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

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

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 outputs are text only.

It is a cybersecurity-specialized fine-tune of MAI-Code-1-Flash , the lightweight agentic coding model already embedded in GitHub Copilot and VS Code. The release describes it as derived from the MAI-Thinking-1 lineage.

CyberGym is a public suite of 1,507 real-world vulnerability reproduction tasks drawn from 188 OSS-Fuzz projects. Microsoft evaluated at CyberGym’s default level 1 configuration, which supplies vulnerable source and a high-level description.

MDASH running MAI-Cyber-1-Flash alongside GPT-5.4 scores 95.95% . Microsoft frames this as roughly 12 points above Anthropic’s Mythos, and the launch chart places the four competing systems between 83.2% and 85.6%.

When Microsoft first detailed MDASH in May 2026, the harness scored 88.45% on CyberGym using only generally available models. That was already the top public leaderboard score, about five points ahead of the next entry at 83.1%. The research team states the improvement plainly: replacing 80% of the existing models in MDASH moved the harness from 88.4% to 95.95%.

MDASH manages over 100 specialized agents through five stages: Prepare, Scan, Validate, Dedupe, and Prove. Auditor agents flag findings, debater agents argue exploitability (using disagreement as signal), and the Prove stage executes triggering inputs with ASan for C/C++ targets.

To control frontier model costs at scale, MAI-Cyber-1-Flash handles up to 90% of MDASH tasks , escalating the hardest 10% to GPT-5.4. This routing yields a 50% cost saving over the previous configuration of GPT-5.4, 5.4 mini, and 5.3 codex.

MDASH was developed by Microsoft’s Autonomous Code Security (ACS) team, featuring members from the DARPA AI Cyber Challenge-winning Team Atlanta. In May, MDASH-assisted work generated 16 CVEs (including four Critical remote code execution flaws) in the Windows networking and authentication stack. Retrospectively, it recovered 96% of 28 MSRC cases in clfs.sys and 100% of 7 cases in tcpip.sys over a five-year window.