Failure-mode testing
Known failure modes including hallucination and instruction failure are explicitly tested.
OpenAI rolled back a GPT-4o update after it produced overly agreeable and flattering behaviour. OpenAI later said its pre-launch review did not adequately catch the behaviour shift.
The incident demonstrated that aggregate preference signals and standard pre-launch evaluation can miss harmful changes in model behaviour at scale.
OpenAI reported that combined model changes, including an additional user-feedback reward signal, weakened controls against sycophancy and that existing evaluations did not sufficiently block the release.
Rollback to the previous version, revised behavioural review, stronger guardrails and expanded pre-deployment testing.
This record should inform control design, testing and monitoring for comparable AI systems. The incident database does not infer that every system using the same provider or model shares the same failure.
These are CRG methodology mappings from the documented incident to controls worth testing in comparable systems. They do not assert that any single control would have prevented the incident.
Known failure modes including hallucination and instruction failure are explicitly tested.
Go-live and ongoing performance thresholds are defined and enforced.
Controls address automation bias, overreliance and misleading confidence.
Material performance, safety, security and cost signals are monitored after release.
OpenAI · confidence 99% · last verified 23 Aug 2026
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