Within Governance
Should Frontier Models Pass Safety Checks First?
Some proposals would require risk assessments, red-teaming and safety testing before the largest AI projects proceed.
On this page
- Pre Training Risk Assessments
- Independent Red Teaming Requirements
- What Happens When Risks Exceed Limits
Page outline Jump by section
Introduction
One of the most prominent proposals within frontier AI governance is that the most powerful models should pass formal safety checks before training proceeds or before a training run is allowed to continue beyond defined thresholds. The idea is simple: if advanced AI systems could eventually create existential risks through loss of control, dangerous autonomy, deception, or catastrophic misuse, waiting until after a model has been built may be too late. Instead, developers would have to conduct structured risk assessments, submit to independent testing, and demonstrate that identified hazards are being managed before moving forward. [GOV.UK]GOV.UKfrontier ai safety commitments ai seoul summit 2024Frontier AI Safety Commitments, AI Seoul Summit 20247 Feb 2025 — Outcome 1. Organisations effectively identify, assess and manage risks w…
Supporters argue that mandatory evaluations could create an early-warning system for dangerous capabilities and reduce incentives to race ahead without adequate precautions. Critics counter that current evaluation methods remain immature, that future risks may be difficult to predict before a model exists, and that mandatory reviews could slow beneficial innovation without reliably preventing catastrophe. The debate therefore centres not on whether testing is useful, but on whether it should become a legal prerequisite for frontier AI development. [International AI Safety Report]internationalaisafetyreport.orginternational ai safety report 2026International AI Safety ReportInternational AI Safety Report 20263 Feb 2026 — In 2025, 12 companies published or updated their Frontier A…
Why Test Before Training Rather Than After?
In most industries, safety checks happen before potentially dangerous systems are deployed. Frontier AI proposals push this logic further upstream. Instead of waiting until a model is completed, regulators or independent reviewers could examine the planned training run itself.
From an AI doom perspective, the concern is that some capabilities may emerge unexpectedly during training. If a model reaches a level where it can autonomously conduct cyber operations, help design biological weapons, deceive supervisors, or assist in accelerating further AI development, the opportunity to prevent those capabilities may already have passed. Advocates therefore argue that risk reviews should occur before the largest training runs begin and again at predefined milestones during development. Frontier Model Forum [2metr.org]metr.orgcommon elementsof Frontier AI Safety Policies16 Dec 2025 — To manage these risks effectively, the policies include evaluations designed to elicit the fu…
This approach is closely linked to compute governance. Large training runs require exceptional amounts of specialised hardware and electricity. Because those runs are visible and measurable, they create a practical point at which regulators could require evaluations before additional compute is authorised. [Frontier Model Forum]forum.effectivealtruism.orgModel Forum26 Jul 2023 — The core objectives for the Forum are: Advancing AI safety research to promote responsible development of fronti…
Pre-Training Risk Assessments
A pre-training assessment is essentially a structured attempt to answer a difficult question: what could go wrong if this training run succeeds beyond expectations?
Most proposed frameworks begin with threat modelling. Developers identify plausible high-severity hazards, estimate how likely they are to emerge, and specify mitigation plans before training begins. The focus is not ordinary product failures but extreme scenarios relevant to frontier AI risk, such as:
- Dangerous biological or chemical assistance.
- Advanced offensive cyber capabilities. [youtube.com]youtube.comSituational Awareness in Government, with UK AISI Chief Scientist Geoffrey Irving…
- Autonomous replication or self-proliferation.
- Strategic deception of human operators.
- AI systems helping create even more powerful successors.
- Loss of meaningful human control over critical decisions. Frontier Model Forum+2arXiv [forum.effectivealtruism.org]forum.effectivealtruism.orgfrontier model forumModel Forum26 Jul 2023 — The core objectives for the Forum are: Advancing AI safety research to promote responsible development of fronti…
Many recent frontier safety frameworks use capability thresholds. Developers define specific levels of dangerous capability that would trigger additional safeguards, training pauses, deployment restrictions, or further review. The purpose is to avoid discovering that a model has crossed a critical safety boundary only after it has already been widely released. [Frontier Model Forum]forum.effectivealtruism.orgModel Forum26 Jul 2023 — The core objectives for the Forum are: Advancing AI safety research to promote responsible development of fronti…
A recurring challenge is uncertainty. Nobody knows exactly which capabilities future systems will develop or how quickly they will improve. As a result, pre-training assessments are often criticised as attempts to forecast hazards that may not yet be scientifically understood. Even supporters generally acknowledge that these assessments can only reduce uncertainty, not eliminate it. [arXiv]arxiv.orgarXiv Evaluating Frontier Models for Dangerous CapabilitiesarXiv Evaluating Frontier Models for Dangerous Capabilities
Independent Red-Teaming Requirements
A central feature of many proposals is mandatory red teaming by people who are not directly responsible for building the model.
Red teaming refers to deliberately trying to make a system fail. Testers attempt to elicit dangerous behaviours, bypass safeguards, identify hidden capabilities, or discover ways the model could contribute to catastrophic harm. The UK AI Security Institute defines red teaming as attempts to elicit dangerous capabilities in controlled conditions. [AI Security Institute]aisi.gov.ukAI Security InstituteFrontier AI Trends Report by The AI Security Institute (AISI)Red teaming: The process of attempting to elicit danger…
Supporters argue that self-evaluation is vulnerable to conflicts of interest. A company competing in a race to build more capable systems may have incentives to interpret ambiguous results optimistically. Independent red teams provide an adversarial perspective designed to uncover problems that internal teams might overlook. This mirrors practices used in cybersecurity, aviation, and other high-risk fields. [Frontier Model Forum]forum.effectivealtruism.orgModel Forum26 Jul 2023 — The core objectives for the Forum are: Advancing AI safety research to promote responsible development of fronti…
Recent work on frontier evaluations has expanded beyond simple misuse testing. Researchers are developing assessments for capabilities linked to AI doom concerns, including:
- Deception and persuasion.
- Strategic reasoning about oversight. [* Situational awareness.]arxiv.orgarXiv Evaluating Frontier Models for Stealth and Situational AwarenessarXivEvaluating Frontier Models for Stealth and Situational AwarenessMay 2, 2025… [* Cyber offence capabilities.]youtube.comSituational Awareness in Government, with UK AISI Chief Scientist Geoffrey Irving…
- Self-reasoning and autonomous planning.
- Biological risk assistance. [arXiv]arxiv.orgarXiv Evaluating Frontier Models for Dangerous CapabilitiesarXiv Evaluating Frontier Models for Dangerous Capabilities
One motivation is that existential-risk arguments often depend not merely on whether a model can answer harmful questions, but whether future systems could strategically conceal goals, manipulate humans, or circumvent controls. Researchers have therefore begun constructing specialised evaluations aimed at detecting precursors to such behaviour. [arXiv]arxiv.orgarXiv Evaluating Frontier Models for Dangerous CapabilitiesarXiv Evaluating Frontier Models for Dangerous Capabilities
What Happens When Risks Exceed Limits?
Mandatory evaluation proposals become most controversial when they specify consequences.
Many frontier safety frameworks propose predefined thresholds. If evaluations reveal capabilities above a certain risk level, development would not simply continue as normal. Instead, organisations could be required to:
- Pause training.
- Implement additional safeguards.
- Conduct further independent testing.
- Restrict deployment.
- Limit model access.
- Report findings to regulators or designated oversight bodies. [metr.org]metr.orgcommon elementsof Frontier AI Safety Policies16 Dec 2025 — To manage these risks effectively, the policies include evaluations designed to elicit the fu… [Frontier]GOV.UKfrontier ai safety commitments ai seoul summit 2024Frontier AI Safety Commitments, AI Seoul Summit 20247 Feb 2025 — Outcome 1. Organisations effectively identify, assess and manage risks w…
The logic is borrowed from safety engineering. A test only matters if failing it changes behaviour. Otherwise, evaluations risk becoming public-relations exercises rather than genuine risk-management tools.
This principle appears in many emerging frontier safety commitments. The Seoul Frontier AI Safety Commitments, for example, emphasise identifying, assessing, and managing risks during development and deployment, with organisations expected to have processes for responding when serious risks are detected. [GOV.UK]GOV.UKemerging processes for frontier ai safetyprocesses for frontier AI safety27 Oct 2023 — Model Evaluations and Red Teaming can help assess the risks AI models pose and inform bette…
The difficult question is where thresholds should be set. If limits are too strict, they may halt development unnecessarily. If they are too permissive, dangerous capabilities may emerge before intervention occurs. There is currently no consensus on the correct thresholds for catastrophic-risk concerns. [Frontier Model Forum]forum.effectivealtruism.orgModel Forum26 Jul 2023 — The core objectives for the Forum are: Advancing AI safety research to promote responsible development of fronti…
Real Policy Proposals and Experiments
Although no major jurisdiction has yet implemented a mature mandatory pre-training evaluation regime, several proposals illustrate what such systems could look like.
California’s SB 1047, ultimately vetoed by Governor Gavin Newsom, became one of the most widely discussed examples. The bill would have required developers of extremely large models to maintain safety and security protocols, conduct testing for catastrophic risks, and demonstrate compliance before deployment. It also proposed auditing and incident-reporting requirements. Supporters viewed it as an early attempt to operationalise frontier AI safety obligations; opponents argued that it relied on uncertain science and could harm innovation. [The Guardian]theguardian.comThe governor cited concerns that the stringent regulations could drive AI businesses out of the state and stifle innovation. Despite veto… [Wikipedia]WikipediaSafe and Secure Innovation for Frontier Artificial Intelligence Models ActSafe and Secure Innovation for Frontier Artificial Intelligence Models Act [LegiScan At the international level]legiscan.comBill Text: CA SB1047 | 2023-2024 | Regular SessionFeb 7, 2024 — The bill would also require a developer to report each artificial intelli…, governments participating in AI safety summits have increasingly supported independent pre-deployment testing of frontier systems. AI safety institutes in the United Kingdom and United States have also developed programmes for evaluating advanced models before release. [Axios]axios.comGlobal leaders commit to pre-deployment AI safety testingThis coalition intends to establish common protocols for AI safety at the new London-based AI Safety Institute, which will serve as a glo… [Axios]axios.comus frontier ai testing white house pivots safetyramps up frontier AI testing as White House pivots toward safetyThe U.S. government is intensifying its oversight of frontier artificial…
Meanwhile, frontier laboratories have begun publishing safety frameworks that define evaluation procedures, capability thresholds, red-teaming processes, and conditions under which development or deployment should be reconsidered. However, most of these frameworks remain voluntary rather than legally mandated. [International AI Safety Report]internationalaisafetyreport.orginternational ai safety report 2026International AI Safety ReportInternational AI Safety Report 20263 Feb 2026 — In 2025, 12 companies published or updated their Frontier A… [2metr.org]metr.orgcommon elementsof Frontier AI Safety Policies16 Dec 2025 — To manage these risks effectively, the policies include evaluations designed to elicit the fu…
The Strongest Arguments in Favour
For people worried about AI doom, mandatory evaluations serve three functions.
First, they create a mechanism for discovering dangerous capabilities before systems become widely available. If existential risks are real, early detection may be far easier than attempting to regain control later. [arXiv]arxiv.orgarXiv Evaluating Frontier Models for Dangerous CapabilitiesarXiv Evaluating Frontier Models for Dangerous Capabilities
Second, they help solve coordination problems. Individual companies may fear that slowing down for safety will leave them behind competitors. Universal evaluation requirements can reduce pressure to cut corners in a capabilities race. [GOV.UK]GOV.UKfrontier ai safety commitments ai seoul summit 2024Frontier AI Safety Commitments, AI Seoul Summit 20247 Feb 2025 — Outcome 1. Organisations effectively identify, assess and manage risks w…
Third, they generate information. Policymakers, researchers, and the public currently know relatively little about the capabilities of the most advanced models. Standardised evaluations can create a common evidence base for future decisions. Some researchers have argued that both pre-mitigation and post-mitigation evaluation results should be reported so that safety claims can be independently assessed. [arXiv]arxiv.orgarXiv Evaluating Frontier Models for Dangerous CapabilitiesarXiv Evaluating Frontier Models for Dangerous Capabilities
The Main Objections and Uncertainties
Critics do not generally dispute that testing is useful. Their objections focus on whether mandatory evaluations can deliver what their advocates hope.
One concern is that evaluations may miss the very behaviours they are designed to detect. The UK AI Security Institute has highlighted issues such as “sandbagging”, where models may appear weaker during testing than they are in practice. If future systems can strategically conceal capabilities, passing an evaluation may not prove safety. [AI Security Institute]aisi.gov.ukAI Security InstituteFrontier AI Trends Report by The AI Security Institute (AISI)Red teaming: The process of attempting to elicit danger…
Another concern is scientific maturity. Researchers continue to debate how to measure deception, autonomy, situational awareness, and other traits central to long-term AI risk arguments. Existing benchmarks remain experimental, and many experts believe evaluation science is still in an early stage. [arXiv]arxiv.orgarXiv Evaluating Frontier Models for Dangerous CapabilitiesarXiv Evaluating Frontier Models for Dangerous Capabilities
A further objection is geopolitical. If only some countries require extensive evaluations, developers may relocate to jurisdictions with fewer restrictions. This concern leads many supporters of mandatory evaluations to emphasise international coordination rather than purely national rules. [Axios]axios.comGlobal leaders commit to pre-deployment AI safety testingThis coalition intends to establish common protocols for AI safety at the new London-based AI Safety Institute, which will serve as a glo…
Why This Debate Matters for AI Doom
Mandatory frontier AI safety evaluations are ultimately an attempt to answer a simple question: should society wait until powerful systems exist before deciding whether they are too dangerous?
Those who assign a significant probability to AI-driven existential catastrophe often view pre-training and pre-deployment evaluations as one of the most practical near-term interventions available. The proposal does not require agreement that AI doom is likely. It only requires accepting that if future systems might pose extreme risks, discovering warning signs before they are fully developed is preferable to discovering them afterwards. [arXiv]arxiv.orgarXiv Evaluating Frontier Models for Dangerous CapabilitiesarXiv Evaluating Frontier Models for Dangerous Capabilities
Whether mandatory evaluations can reliably identify those warning signs remains uncertain. Yet the growing adoption of frontier safety frameworks, government testing partnerships, independent red-teaming programmes, and capability-threshold approaches suggests that safety checks before training are moving from a theoretical proposal toward a central component of the broader debate over how humanity should manage the risks of increasingly powerful AI systems. [IT Pro]itpro.comThis initiative aims to enhance evaluation frameworks to ensure AI tools are reliable and safe, especially concerning national security a… [GOV.UK]GOV.UKemerging processes for frontier ai safetyprocesses for frontier AI safety27 Oct 2023 — Model Evaluations and Red Teaming can help assess the risks AI models pose and inform bette… [3metr.org]metr.orgcommon elementsof Frontier AI Safety Policies16 Dec 2025 — To manage these risks effectively, the policies include evaluations designed to elicit the fu…
Amazon book picks
Further Reading
Books and field guides related to Should Frontier Models Pass Safety Checks First?. Use these as the next step if you want deeper reading beyond the article.
The Coming Wave
Strongly aligned with debates about mandatory testing, safeguards, and oversight before deploying advanced AI.
Human Compatible
Makes the case for designing and evaluating AI systems for safety before capabilities become difficult to control.
The Alignment Problem
Discusses testing, benchmarking, and identifying risks before deployment.
Superintelligence
Provides much of the intellectual background behind proposals for stringent frontier-model oversight.
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Endnotes
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Title: frontier ai safety commitments ai seoul summit 2024
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Source: metr.org
Title: common elements
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Source: arxiv.org
Title: arXiv Evaluating Frontier Models for Dangerous Capabilities
Link: https://arxiv.org/abs/2403.13793 -
Source: Wikipedia
Title: Safe and Secure Innovation for Frontier [Artificial]({{ ‘artificial-goals/’ | relative_url }}) Intelligence Models Act
Link: https://en.wikipedia.org/wiki/Safe_and_Secure_Innovation_for_Frontier_Artificial_Intelligence_Models_Act -
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Title: arXiv Evaluating Frontier Models for Stealth and Situational Awareness
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Source: axios.com
Title: Global leaders commit to pre-deployment AI safety testing
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Preliminary Taxonomy of AI-Bio Safety EvaluationsDec 20, 2024 — This issue brief offers an initial taxonomy and definitions for frontier...
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by several companies16 AI companies joined the Frontier AI Safety Commitments in May 2024, basically committing to make responsible scali...
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for Frontier AI FrameworksJun 18, 2025 — Frontier AI frameworks outline methodologies for identifying, managing and mitigating the potent...
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nd to understand likely adversary...Read more...
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more...
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Title: frontier threats red teaming for ai safety
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26 Jul 2023 — “Red teaming,” or adversarial testing, is a recognized technique to measure and increase the safety and security of systems...
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Title: second key update technical safeguards and risk management
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Second Key Update: Technical Safeguards and Risk...25 Nov 2025 — This second Update to the 2025 International AI Safety Report assesses...
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Frontier Model Forum — AI Governance Definition & Guide27 Mar 2026 — The Forum has published guidance on topics including responsible dev...
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Additional References
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Link: https://www.orrick.com/en/Insights/2024/07/California-Looks-to-Regulate-Cutting-Edge-Frontier-AI-Models-5-Things-to-Know-About-SB1047Source snippet
California Looks to Regulate Cutting-Edge Frontier AI ModelsJul 19, 2024 — The bill defines a “covered model” as an AI model “trained usi...
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Link: https://www.lawfaremedia.org/article/california-s-proposed-sb-1047-would-be-a-major-step-forward-for-ai-safety-but-there-s-still-room-for-improvementSource snippet
Pros and Cons of California's Proposed SB-1047 AI...May 8, 2024 — SB-1047 would create a new regulatory framework for frontier AI system...
Published: May 8, 2024
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Source: deeplearning.ai
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California's Proposed AI Safety Law Puts Developers at Risk10 Jul 2024 — It puts in place complex reporting requirements for developers w...
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Eight Key Takeaways from California's SB 1047, Vetoed...Sep 29, 2024 — The frontier models that are publicly available are just below th...
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Mapping Frameworks at the Intersection of AI Safety and...22 Sept 2025 — Examples include: SaferAI's 2025 Framework, which integrates re...
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Progress from our Frontier Red Team19 Mar 2025 — In this post, we are sharing what we have learned about the trajectory of potential nati...
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ed teaming, proactively mitigating risks related to harmful bias...Read more...
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Title: california passes first of its kind ai safety law
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California Passes First-of-Its-Kind AI Safety Law9 Oct 2025 — California recently passed groundbreaking AI safety legislation that shifts...
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Misrepresentations of California's AI safety bill27 Sept 2024 — SB-1047 does so by requiring developers of “covered models”–AI models tha...
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What You Need to Know About SB 1047: A Q&A with...19 Jun 2024 — Senate Bill 1047 is designed to apply to models trained above certain co...
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