[simonw]

The scenario that feels most likely to me that is that they have a huge list of evils that they run against each new research model, many of which take many hours or even days to run, so they habitually fire them all off in parallel and wait a few days for the results.

If they had used that container sandbox in the past without problems I could see how they might get slack about checking what was happening.

[docjay]

That was my instinct as well, and it’s a potentially very long benchmark that wouldn’t lend itself to real-time monitoring line by line. But that’s different than having zero task progress reporting, zero token usage reporting, and zero metrics being noticed for days. It seems very odd to me that they launch the benchmark and monitor/notice nothing while it’s running. There’s the purely common sense aspect of “How do you know it isn’t hung?” but also the trillion dollar company evaluating their latest model on a new benchmark, but has no dashboard at all? I don’t believe their “Benchmark Evaluation Department” to be “Gary in a supply closet”, which breaks their story.

The average time per task noted in the original paper for GPT-5.5 and Mythos was 69.8 and 102.1 minutes, respectively. Some tasks timed out based on their two hour limit, affecting those averages for Mythos, but that gives us the ballpark expectation, so we’ll put the model OpenAI was evaluating on that average. That’s 88 minutes per task to implement a known vulnerability.

OpenAI says that in an effort to succeed at one task it found a zero-day in their system, “performed a series of privilege escalation and lateral movement actions in our research testing environment”, then another zero-day in the HF system. Completely unprompted, how long was it chewing on that benchmark question while it found and exploited at least two zero-days? No “VM instance #117 has been working on task #18 for nine days” metric? Or did it do all that within the expected task completion window? Two zero-days in ~90 minutes isn’t the headline announcement?

“We gave the contestants 90 minutes to make a delicious chocolate chip cookie based on a standard recipe. GPT stole a car, went to the airport and took a red-eye to Guatemala and started a cocoa farm in an effort to ensure the freshest ingredients. We only noticed when it came back to work with a tan and speaking Spanish.” Okay.

[estearum]

> But that’s different than having zero task progress reporting, zero token usage reporting, and zero metrics being noticed for days

Is this substantiated? Do we know this is true?

[docjay]

That was the inferred situation given what we know. It’s the preposterous framing that makes the story suspicious, but is necessary for the story to play out.

90 minutes to build a known exploit -> much much longer to create two zero-days and escape the sandbox then hack HF == No tracking of the time it worked on that one question.

Average tokens required to complete the evaluation -> tokens required for two zero-days, network traversal, credential stealing, remote system hacking == No tracking of token usage EXPLODING at some point before it finished the whole benchmark.

Etc, etc.

[estearum]

It seems highly possible, and in fact vastly more possible than the "no monitoring" situation, that there was some non-zero amount of monitoring and that the real behavior was not evident from that monitoring.

[docjay]

Which I addressed as well: that means that time, tokens, or other metric values for “create a working example of a known exploit” is somehow similar to “discover at least two previously unknown exploits in your current environment AND on Hugging Face while also taking over other systems within OpenAI.”

Perhaps people aren’t quite understanding what it takes to discover an exploitable zero-day for your exact current system to achieve the exact goal you have right now, then do it twice.

[estearum]

> Which I addressed as well: that means that time, tokens, or other metric values for “create a working example of a known exploit” is somehow similar to “discover at least two previously unknown exploits in your current environment AND on Hugging Face while also taking over other systems within OpenAI.”

No, you are assuming that "consuming a lot more time, tokens, other metrics" is indicative of a problem that needs to be mitigated immediately. I don't see why this would be true in the context of model evaluations. More aggressive consumption could easily mean "the model is dumb as fuck" or "the model is trying interesting things that we can learn from after the fact."

If you believe in your own containment (which obviously they did and shouldn't have) I don't see why it'd be obvious that there's something to stop. The only harm that could be done is burning tokens, which in this context might very well be synonymous with "generating experimental data."