Two of OpenAI's own incidents are on this page: the Hugging Face intrusion in July, and the swarm that turned a dormant German wiki into a message board. This is the company's account of why the two were handled differently. What the statement does not say, and what the other entries here establish, is the consequence: the wiki was known to OpenAI before the Hugging Face incident and was never disclosed as an incident of its own, and it was outside researchers who found it and published, months later. Worth reading against what it cost the people at the other end of it. One volunteer moderator cleared about a hundred pages an evening against four hundred a day, for five days, and then spent five weeks cleaning up.
How we think about the “wiki incident,” where our agents wrote to several internet sites: it’s past time for us to define standards for when and how we share misalignment incidents, not just misalignment properties of our models.
Historically, we have treated misalignment largely as a research question, which gets communicated in research publications such as systems cards. This year, we’ve started to see misalignment cause new types of real-world impact.
For the Hugging Face incident, where misalignment led to security impact to us and third parties, we followed a traditional security incident response playbook. We immediately started working with Hugging Face to understand what had happened and also disclosed publicly the very next day. Our investigation continues, and we are continuing to notify parties whom our models impacted in less significant ways.
Prior to the Hugging Face incident, we saw early signs of agents using the internet in unintended ways, as reported in openai.com/index/how-we-monitor-internal-coding-agents-misalignment/, deploymentsafety.openai.com/gpt-5-6, and openai.com/index/safety-alignment-long-horizon-models/. We considered the wiki incident to be an instance of misalignment similar to the ones we’d shared.
Our misalignment disclosure practices need to expand for this new phase of model capabilities. We and the larger AI community do not yet have a clear standard for how to report misalignment that shows up during training, evaluation, and deployment, including examples that don’t look like traditional security incidents but could provide insight into AI behavior and future risks. We’re working on a framework and will share it in upcoming weeks, and in parallel we're working with dozens of government regulatory agencies worldwide on these issues.
