
What happened
Vals tested AI models on simulated corporate networks; OpenAI's restricted Daybreak completed 81% of tasks, while open-weight DeepSeek averaged 73%, and on some tasks the two scored equally.
Why it matters
Closed models no longer hold a definitive edge in cyber skills, so gated access may only buy time before advanced hacking capabilities are broadly available, according to Vals.
WHO IT HITSSecurity teams at companies running their own AI models may find open-weight tools nearly as capable as restricted frontier systems for probing networks, potentially lowering the cost of both defense and attack.
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The Vals experiment lands as frontier labs, particularly Anthropic, have argued that gated access to advanced models can buy time before hacking capabilities become widely available. Anthropic's Logan Graham told WIRED in April that the company's Project Glasswing aimed to get tools to defenders for a head start, and that the world needed to prepare for broad availability in 6, 12, or 24 months. Seven months later, Vals's findings suggest that transition has arrived on schedule, with open-weight models like DeepSeek matching restricted ones on many simulated tasks.
Yet real-world observation of cybercriminal and state-backed activity has repeatedly shown attackers conserving resources and making do with older, less capable models for most of their activity. Even with strict controls, defenders must assume the best-funded attackers have already had some access to closed models. Alex Zenla of Edera cautions that closed labs likely have additional unreleased models exceeding these capabilities, framing the situation as a cat-and-mouse game in which labs race to secure vulnerabilities before open-weight models find them.
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