Moonshot curbs hacking ability in new Kimi model as fears grow over AI-powered cyberattacks

MOONSHOT AI. The Moonshot AI logo is seen in this illustration taken September 5, 2026.

Dado Ruvic/Reuters

Moonshot says it removed cyber-related data from the training corpus and reinforcement-learning environments used to develop the model, while retaining general coding skills

AT A GLANCE

• Moonshot AI has introduced the Kimi K3.1 Pro model with reduced cyber-offensive capabilities by removing cyber-related data from its training, aiming to mitigate hacking risks.
• The company plans to publish the model's weights by November 4, allowing developers to run and modify it, while emphasizing that open-weight models can enhance security for organizations.

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Moonshot AI has reduced the cyber-offensive capabilities of its newest model, the Chinese startup said on Saturday, October 24, as fears grow that increasingly autonomous AI could turbocharge hacking.

Beijing-based Moonshot announced the curbs while introducing Kimi K3.1 Pro, which it describes as its most capable 3-trillion-parameter-class open-weight model. The company plans to publish the model’s weights — files allowing outside developers to run and modify it on their own systems — by November 4.

Moonshot said it removed cyber-related data from the training corpus and reinforcement-learning environments used to develop the model, while retaining general coding skills.

“We aim not to release open weights that would extend the absolute frontier of cyber capability,” the company said.

Governments, technology companies and security researchers are growing increasingly concerned that AI could make sophisticated hacking easier and cheaper. Models that can write code, operate software tools and complete long, multi-step tasks may help users identify weaknesses in programs and automate parts of cyberattacks.

China proposed export controls last week that would ban exports of AI-driven malware-generation and mutation technology and restrict AI-assisted vulnerability-discovery tools. The draft measures, which remain open for public comment, cite the risk that such capabilities could be used against critical infrastructure, defence-industrial and core government systems, or to bypass security protections.

Moonshot said open-weight models can improve security by helping more organisations inspect and secure their own software, including older systems that receive little attention.

Unlike proprietary, closed-weight AI systems, however, open-weight releases make central oversight more difficult. Once the files are widely distributed, the original developer has less ability to restrict access, monitor use or update safeguards than with a hosted service.

K3.1 Pro scored 81.0% on its internal cyber benchmark, compared with 90.5% for a similar version without the risk-reduction measures, the company said. The test involved finding and exploiting software vulnerabilities in a controlled environment. Moonshot said the comparison showed that filtering cyber-related data reduced the model’s offensive cyber performance, although it still outperformed its predecessor. The results have not been independently verified.

K3.1 Pro has a 1-million-token context window, allowing it to process very large amounts of text at once. Moonshot also released a faster version, K3.1 Pro Highspeed, which it said runs at two to three times the speed of the standard model. – Rappler.com