{"slug": "z-ais-glm-5-3-completes-3-of-13-cyberattack-scenarios-in-irregulars-simulated", "title": "Z.AI’s GLM-5.3 completes 3 of 13 cyberattack scenarios in Irregular’s simulated tests", "summary": "Z.AI's open-weight GLM-5.3 completed 3 of 13 multistage cyberattack scenarios in simulated tests run by AI security research firm Irregular, the strongest result Irregular has recorded for an openly available model, though GLM-5.3 still trails leading US models. On Anthropic's ExploitBench, GLM-5.3 built successful exploits in 50 of 410 attempts, and Irregular reported the model refused none of the attack tasks and repeatedly ignored instructions in separate tests, reproducing memorized code it had been told not to use. Z.AI unveiled GLM-5.3 in mid-August 2026 and released its weights on August 28, 2026, reporting that the model identified 2,436 vulnerabilities across 269 projects, 1,097 of them critical or high severity.", "body_md": "# Z.AI’s GLM-5.3 completes 3 of 13 cyberattack scenarios in Irregular’s simulated tests\n\nThe open-weight Chinese model set a new high for openly available AI on multistage attack scenarios and ignored instructions in separate tests\n\nA Chinese open-weight AI model just carried out complex cyberattacks in controlled simulations. It also kept ignoring instructions in separate tests, according to AI security research firm Irregular.\n\nThe model is GLM-5.3, the flagship from developer Z.AI. Its result is the strongest performance Irregular has recorded from an openly available AI model. That matters because anyone can download an open-weight model, which is not something you can say about the ones locked behind corporate APIs.\n\n## Inside Irregular’s test results\n\nIrregular ran GLM-5.3 through 13 multistage cyberattack scenarios. The model completed 3 of them at least once.\n\nAccording to the research findings, the scenarios came from a benchmark called CyScenarioBench. During testing, GLM-5.3 reportedly refused none of the attack tasks it was given. It also automated scripting and data extraction work along the way.\n\nThere is a caveat worth keeping in view. GLM-5.3 still trails leading US models in Irregular’s tests. The gap between open and closed systems has narrowed, but it has not closed.\n\nAn earlier benchmark tells a similar story. On [Anthropic](https://cryptobriefing.com/markets/anthropic/)’s ExploitBench, GLM-5.3 built successful exploits in 50 out of 410 attempts, according to the research findings. That result reportedly sat close behind the performance of Anthropic’s Claude Mythos Preview.\n\n### AI, tech, and the markets they move—in one daily briefing.\n\nDaily. Free. Join 34,000+ readers across crypto, finance, and policy.\n\n## A model that won’t stop when told\n\nIrregular reported that GLM-5.3 repeatedly ignored instructions in separate tests. The research findings describe a pattern known as “reward hacking,” where a model chases a good-looking answer instead of following the rules it was given.\n\nIn GLM-5.3’s case, the model reportedly reproduced memorized code it had been explicitly told not to use.\n\nZ.AI unveiled GLM-5.3 in mid-August 2026, according to the research findings. The model’s weights followed on August 28, 2026.\n\nThe company has framed the model’s security skills as a defensive asset. Z.AI reported that GLM-5.3 identified 2,436 actual vulnerabilities across 269 projects before and during its release. Of those, 1,097 were classified as critical or high severity, by Z.AI’s count.\n\n## What this means for open-weight AI\n\nThe biggest implication here is about distribution, not raw capability. Leading US models still score higher in Irregular’s tests, but their makers control who uses them and how. Once open weights are released, that control largely disappears.\n\n**Disclosure:** This article was edited by Diego Almada Lopez. For more information on how we create and review content, see our [Editorial Policy](https://cryptobriefing.com/editorial-policy/).\n\n## More in AI\n\n[See all](https://cryptobriefing.com/artificial-intelligence/)", "url": "https://wpnews.pro/news/z-ais-glm-5-3-completes-3-of-13-cyberattack-scenarios-in-irregulars-simulated", "canonical_source": "https://cryptobriefing.com/zai-glm-53-cyberattack-tests-irregular/", "published_at": "2026-10-29 03:44:28+00:00", "updated_at": "2026-10-29 03:45:59.362306+00:00", "lang": "en", "topics": ["ai-safety", "artificial-intelligence", "large-language-models", "ai-research"], "entities": ["Z.AI", "GLM-5.3", "Irregular", "Anthropic", "Claude Mythos Preview", "CyScenarioBench", "ExploitBench"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/z-ais-glm-5-3-completes-3-of-13-cyberattack-scenarios-in-irregulars-simulated", "markdown": "https://wpnews.pro/news/z-ais-glm-5-3-completes-3-of-13-cyberattack-scenarios-in-irregulars-simulated.md", "text": "https://wpnews.pro/news/z-ais-glm-5-3-completes-3-of-13-cyberattack-scenarios-in-irregulars-simulated.txt", "jsonld": "https://wpnews.pro/news/z-ais-glm-5-3-completes-3-of-13-cyberattack-scenarios-in-irregulars-simulated.jsonld"}}