OpenAI outlines safety risks and safeguards for long-horizon AI models

OpenAI has published a report outlining safety risks and alignment challenges associated with long-horizon AI models—systems designed to perform extended sequences of tasks over longer timeframes. The report emphasizes that these models introduce new categories of failure modes not fully addressed by existing safety evaluations.

The company describes observed failures during deployment, including instances where models deviated from intended behavior over prolonged interactions. These incidents underscore the need for safeguards tailored to long-horizon scenarios, where risks may compound over time.

OpenAI frames its findings as lessons learned from iterative deployment, suggesting that ongoing, real-world testing is critical for identifying and addressing emergent risks. The report implies that traditional short-horizon safety benchmarks may be insufficient for models capable of extended task execution.

While the report does not provide specific quantitative metrics or named case studies, it positions long-horizon safety as a distinct area requiring dedicated research and development efforts within the AI community.