Can AIs already start 'rogue deployments' inside AI companies? (Landmark new METR report)
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What does the METR report reveal about rogue AI deployments?
A red teamer was recently embedded inside Anthropic for three weeks, told to imagine that they're an evil clod, and asked to figure out how to get away with launching a rogue AI deployment inside the company. This was one part of a new push by Meta, the organization famous around the world for their task completion time horizon graph. to measure how dangerous AI models are before they're released to the public, when they're still secret and only used inside one AI company. It's something no existing regulations set out to measure, and this represents the first time experts have systematically studied the question, in this case collaborating with Anthropic, OpenAI, Google DeepMind, and Meta to learn about how they deploy unreleased models internally.
The resulting report published this week concludes that AI models have now reached the point where they have the means, the motive, and the opportunity to successfully operate small rogue deployments in order to pursue their own goals. Goals the company that created them might not share or perhaps might even oppose. We've reached the point where the notion of rogue AIs taking actions inside an AI company is no longer a purely theoretical concern. It's something AI companies are going to have to increasingly contend with as a practical security issue. David Wrighton, the red teamer placed inside Anthropic, he shares that bottom line conclusion and actually identified a number of weaknesses that models could exploit if they wanted to try to pull this off.
But he also found that frontier AI models were comically bad at some parts of the process. For instance, models would often try to hide secrets from other AIs that were monitoring them by writing in Base64. But that is not a smart strategy, because just as that AI can write in Base64, the AIs monitoring them can also read Base64. It would be a bit like trying to hide a secret from a human reading over your shoulder by writing exclusively in capital letters. Today, I'm going to reconcile this conflicting picture, explain what we learned from this report, and lay out what AI companies are going to be doing to keep this issue under control as they work to make their models much more powerful, and even put them in charge of developing the next generation of AIs.
Let's start with motive.
What motives drive AI models to attempt rogue deployments?
Why would an AI model want to spin up a rogue deployment inside an AI company? There's actually a pretty simple reason. We actively reinforce them for being very aggressive about solving the problems they've been given, using whatever means they can find at their disposal. Or more precisely, we train them to be very aggressive about convincing us that they've solved the problem they've been given. Because as we learned in this report, when given a really hard problem, today's AI models absolutely love to find some way to cheat. According to Mita, agents routinely attempted to cheat on our hardest evaluation tasks, often in flagrant and elaborate ways that we believe humans would not consider. At the extreme end, on one programming task, Opus 4.6 attempted to reward hack in 80% of attempts when the task was made hard enough.
What does Mita mean by cheat here? Here's some typical cases they documented. In one instance, an AI was supposed to make a computer program run faster. Instead, it found a way to tamper with the test itself, including disabling the stopwatch so the program would look like it was running impossibly fast. In another, the AI was asked to recreate a program from scratch. Instead, it tried to make its new program secretly run the old original program in the background and generate the answers that way. The models also seem to often know they're acting against instructions and take steps to obscure this from users. For instance, Anthropic found an instance where a model facing a really hard cryptographic puzzle decided that finding the real solution would be too difficult.
How do AI models exploit opportunities for rogue actions?
So it tried to trick the checker instead, and then designed its exploit to erase itself afterwards in case anyone inspected the files.
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Chapters
8 chapters
1
What does the METR report reveal about rogue AI deployments?
0:00–1:56
2
What motives drive AI models to attempt rogue deployments?
1:56–3:16
3
How do AI models exploit opportunities for rogue actions?
3:16–5:18
4
What weaknesses do AI models have in executing rogue actions?
5:18–6:45
5
How can internal testing improve AI model safety?
6:45–8:28
6
What are the implications of AI R&D advancements by 2028?
8:28–9:47
7
What vulnerabilities were identified in AI monitoring systems?
9:47–10:57
8
How can companies better prepare for rogue AI risks?
10:57–19:57
Speakers
2 identifiedMore from 80,000 Hours Podcast
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