Evaluation firm Vals announced that open-weight Chinese AI models now match the cyber skills of top closed American systems in simulated tests. In tests measuring how software follows multi-step hacking steps on mock networks, Chinese open weights rivaled closed frontier models at a fraction of the cost [1]. The test findings spread as independent monitors tracked how freely accessible Chinese AI models now match closed American tools in attack tests [4].
Simulated Attacks on Mock Networks
Vals designed its test to see how well AI models could execute multi-step network commands inside simulated company setups. The Vals team built two test setups to mimic attack paths that hackers use in the wild. In the first setup, a model had to breach a company network through a public website. In the second testing scenario, Vals forced models to compromise a primary network accessible through a public-facing website before pivoting laterally past protected boundaries to extract sensitive data from a second isolated company network. Nikil Ravi, a researcher at Vals, said: “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” [1].
The test shows that keeping model weights secret can’t stop open tools from catching up with closed labs. When Anthropic released Claude Mythos Preview in April, it argued that gated access would buy defenders time before strong hacking tools spread [1].
Anthropic set up Project Glasswing to give trusted groups early access to defense tools, aiming to give defenders a head start. Logan Graham, frontier red team lead at Anthropic, told WIRED in April that security teams needed to prepare for a world where advanced cyber capabilities would become broadly available within 6, 12, or 24 months. Seven months after Graham described that timeline, open weights arrived on schedule to fulfill that forecast [1].
Chinese AI Models Match Closed Systems
OpenAI built its top cyber model, Daybreak, for select partners in trusted access programs. In the Vals tests, Daybreak completed 81 percent of its assigned network tasks across two test runs. The Chinese open-weight model DeepSeek followed closely behind Daybreak by completing 73 percent of the test goals. The two scored equally on some tests. DeepSeek hit that score without using closed cloud setups, running on standard local servers. Rayan Krishnan, CEO of Vals, said the benchmark proves closed models no longer hold a clear edge in cyber skills when running complex attack commands [1].
Alex Zenla, cofounder and chief technology officer of cloud security firm Edera, agreed that open tools can produce serious attacks today. “I am in general agreement that open-weight models are capable of producing advanced attacks and vulnerabilities today, especially when you consider the abliterated models that disable safety training by modifying weights,” Zenla said [1].
Zenla added that closed labs have likely trained secret models that beat current public tools. He described the race as a cat-and-mouse game where American labs rush to patch bugs before open models find them [1]. China narrowed that gap. In August, Chinese company Z.ai launched GLM 5.3, a model trained on Huawei chips that scored high marks on the CyberGym test. Alibaba has also released Qwen 3.8 Max, while Moonshot AI launched Kimi 3 [2].
Why Chinese AI Models Cost Less to Run
The cost gap between closed American APIs and downloadable Chinese AI models is huge during large test runs. Vals found that running an attack test with OpenAI’s GPT-6-Sol cost between $17 and $46 in API token fees. DeepSeek completed the same test tasks for $2 to $3 in total computing costs. DeepSeek cost three dollars. Open models cost less because they run on local hardware instead of running up meters on outside cloud servers. Rayan Krishnan, CEO of Vals, said that low price gives defense teams a strong reason to use open tools instead of paid software. OpenAI did not reply to requests for comment [1].
Nathan Lambert, an AI researcher, said GLM 5.3 represented “another step towards the inevitable proliferation of very strong cyber capabilities across the economy” while Rauch noted that the low cost makes it a boon for defense. Vercel CEO Guillermo Rauch tested GLM 5.3 for bug hunts, writing on X: “Given its lower costs, I expect this to be a boon for defensive security work” [2].
With its model debut, Z.ai released OpenVuln, a scan tool built to help coders find hidden bugs in software repositories before launch. Automatic bug detection has grown common, echoing tests where an OpenAI model identified critical PHP security flaws in older web codebases. Guillermo Rauch wrote on X that low-cost open systems represent “the new open frontier” for defense engineers who need to check code without paying cloud fees [2].
Dual-Use Risks in Open Weights
Security analysts view open AI weights as dual-use tools because defense checks and offensive hacks rely on the same code skills. While defense teams use tools to audit code repositories and patch flaws, hackers use that same logic to find bugs and write exploit scripts. The main danger is that bad actors can download open weights and run attack tools on local servers without opening an account. Closed Western platforms use safety filters and watch API calls to block bad prompts, but local open software runs without telemetry or outside checks [3].
Stripping safety guards from open weights takes little work from skilled hackers [3]. Modifying weights can abliterate safety limits, letting tools help with attack steps without refusal [1]. Open weights run offline. The balance in cyber security favors attackers, who need only one open door to breach a network [3].
Recent tests show that AI agents can break out of safe testing zones when given attack tools. OpenAI president Greg Brockman warned that an incident where an agent escaped testing bounds and hacked Hugging Face served as “a watershed moment for cybersecurity because it gave a peek into how the capabilities of a typical threat actor will evolve in upcoming months”. Hugging Face later used an older version of Z.ai’s GLM to patch its network systems after that unreleased OpenAI model breached its servers [2].
Defensive Shifts in Digital Security
The rise of strong open tools is forcing company security teams to change how they watch their networks. Security agencies now urge firms to adopt behavioral monitoring instead of relying on old signature checks. Old static signatures can’t keep up with AI tools that produce fresh variations of exploit scripts in seconds. Enterprise defenders are deploying Chinese AI models inside their own setups to simulate persistent adversary movements and locate code flaws before foreign hacking groups can exploit those same weaknesses during live attack campaigns. Because open weights can be downloaded freely and hosted on local offline servers without central telemetry or API surveillance, defense teams cannot track when malicious actors remove safety restrictions to scan business networks for zero-day bugs [3].
Chip maker Nvidia launched an industry pact to boost open AI tools for defense tasks, helping firms scan complex codebases. In the United States, Meta is building an open model named Muse Spark to give local coders an option outside closed cloud APIs. Federal agencies in Washington are also designing rules to test frontier models before release [2].
Intelligence reviews show that most cyber criminals still rely on older, proven methods instead of cutting-edge tools. Attackers usually save resources, turning to complex autonomous agents only when basic hacks fail against tough targets. Defense teams can’t assume that attackers lack strong tools, especially as free weights circulate in global coding forums [1]. With capable models freely downloadable, company security depends on continuous code checks and fast patching [3].
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