Glossary · term

Frontier Model Forum

The Frontier Model Forum (FMF) is a member-funded, industry-supported U.S. nonprofit association focused on the safety and security of frontier AI. It convenes major developers, publishes technical material, supports research, and operates a mechanism for sharing selected risk information. It is not a regulator, certification body, safety institute, or assurance that every member model is safe. On 5 September 2026, FMF listed six members: Amazon, Anthropic, Google, Meta, Microsoft, and OpenAI.

Regulation2023-07-26Wave 2 · 2024Maturity: 3/5

Origin and context

Anthropic, Google, Microsoft, and OpenAI announced FMF on 26 July 2023 as an industry body for safety research, best practices and standards, and information sharing. Amazon and Meta joined in May 2024. FMF now describes itself as a 501(c)(6) nonprofit led by an executive director, governed by an operating board of member representatives, and financed by member fees. That structure makes it a durable organization rather than a one-off pledge.

Sources: s1, s2, s3, s6, s7

Why it matters

FMF gives competing frontier-model developers a venue to compare practices where disclosure may be sensitive. Its annual report records work on biological, cyber, model-security, and frontier-framework questions; more than $10 million allocated through the AI Safety Fund; and a member agreement for sharing information about vulnerabilities, threats, and concerning capabilities. These are concrete coordination outputs, but most operational evidence is reported by FMF itself and should not be read as independent validation of effectiveness.

Sources: s4, s5, s6

Example

Suppose a member identifies a safeguard bypass that is unusually relevant to frontier systems. FMF's pilot mechanism can support restricted exchange with other members under defined legal and technical controls. That is information sharing for collective learning; it is not automatically a report to a regulator or a coordinated incident response. FMF's 2026 brief separates those three functions and says the current agreement covers only specified frontier-risk categories.

Sources: s5

How it differs

Frontier AI Safety Commitments

The Frontier AI Safety Commitments are voluntary promises convened by the UK and Republic of Korea for a wider set of companies. FMF is a continuing member organization that analyzes practices related to those commitments; it is not the commitments themselves or their enforcement body.

RSP / ASL

Anthropic's Responsible Scaling Policy is an evolving policy of one FMF member, with company-specific thresholds and controls. FMF compares frontier-framework approaches across members but does not turn an individual RSP into a binding common policy.

AI safety institutes

AI safety or security institutes are government-backed technical organizations that research and evaluate advanced AI to inform public policy. FMF is funded and governed by member companies. Cooperation between them does not give FMF public authority or make an institute an industry trade body.

Maturity and evidence

Maturity is rated 3. FMF has nearly three years of continuity, a legal and governance structure, six current members, repeated publications, two funded grant rounds, and an operating information-sharing program. Independent reporting and research use the organization as a stable referent. A rating of 4 would overstate the evidence: membership is concentrated among funders, outputs remain voluntary, and independent studies do not establish that FMF caused better safety outcomes.

Sources: s2, s3, s4, s5, s6, s7

Limits and open questions

FMF's operating board represents member firms and its revenue comes from member fees, so readers should separate coordination value from independent oversight. Technical reports often synthesize member practice and may not demonstrate consensus beyond that group. Fund totals, participation, or an information-sharing agreement do not prove that models are safe, that incidents are comprehensively disclosed, or that recommendations are implemented. Those claims require external evidence and safety-domain review.

Sources: s2, s4, s5, s6

Related terms

References

Last updated: 2026-09-07

In the Skills Atlas

This term is also covered in the Skills Atlas as ai risk management skill.

In the Skills Atlas

This term is also covered in the Skills Atlas as model evaluation skill.