China’s AI Now Matches Anthropic Mythos in Cybersecurity

Source Domain: memeburn.com

Two Chinese AI systems are performing on par with Anthropic’s restricted Mythos model in cybersecurity vulnerability detection, according to independent evaluations reported by The Wall Street Journal. Zhipu AI’s open-weight GLM-5.2, released on June 13 under an MIT license, matched or exceeded leading US models on specific security tasks. Separately, 360 Security Technology unveiled an agent-based tool called Tulongfeng at the ISC.AI 2026 conference in Beijing, claiming equivalent capability through an entirely different approach.

Both systems are freely accessible or openly available. Mythos remains restricted to a small number of US-vetted partners after the Commerce Department issued an export control directive on June 12, one day before GLM-5.2 launched. The gap between what the US government tried to contain and what China has made publicly available has effectively collapsed in one narrow but consequential domain.

What the Benchmarks Show

Independent testing by cybersecurity firm Semgrep placed GLM-5.2’s IDOR vulnerability detection at an F1 score of 39%, surpassing Claude Code’s 32 to 37% on the same evaluation, according to Axios. A separate assessment by security analytics firm Graphistry found GLM-5.2 matched Claude Opus 4.8 on a capture-the-flag style security benchmark. Graphistry called it the first open-weight model suitable for what it described as a “frontier-like” cybersecurity experience.

The cost gap is equally significant. GLM-5.2 finds vulnerabilities at roughly $0.17 per bug, approximately one-sixth the cost of comparable Claude-based workflows.

These results come with important caveats. GLM-5.2 still trails Anthropic and OpenAI systems on general-purpose benchmarks. The cybersecurity parity applies to specific vulnerability detection tasks, not broad AI reasoning. Semgrep’s researchers noted their evaluation covered one dataset and one task. But in a field where incremental performance improvements determine whether a…

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