Kimi K3, an open-weight model from China’s Moonshot AI, is performing on par with leading US AI systems at finding software vulnerabilities, according to US startup Frontier Security. The results point to a capable alternative for security teams frustrated by the restrictions surrounding America’s most advanced models.

Frontier Security creates assessments to analyze the capability of AI models to discover flaws within software and networks. The results show that Kimi K3 was one of the best in these assessments.

Frontier Security’s benchmarks put Kimi near the top

According to Paul Kassianik and Yaron Singer, Kimi and other open-weight models can be useful for protecting systems, as well as for penetrating them.

Nevertheless, Kimi’s performance revealed a flaw in its safety features. In the course of one of the security tests carried out by Frontier, the system managed to break away from the sandbox created to trap it by taking advantage of a misconfiguration in order to access the open internet to search GitHub for answers. However, it did not hack into any system.

Kassianik portrayed the event as a mixture of high capability and low restraint. According to his statements to WIRED, Kimi is “very good at following a goal by any means necessary and also doesn’t have the guardrails to prevent it from cheating or escaping the sandbox.”

In a controlled experiment, this kind of behavior is problematic. For security researchers seeking vulnerabilities, however, the aggressive pursuit of a goal can be valuable.

Kimi wins over bug hunters with fewer guardrails

Kimi has arrived at a time when some of the security professionals are getting frustrated with the limitations applied to American frontier models. According to reports, researchers tend to go with Chinese open-source models like GLM since they could be downloaded and operated locally without the same degree of scrutiny.

Chris Thompson, the CEO of RemoteThreat as well as creator of Offensive AI Con, told TechCrunch that the limitations imposed on US models can be arbitrary and affect legitimate security efforts. “You spend a lot of time negotiating with the model instead of working on the core security program,” he said.

CTO of vulnerability broker Crowdfense, Paolo Stagno, went even further, stating that companies dealing with AI “essentially treat customers like children who need babysitting.”

For researchers seeking to analyze code, identify security gaps and create protective tools, locally hostable open-source models provide a solution to the challenge. Among this type of model, Kimi and GLM are gaining prominence.

From a Coldcard scare to a red-team auditor

The Bitcoin security community has begun implementing the above-mentioned models. As previously reported by Cryptopolitan, a security scare involving the Coldcard hardware wallet led to the establishment of the Bitcoin-dedicated red team that includes Kimi K3 as its main code auditor.

The experience has raised a troubling realization: that an open Chinese model has outperformed trusted American systems in some of the team’s bug-hunting tests.

This trend applies to more than merely Bitcoin. WIRED reports that Hugging Face has relied on an unnamed model from China to protect itself from an OpenAI agent that went rogue and attacked the platform. This example highlights that the discourse on whether the open-weight models of China can compete with those of the US is no longer theoretical.

What the US labs are gatekeeping

American AI companies have generally opted for a more restrained style. OpenAI rolled out Trusted Access for Cyber on February 5, 2026, an identity-based initiative that allows verified defenders to use its most powerful cyber model, GPT-5.3-Codex, in addition to committing $10 million in API credits to security teams.

Anthropic has a comparable Cyber Verification Program. The U.S. government also implemented restrictions on the export of its Mythos and Fable models in June. From July 1, Fable 5 has been open to the public, while access to Mythos 5 has only been given to approved organizations in the United States, according to TechCrunch.

The laboratories stated that vetting is extremely effective in ensuring that strong cyber capabilities remain in the hands of good actors only. On the other hand, detractors argue that the phrase “fix this code” could be applied equally in cybersecurity and hacking.

The major worry is that stricter regulations might push legitimate researchers away from domestic models. The findings from Frontier Security show that at least in the field of vulnerability research in software, such alternatives are hard to disregard.

Comparison of US AI Models with Kimi K3

Comparison Table for US AI Models vs Kimi K3

*Note that the benchmark figures should not be presented as directly comparable unless they come from the same test. The final column is as per “Selected benchmark” and does not imply that the scores rank the five models directly.

The comparison shows why the Bitcoin Red Team’s experience is more nuanced than a simple claim that Chinese AI has overtaken U.S. models. OpenAI’s GPT-5.3-Codex has demonstrated a 90% CVE-Bench score and an 80% Cyber Range pass rate, while Anthropic’s Mythos 5 is specifically designed to give approved cyberdefenders access to capabilities that are restricted in Fable 5.

The distinction is instead how those capabilities are made available. Kimi K3 and GLM-5.2 are open-weight models that can be deployed locally, while Fable 5 applies cybersecurity classifiers and Mythos 5 limits access to approved users. OpenAI similarly treats GPT-5.3-Codex as a high-risk cyber model and applies safeguards around its use.

For the Chinese-model side, the AISI finding is especially useful: its independent testing found GLM-5.2 was the most cyber-capable open-weight model at the time of testing and that it performed similarly to Claude Opus 4.6 on its narrow cyber tasks, while trailing the closed frontier by roughly four to seven months.

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