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Microsoft's MAI-Cyber-1-Flash demonstrates that specialized sparse MoE architectures can significantly outperform general frontier models when integrated into agentic workflows for automated vulnerability reproduction. By routing 90% of tasks to this cost-efficient model and reserving GPT-5.4 only for the most complex cases, MDASH achieves a substantial reduction in inference costs while pushing CyberGym scores above 95%. This release highlights a critical shift toward domain-specific fine-tuning within multi-agent systems rather than relying solely on monolithic general-purpose models for security operations.

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Microsoft AI Releases MAI-Cyber-1-Flash: A 5B-Active-Parameter Cyber Model That Pushes MDASH to 95.95% on CyberGym

MarkTechPostJuly 28, 20263 min read

Microsoft AI has released MAI-Cyber-1-Flash, its first model built specifically for cyber defense. It is a 137B total, 5B active sparse MoE fine-tune of MAI-Code-1-Flash with a 256k context window. The model does not ship as a standalone endpoint — it runs inside MDASH, Microsoft's multi-model agentic scanning harness, where it handles up to 90% of tasks and pushes the system to 95.95% on CyberGym. The post Microsoft AI Releases MAI-Cyber-1-Flash: A 5B-Active-Parameter Cyber Model That Pushes MDASH to 95.95% on CyberGym appeared first on MarkTechPost.

Microsoft AI has released MAI-Cyber-1-Flash, its first model built specifically for cyber defense. The model does not ship as a standalone endpoint. It runs inside MDASH, Microsoft’s multi-model agentic scanning harness. MAI-Cyber-1-Flash is a transformer with self-attention and sparse Mixture-of-Experts layers. It carries 137B total parameters with 5B active, and a 256k context length. Inputs and…

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