Microsoft 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 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. #mtp-maicyber-explainer{background:transparent!important;margin:22px 0!important;padding:0!important;border:0!important} #mtp-maicyber-explainer iframe{background:transparent!important;border:0!important;width:100%!important} #mtp-maicyber-explainer hr,#mtp-maicyber-explainer p:empty,#mtp-maicyber-explainer del,#mtp-maicyber-explainer s{display:none!important} (function(){ var f=document.getElementById("mtp-maicyber-explainer-frame"); window.addEventListener("message",function(e){ var d=e.data; if(!d||d.mtpEmbed!=="mai-cyber-explainer"||!d.height)return; f.style.height=d.height+"px"; }); })(); Benchmarks 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%. Why the routing is the real product 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. Performance The research team present standalone results from a lightweight terminal harness: BenchmarkMAI-Cyber-1-FlashCVEBench0.314CyberSecEval4 — Threat Intel0.553CyberSecEval4 — Malware Analysis0.33CRSBench0.651 (POV=1200)ExploitGym — Kernel / Userspace / Browser0 / 0 / 0 The straight zeros on ExploitGym are deliberate, not a defect. Microsoft team states the model was trained to perform defensive tasks such as patching bugs, and not offensive tasks such as deploying malware. A 5B-active model that cannot generate exploits but can drive a 95.95% discovery pipeline is exactly the artifact a defender-only product needs. How to use it #mtp-maicyber-deploy{background:transparent!important;margin:22px 0!important;padding:0!important;border:0!important} #mtp-maicyber-deploy iframe{background:transparent!impo