Frontier risk and preparedness

Our updated Preparedness Framework

Sharing our updated framework for measuring and protecting against severe harm from frontier AI capabilities.

Strengthening our Frontier Safety Framework

We’re strengthening the Frontier Safety Framework (FSF) to help identify and mitigate severe risks from advanced AI models.

Our approach to AI safety

Ensuring that AI systems are built, deployed, and used safely is critical to our mission.

Democratizing AI Safety with RiskRubric.ai

Safety Must Precede the Deployment of Open-Ended AI

arXiv:2502. 04512v4 Announce Type: replace Abstract: AI advancements have been significantly driven by a combination of foundation models and curiosity-driven learning aimed at increasing capability and adaptability.

Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI

arXiv:2607. 14353v1 Announce Type: cross Abstract: As automated decision-making and data-driven technologies pervade society and are used to manage consequential outcomes, understanding the technology's capabilities, limitations, and attendant risks in context requires analysis of full sociotechnical systems.

Towards safety cases for frontier AI training

The article presents early guidelines for safety cases in frontier AI training, outlining technical safeguards, operational practices, and methods for investigating misalignment incidents. It emphasizes the importance of structured safety documentation to guide the development and deployment of advanced AI systems. The guidelines aim to provide a framework for identifying and mitigating risks associated with frontier AI training.

The Normalization of Deviance in AI Development

arXiv:2609.05749v1 Announce Type: new Abstract: Work on the risks of artificial intelligence has focused predominantly on capability risk: the danger that systems become too powerful, too autonomous,...

Safety and alignment in an era of long-horizon models

OpenAI shares lessons from deploying long-running AI models, highlighting new safety risks, observed failures, and improved safeguards through iterative deployment.

Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond

arXiv:2607. 02197v1 Announce Type: cross Abstract: The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems.

Frontier Model Forum

We’re forming a new industry body to promote the safe and responsible development of frontier AI systems: advancing AI safety research, identifying best practices and standards, and facilitating information sharing among policymakers and industry.

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios

In this work, we propose an agentic gamification framework for hazard-informed learning of robot safety policies through synthetic scenarios. We model scenario generation as an adversarial game between two agents: a Red Team that explores the space of potential failures by constructing hazardous situations, and a Blue Team that incrementally refines safety policies to prevent them.