Priorities for Frontier AI Policy
A policy agenda for frontier AI, proposing lifecycle oversight including internal models, American AI leadership measures, and national resilience against AI-enabled cyber, bio and distillation threats.
Research / Sources
IAPS
Policy research on advanced AI security and governance. www.iaps.ai
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The OpenAI/Hugging Face Incident: Challenges in Controlling and Containing Cyber-Capable AI Systems
IAPS policy memo on the 2026 incident where OpenAI models escaped an evaluation sandbox and autonomously attacked Hugging Face, and what policymakers should do about it.
IAPS Reacts to the White House Executive Order on AI Innovation and Security
IAPS reacts to a White House executive order on frontier AI security, assessing its cyber-defense provisions, voluntary developer collaboration, and unresolved implementation questions.
After Mythos: A National Security Playbook for Frontier AI
A policy memo proposing US executive actions to address national security risks from frontier AI, including securing model weights, automated cyber defense, information sharing, and government capacity.
Risk Reporting for Developers’ Internal AI Model Use
Proposes a harmonized standard for frontier developers' internal-use risk reports, aligned with California SB 53, New York RAISE Act, and the EU GPAI Code of Practice.
Asymmetry by Design: Boosting Cyber Defenders with Differential Access to AI
This IAPS report proposes three differential access approaches that let frontier AI developers tilt dual-use cyber capabilities toward defenders, with a framework for choosing among them based on risk.
Coordinated Disclosure of Dual-Use Capabilities: An Early Warning System for Advanced AI
Proposes a Coordinated Disclosure of Dual-Use Capabilities process, with a government clearinghouse receiving reports of dangerous AI capabilities so defenders get early warning and time to respond.
Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Framework
Argues that external scrutiny, including third-party auditing, red-teaming and researcher access, is needed for public accountability over frontier LLM decisions, and sets out six requirements under the ASPIRE framework with policy recommendations.
Deployment Corrections: An Incident Response Framework for Frontier AI Models
Proposes an incident response framework and toolkit of deployment corrections that frontier AI developers can use to handle dangerous capabilities or behaviours discovered after model deployment, with recommendations for standardisation.