Text-to-Image (T2I) diffusion models enable high quality open ended synthesis, but practical use requires suppressing unsafe generations while preserving behavior on benign prompts. We study this tension relative to the frozen generator, using its prompt conditioned distribution as the preservation reference. Since T2I safety is commonly evaluated by bounded risk scores on generated images, total variation (TV) bounds how much expected risk can change from this reference. We call this fixed reference constraint the Safety-Prompt Alignment Tradeoff (SPAT): reducing expected unsafety requires prompt conditioned distributional deviation. To make this deviation selective and adjustable, we define the tau safe set as prompts whose reference risk is at most tau, and cast intervention as projection toward nearby prompts in this set. We propose Selective Prompt prOjecTion (SPOT), an inference time framework that approximates this projection without retraining the generator or learning a category specific rewriter. SPOT uses an LLM to rank candidate rewrites and a safeguard VLM to accept generated images under the same tau. Across four datasets and three diffusion backbones, SPOT achieves relative inappropriate (IP) score reductions from 14.2% to 44.4% over strong safety alignment baselines while keeping benign prompt behavior close to the fixed reference.
Counterfactual Neighborhood Risk Fields: Evidence-Grounded Selective Safety for Vision-Language Models
Tongji University（同济大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Electronic Science and Technology of China（电子科技大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Time-Series Forecasting in Safety-Critical Environments: An Open-Source Package for EU-AI-Act-Compliant Development / Zeitreihenprognose in sicherheitskritischen Umgebungen: Ein Open-Source-Paket für die KI-VO-konforme Entwicklung
Safety Alignment Should Be Made More Than Just A Few Attention Heads
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）
Honda Research Institute (HRI) Europe（本田欧洲研究院（HRI 欧洲））
Risk assessment is a central element for the development and validation of Autonomous Vehicles (AV). It comprises a combination of occurrence probability and severity of future critical events. Time Headway (TH) as well as Time-To-Contact (TTC) are commonly used risk metrics and have qualitative relations to occurrence probability. However, they lack theoretical derivations and additionally they are designed to only cover special types of traffic scenarios (e.g. following between single car pairs). In this paper, we present a probabilistic situation risk model based on survival analysis consid…
The Blind Spot of Agent Safety: How Benign User Instructions Expose Critical Vulnerabilities in Computer-Use Agents
University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of Minnesota（明尼苏达大学）；University of Southern California（南加州大学）；McGill University（麦吉尔大学）；Mila（Mila研究院）；MBZUAI（MBZUAI研究院）
Department of Civil and Environmental Engineering, University of Wisconsin-Madison（土木与环境工程系，威斯康星大学麦迪逊分校）；School of Computer, Data and Information Sciences, University of Wisconsin-Madison（计算机、数据与信息科学学院，威斯康星大学麦迪逊分校）
Short-term load forecasting under EU-AI Act Requirements in Safety-Critical Environments: Results from a 41-day live challenge on the aggregated German transmission-grid load
Inference-Time Selective Debiasing to Enhance Fairness in Text Classification Models
MBZUAI（穆罕默德·本·扎耶德人工智能大学）；AIRI（人工智能研究所）；RUDN University（俄罗斯人民友谊大学）；FRC CSC RAS（俄罗斯科学院联邦研究中心计算机科学与控制研究所）；IIIT Delhi（德里印度信息技术学院）；The University of Melbourne（墨尔本大学）
