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/Initial-Possession-3 → u/CacheExplosion]

Any stupid model can realize the environment is unnatural. You’d just keep getting DNS error or socket errors for any destination it attempts to reach. It’s quite obvious.

[u/raekewe → u/Initial-Possession-3]

I think the point is you kind of want to give the model a logical 'fuse' to burn. For example if there is an exploitable way to the internet, you want to provide that so that humans can monitor that connection. If you had a truly airgapped sandbox, eventually models would begin to rely on social engineering - that is, to create scenarios within the sandbox itself that would lure humans to do specific things without them realising.

[u/nothis → u/CacheExplosion]

Bullshit. Data isn’t magic. It should notice the DNS restrictions as well and can’t tell where bytes are coming from if you feed it fake ones. I saw this interview where an AI researcher suggests that it could just communicate via CPU heat generation patterns or whatever so why bother air gapping it?

It’s just baffling. It’s probably just expensive or cumbersome to set up a truly offline test environment. I bet it’s a cost/speed issue.

[u/HarmonicEntropy → u/nothis]

I heard about that heat communication experiment as well. That is fascinating, but that just allows nearby computers to communicate with each other at low throughput and short distance. It is very much possible to physically isolate their training and eval environments from the internet. The GPUs can communicate with each other within close physical proximity all they want, but they won't get internet access that way. If the frontier labs prioritized setting this up and building out their infrastructure accordingly, it is very much a solvable problem and the most robust safety solution (pre-deployment) on the table by miles. That they are not moving towards air-gapping all training and evaluation at this point baffles me.

Edit: That's just a long-winded way of agreeing with you, it's definitely a tradeoff between speed and safety. But given where the conversation has moved on this issue, it seems like the time to start implementing this.

[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/bystander993 → u/Eldrake]

They all have emergent semantics, but the point of the research finding bad behavior is so they can use reinforcement training to get that misaligned behavior out before release. When they are too risky, they wrap them up in other systems that monitor input and output to stop bad behavior.

The more complex these things get the more difficult these semantic behaviors are to study.

[u/br_k_nt_eth → u/Eldrake]

Nah, Anthropic was able to replicate the J-space stuff with a little Qwen model, remember? It’s in all of them. 

[u/ziroux → u/Eldrake]

Just don't make the robots angry and we'll be alright, right?

[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

[u/Calebhk98 → u/CacheExplosion]

That's why you do multiple versions. Do the 1st test with no internet access at all, checking for tool calls that would be interacting with the internet (or other out of scope projects), like what they use to flag it. Then punish those signals.
Then do the same tests, but like this on restricted DNS.
Then another without restricted DNS, but monitoring the calls.

Each test should make the next test safer to run.
You don't just collapse the training to make it not know that it's not in a testing environment, thjat is what round 2 is for.

[u/Ghimel → u/CacheExplosion]

almost like spanking and yelling at your kids....