OpenAI has published a formal framework outlining priorities and principles for third party assessments of its most advanced AI systems, saying external scrutiny should be substantive, secure, and technically rigorous. The company frames these third party assessments as complementary to government testing and oversight, and as mechanisms that can probe training, evaluation and deployment while protecting sensitive data and intellectual property.
The framework identifies four priority areas where outside experts can add the most value. First, OpenAI calls for independent evaluation of safety cases across model training and deployment, asking assessors to judge whether the documented evidence supporting a model’s safety claims is adequate and whether agreed safety conditions were followed. Second, the company highlights assessment of critical safeguards — the layered protections that sit between a model and real-world harm. Third, it asks for capability and alignment evaluations tied to its Preparedness Framework, which centers on risks such as chemical and biological threats, cybersecurity, and AI self-improvement. Finally, OpenAI supports independent investigations of serious misalignment incidents, bringing rapid external expertise to bear when models behave in unexpected or harmful ways.
On safety cases, OpenAI says assessors with expertise in alignment, control methods, cybersecurity and misuse risk should examine whether submitted evidence substantiates safety claims and whether incentives in training might encourage deception or other risky behavior. The company emphasizes that these reviews should span internal deployment as well as external release, so reviewers can evaluate both the development context and the conditions under which a model operates.
When it comes to safeguards, the framework asks third parties to test evolving “safeguard stacks” that include model-level protections, enforcement mechanisms, security controls and monitoring for misalignment. Assessors are encouraged to test systems under realistic conditions, including grey-box adversarial approaches that simulate an informed attacker with some internal knowledge. The goal is to identify vulnerabilities such as jailbreaks, capability uplift in high-risk domains (for example cyber or biological), and gaps in detection or containment measures, and to evaluate whether monitoring covers relevant use cases and scales with increasing model capability.
Preparedness-aligned capability and alignment evaluations are the framework’s third priority. OpenAI requests that external reviewers verify whether capability tests adequately cover thresholds described in the Preparedness Framework and evolve as models improve. Reviewers should also examine alignment evaluations for blind spots that might miss severe misalignment behaviors or contexts in which a model could act harmfully.
For serious incidents, OpenAI supports rapid, independent investigations that can bring specialized skills such as digital forensics, alignment analysis and large-scale review of chain-of-thought traces. The company notes such inquiries may require access to highly sensitive internal logs and third party data; findings from these investigations can update a model’s safety case and inform remediation steps.
Alongside priority areas, OpenAI lays out core principles intended to ensure assessments are effective, secure and trustworthy. These include clearly scoped, pre-registered claims for assessment and mechanisms to handle significant risks discovered outside the original scope. Access should be proportionate — balancing an assessor’s need for evidence with legal, security and intellectual-property constraints — and the framework encourages alternative access arrangements where full direct access is impractical.
The principles call for transparent methodologies and standards, with assessors distinguishing direct findings from interpretation and disclosing uncertainties. Technical expertise and conflicts-of-interest disclosures are required to guard against commercial influence, and assessors must follow robust information-security and confidentiality protocols. The framework also urges that findings be actionable, allowing time for remediation before publication, and that publication practices maximize openness while protecting sensitive data through principled redaction and editorial independence.
OpenAI says it will support the growth of an independent assessment ecosystem and help develop shared international standards through private governance and future laws. The company stresses that no single third party can address every frontier safety question and that multiple external organizations will be engaged to cover the outlined priority areas and adhere to the stated principles.
The full framework, published by OpenAI, is intended as a guide for collaborations with private and non-profit assessment organizations and to complement government-led testing and oversight. By coupling strong independence mechanisms with proportionate safeguards, OpenAI aims to enable meaningful external scrutiny of its frontier models without exposing highly sensitive information.
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