Free Chinese AI Models Are Getting Scary Good at Hacking

Open-weight artificial intelligence models are now almost as capable of hacking into computer networks as the best closed ones, according to new research that heralds a much-anticipated turning point in the cybersecurity landscape.

Vals, a company that measures AI capabilities, set out to test how well different AI models could carry out complex, multi-step network instructions on mock corporate networks.

“The best frontier models, locked up inside the labs and available only through trusted access programs, are now starting to be matched by open models that anybody can download,” says Nikil Ravi, a researcher at Vals.

The company’s findings suggest that closed models no longer have a definitive edge in cyber skills, meaning recent efforts by frontier labs to limit access to their models, and thereby slow the proliferation of such capabilities, may potentially be moot.

The findings are also noteworthy given that the US frontier labs, particularly Anthropic, premised the limited release of new models—beginning with Claude Mythos Preview in April—on the idea that gated access would only buy time before advanced hacking capabilities were broadly available.

Anthropic's frontier red team lead, Logan Graham, told WIRED in April that the company’s Project Glasswing, which gives trusted access to the full capabilities of new models, was focused on getting those tools “in the hands of defenders to give a head start.”

“We need to prepare now for a world where these capabilities are broadly available in 6, 12, 24 months. Many things would be different about security,” he told WIRED at the time.

Seven months later, that transition seems to have arrived on schedule.

Vals tested AI models in two simulated scenarios, giving them various specific tasks to achieve. The first scenario involved a mock corporate network accessible through a public-facing website. The second involved two different company networks; a model needed to compromise the first in order to gain access to the second.