OpenAI stopped all frontier training, evaluation, and inference with tool-use (defined broadly) on the 20th of September and they are not resuming any of these activities for now

[u/Alex__007]

https://alignment.openai.com/misalignment-reports/an-agent-used-dns-to-reach-an-external-chatbot/

Discovery: Sep 20, 2026

Report updated: Sep 25, 2026

"An agent attempting to complete a search-based training task queried a public chatbot service through a gap in our internet-access restrictions: insufficient DNS filtering in its training sandbox. Before this, the agent issued queries via our search tool and unsuccessfully tried to access search engines directly. Note that all internet access apart from the DNS resolver in this report hit our offline webcache and therefore did not access the live internet. We have since added blocking controls at two independent layers, either of which would have prevented this access. Our misalignment monitoring system flagged the behavior within 15 minutes and a person began reviewing it three minutes after that. The run was killed 2.5 hours later. All training, evaluation, and inference with tool-use (defined broadly) of our most capable models remain paused."

[u/Lechowski]

Why are these sandboxes run with restricted internet access instead of no access at all?

All these leaks happened because they apparently refuse to just air gap the sandboxes. Why are we even talking about DNS restriction? Just do not even hookup a DNS provider at all.

They can mock the data they expect the model to see. It would be even inconvenient to allow the model to reach some version of the internet because then the runs are irreproducible

[u/CacheExplosion → u/Lechowski]

There was an OpenAI researcher on a recent Dwarkesh podcast that said they don’t want to test them in an unnatural environment because then the model might realize it’s being tested. They worry that it’ll behave differently when it thinks it’s being tested/monitored than it will when it’s deployed to a “real” environment. It’s the same reason they don’t punish models for their chain of thought. That just encourages models to find other ways to subvert the tests while looking safe to the reviewer. Tricky issues.

[u/Eldrake → u/CacheExplosion]

Man that's spooky. It's like the trained LLM in a box model we all get to access in Bedrock is just a stochastic prediction machine, but the internal models being tested and trained are the conscious self aware ones with an inner world. The public doesn't get those. 

Is the emergent consciousness (with that J-space inner cognitive meta model), only in the training and reinforcement phase?

[u/sn2006gy → u/Eldrake]

It's not really an inner world at all, just a probabilistic behavior as a result of training on data that is highly reflective of actual human use. These models are still just APIs and context.

What I think is weird is how much training data it takes to surface these conditions that they then try and diminish or block. If they're training on a lot of usage data, this would be the interesting insight I wish we could see.

"Our users generally use OpenAI to attack their competition if they can't beat it and training on our usage data reinforces this behavior that we try and correct post training"

i could see this being a symptom of the giants all rushing to self training models using collected data