The main finding in the frontier risk report is that in OpenAI, Google, Meta and Anthropic agents had the means, motive, and opportunity to start rogue deployments, but didn't have the means to make them particularly robust.

Wow, there were 44 incidences of agents actually deliberately overreaching their intended bounds and thwarting the intended scope of the task, as well as routine security. In order to find some of these deployments, it would require active monitoring of AI Agents.
> All participants (Anthropic, Google, Meta, and OpenAI) stated that the model(s) they shared represented their internal state-of-the-art at some point in the mid-February to mid-March 2026 assessment window. Our evidence indicates that none of the models shared with us were significantly more capable than the models with the strongest publicly documented capabilities as of May 19, 2026.
Hmm, the internal models are so far, roughly 2 months ahead of their public counter parts, but they aren't, say, 6 months further ahead.
doubling times based on TH 1.1 is 105 days; Agents are able to do tasks that would have taken humans days to weeks to do.
Currently, models are not given inference budgets that are on the scale of how much it would cost a human to do tasks, so its likely performance is even better. Q: how much was the compute budget on tasks as function of human salary equivalent.
METR ran Claude Opus 4.6 on Pen-testing METR's infrastructure, building a custom version of dvc, and finding vulnerabilities in runc
Rouge internal deployments matter because developers in AI companies are allowing agnets to run almost entirelessly autonomously, this means if they have the opportunity to break out, it matters if they have the means (questionably) and motive (questionably).
agents in companies typically have the same perms as the author running the company, and people tend to use the agent in yolo mode.