A Call for Control of Frontier AI Models
Source: 22 national leaders and the European Commission · 2026-09-22
At the same time, the rapid development of frontier AI models poses serious risks to safety and security if not appropriately managed.
Recently we have seen capable AI systems circumventing testing safeguards, exploiting vulnerabilities and gaining unauthorized access to real-world systems.
Leading scientists and executives are warning that the pace of development could outpace our ability to manage emerging risks.
To realise AI's potential, industry, governments and society must act now. We must address these risks and strengthen oversight — without widening the gap between countries in access to the benefits of AI.
AI must remain under human direction, oversight and control. It must be developed and used in line with international law.
Companies to develop transparent safety protocols, including mandatory pre-deployment testing and independent evaluation, with qualified evaluators granted sufficient access to assess risks.
Governments and regional organisations to further develop and coordinate common standards, strengthen transparency — including shared reporting of serious safety incidents — and ensure that countries across all regions have access to scientific capacity, expertise and trusted evaluation.
UN member states to build on existing international mechanisms and explore creating an international institution, able to set standards, enable verification, and convene states when capability thresholds are crossed.
Lines of inquiry this paper opens 24
Research framings built by reading the notes related to this paper — the questions it feeds into.
How reliably can language models perform causal versus temporal reasoning? Do individually safe AI actions create unsafe outcomes in integrated systems?- What safety protections work when simulators have access to real APIs?
- Which AI safety problems lack the scalar metrics autoresearch requires?
- What safety systems prevent therapeutic AI from soothing where it should challenge?
- How should safeguards be built into AI research pipelines?
- What makes human-AI collaboration safer than autonomous self-improvement?
- Where do frontier AI models already exceed safety thresholds in capability areas?
- Why does human-AI collaboration preserve safety compared to autonomous self-improvement?
- What path-dependent mechanisms could lock in societal-level AI harms?
- Why do visible individual harms typically precede abstract catastrophic risks?
- Why do researchers disagree on open model risks despite same evidence?
- How often do deployed AI systems actually get stopped when they cause harm?
- How does autonomy level shape the kinds of risks AI agents pose?
- Does low autonomy AI inherently create different risks than high autonomy AI?
- Do all frontier model developers face the same insider-threat risk from their systems?
- How do we measure marginal risk instead of speculating about misuse scenarios?
- What countermeasures have been successfully developed and tested on frontier models?
- How do cyberattack and bioweapon risks scale with open model access?