划多轮图像编辑以越狱视觉独占目标
Published Feb 5
Evaluated models Llama-3.2-11B , InternVL3-8B , Qwen3-VL-8B and others8 Abstract 作者称全局规划能攻破对单轮攻击更稳的前沿 MLLM。MM-Plan 是面向 Visual Exclusivity 的 black-box 多模态红队方法，并配有人工基准 VE-Safety。
提出 UltraBreak，用单张对抗图像对视觉语言模型实施可迁移越狱
Published Feb 1
Evaluated models Qwen2-VL-7B-Instruct , Qwen-VL-Chat , Qwen2.5-VL-7B-Instruct and others13 提出多轮多模态越狱，并用 FragGuard 拦截有害响应
Published Jan 9
Evaluated models LLaVa-1.6 (7B) , LLaVa-1.6 (13B) , Qwen-2-7B and others5 Attacks arXiv 2511.16110 · Nov 20, 2025 提出 MFA 框架，协同突破视觉语言模型的对齐、提示与审核层
Published Nov 20, 2025
Evaluated models MiniGPT-4 , LLaMA-4-Scout-Inst , LLaMA-3.2-11B-Vision-Instruct and others17 Abstract 作者称多层防御的 VLM 仍存在跨模型漏洞，MFA 整合三项技术实现了端到端越狱。
Attacks arXiv 2511.13892 · Nov 18, 2025 提出图像排版操纵与多轮提示越狱，绕过交通场景视觉语言模型安全对齐
Published Nov 18, 2025
Evaluated models LLaVa-1.6 , Qwen-2 , GPT-4o-mini You have reached the end