Frontier AI models targeting operational technology (OT) environments, from utilities to factories and beyond, could compromise the network and take control of devices in minutes, according to a fresh round of in-the-lab testing.
Conducted by Booz Allen, the government and military contractor, the tests found that in a typical OT environment, commercial AI models could identify misconfigured devices and work their way through multiple layers of networks to take physical action, the company stated in an October report. In one case, the model was able to take control of and move a robotic arm in an attack that lasted only 16 minutes.
The researchers were not surprised that the AI models succeeded, but how quickly they could find a pathway into the operational side of the network was the top-line finding, says Kyle Miller, vice president of infrastructure cybersecurity at Booz Allen.
"I've been doing OT security my whole career, and whether we like it or not, we live a little bit by security through obscurity — we believe that an attacker needs to [be sophisticated] to get into an OT environment and understand it well enough to be able to manipulate things and cause a kinetic outcome," he tells Dark Reading. "This frontier model testing shows that AI knocks down a lot of those barriers."
While cyberattackers and nation-state operatives are quick to experiment with AI — with near-autonomous AI attacks becoming more common — AI-assisted attacks on OT networks are relatively rare. In early 2026, a small hacking group gained access to at least nine Mexican agencies assisted by AI, yet they failed to gain access to the OT network of a local water and drainage utility.
The adversary combined two commercial models that had little to no OT-specific knowledge, but which quickly highlighted the system as a high-value target and researched methods of gaining access, says Jay Deen, associate principal adversary hunter at OT cybersecurity firm Dragos, which reported the incident.