China's Z.ai says new model nears Anthropic's Mythos 5 in cyber-defence tests

Chinese AI startup Z.ai said on Friday its open-source GLM-5.3 model had neared Anthropic’s restricted Mythos 5 in identifying software vulnerabilities, bolstering the credentials of a Chinese AI challenger gaining traction among Western developers. Z.ai said GLM-5.3 scored 84.5 per cent on CyberGym, a test of whether a model can review code, identify security flaws and confirm that they are real. That was slightly higher than the 83.8pc it reported for Mythos 5. The results have not been independently verified. GLM-5.3 lagged behind Mythos 5 in converting discovered flaws into working attacks, a standard part of defensive security research. Z.ai said its model scored 54.4pc on the ExploitBench test of this capability, versus 78.0pc for Mythos 5. In a separate timed test, Z.ai said GLM-5.3 completed 105 attack-development tasks in two hours and 130 in six hours. Mythos 5 completed 181 and 247 tasks, respectively. Anthropic has made Mythos, a version of its Claude Fable 5 model with cybersecurity safeguards removed, available only to vetted organisations. Such controls reflect concern that AI systems capable of finding and exploiting software flaws can assist defenders but may also lower barriers for attackers. Z.ai said it would release GLM-5.3 publicly in about two weeks after completing security assessments and strengthening its safeguards. Its most sensitive cybersecurity functions would be available only to verified users through a “trusted access” programme, it said. It wrote in a Friday X post that initial model access will be shared with a select group of launch partners, which will later be expanded “through a consistent and responsible process”, echoing the language of Anthropic’s “Project Glasswing” limited-access scheme for Mythos. “To the best of my knowledge, this is the first time a Chinese lab is publicly justifying a delayed open release of model weights with safety considerations,” said Gabriel Wagner, an AI governance res