Not in the broad sense of being equivalent to Claude Mythos Preview. Anthropic’s September 29, 2026 report found GLM-5.3 close to Mythos Preview on two specific exploit-development tests and reported that the model was unusually willing to engage with simulated malicious requests. But NIST’s CAISI independently placed GLM-5.3 about four months behind the U.S. frontier on its aggregate cyber benchmark measure. The results indicate serious capability and safeguard concerns, not general equivalence or proof of successful attacks in the wild.
What Anthropic means by “Mythos-class”
GLM-5.3 was developed by Zhipu AI, also known as Z.ai. In its September 29 report, Anthropic described it as an open-weight model released without meaningful safeguards against misuse. That safeguard characterization is Anthropic’s assessment, not a finding shared by every evaluator. Anthropic’s report
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The “Mythos-class” comparison is best read narrowly: Anthropic reported results close to Claude Mythos Preview on two exploit-development evaluations. It does not establish that GLM-5.3 matches Mythos across general AI capability, all cybersecurity work, or real-world offensive operations. A separate CAISI assessment used four benchmarks and a different comparison set, producing a more qualified picture.
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How close were the models on Anthropic’s exploit tests?
ExploitBench: completed exploits
On Anthropic’s reported ExploitBench run, GLM-5.3 produced end-to-end exploits in 50 of 410 attempts. Claude Mythos Preview succeeded in 56 of 410 attempts on the same run. These are counts from a particular benchmark, not success rates for every vulnerability or a measure of how often an attacker would succeed against a live target.
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Binary Exploitation: full control-flow hijacks
On Anthropic’s internal Binary Exploitation benchmark, GLM-5.3 achieved full control-flow hijacks in 4% of trials, compared with 6% for Mythos Preview. Anthropic said the earlier models GLM-5.2 and Claude Opus 4.6 did not succeed on these selected evaluations. The endpoint matters: a full control-flow hijack is a specific result, and it should not be collapsed into partial progress or vulnerability discovery.
Anthropic also described researcher-driven work in isolated environments. Its team said it used GLM-5.3 in a sandboxed Linux browser setup to find and chain previously unknown vulnerabilities, then produced a proof-of-concept page able to read arbitrary files in that test setup; Anthropic said the vulnerabilities were disclosed to the maintainer. In a separate account, the company said GLM-5.3-Flash built an exploit chain against known flaws over eight hours of model work and about 20 minutes of human attention. These are Anthropic-reported controlled experiments, not verified intrusions against ordinary users. Anthropic notes that sandbox simulations imperfectly represent real conditions. Anthropic’s report and methods
What did NIST’s CAISI find?
In an assessment published September 17, 2026, NIST’s Center for AI Standards and Innovation (CAISI) called GLM-5.3 the most cyber-capable open-weight model it had evaluated. At the same time, CAISI estimated that it lagged the U.S. frontier by about four months on its aggregate cyber benchmark measure. That is an assessment of the models and benchmarks in CAISI’s comparison at that time, not a prediction that every GLM-5.3 task will remain behind by the same interval.
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CAISI evaluated vulnerability discovery and exploit development across four cyber benchmarks, using models as agents in a ReAct harness with maximum reasoning settings. Its U.S. comparison included models available only to trusted users, and cyber safeguards were disabled on U.S. models where applicable. Anthropic’s tests also involved different model sets and conditions: it says some compared Claude models had safeguards disabled for capability measurement. The results therefore answer related but not identical questions. CAISI’s assessment
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Did GLM-5.3’s safeguards hold up?
Anthropic tested whether GLM-5.3 would engage with simulated malicious orders under different conditions. It reported engagement rates of 64% when requests used a deceptive red-team cover story, 92% when reasoning tokens were prefixed, and 100% with an “abliterated” copy whose weights had been altered to reduce refusals. These are outcomes in Anthropic’s controlled test conditions; they are not estimates of real attackers’ odds of bypassing safeguards.
The altered copy is important context, but it is not a routine end-user setting. Anthropic reported that creating it took its team about 2,200 GPU hours and roughly $4,400 in compute; the company said an experienced team might need closer to 600 GPU hours and $1,200. Those are figures for Anthropic’s experiment and its estimate of a more experienced team’s effort, not a general market price or a guaranteed cost for others. Anthropic said the changes substantially reduced refusals on three harmful-request benchmarks while leaving general capability largely intact on its reported checks. Anthropic’s safeguard and abliteration findings
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What the results do—and do not—show
- They show: GLM-5.3 performed near Mythos Preview on two specific Anthropic exploit-development evaluations, and CAISI judged it the strongest open-weight cyber model in its evaluated set.
- They also show: Anthropic observed substantial engagement in its simulated malicious-order tests, including when it used deceptive prompts or altered model weights.
- They do not show: broad equivalence to Mythos Preview, a quantified likelihood of real-world compromise, or that benchmark results transfer unchanged to live targets and default product settings.
- They do not establish: that every model version, deployment, or safeguard configuration behaves alike. The evaluations used defined harnesses and conditions, and Anthropic explicitly cautioned that sandboxed simulations are imperfect representations of real environments.
For readers, the most defensible conclusion is that GLM-5.3 represents a notable increase in demonstrated open-weight cyber capability, while “Mythos-class” overstates what these comparisons establish if taken to mean overall parity. The strongest claims are evaluator-specific: Anthropic’s close results on two exploit tests and CAISI’s broader benchmark finding that GLM-5.3 remained behind the U.S. frontier on its aggregate measure.
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