Microsoft reports that its FORGE Lab helped discover Windows vulnerabilities assigned 140 CVEs from May through September 2026 and submitted 155 internally validated reports across 23 open-source projects. It distinguishes those reports from the 93 with documented maintainer acknowledgement or acceptance, and describes verification and remediation capacity as constraints on useful discovery. Its validation-cost figures cover successful selected cases and exclude other pipeline and human costs.
Why it matters
It connects agent-assisted discovery to operational validation and servicing, while keeping candidate volume separate from confirmed findings and shipped fixes.
Key facts
As stated in the sources, with where to find them.
- FORGE reports 140 Windows CVEs from May through September 2026, including 52 addressed in September’s security release. Monthly cohorts reflect announcement and servicing, not discovery dates or scan throughput.Introduction; CVEs per cohort figure
- The open-source snapshot contains 155 internally validated reports across 23 projects, with 93 across 14 projects or project families having documented maintainer acknowledgement or acceptance. The latter category is not a count of patched vulnerabilities.Open-source vulnerability reporting snapshot
- For 182 confirmed Linux-kernel crash findings, generating supporting proof of concept averaged $3.61 in model cost and 21.5 minutes. Six selected privilege-escalation validation cases averaged $8.56 and 25.4 minutes, using GPT-5.5. Microsoft says the model and harness had no extensive task-specific tuning.Measured costs of automated validation
- Cost averages include attempts associated with the successful cases shown but exclude initial screening, failed or filtered candidates, human investigation and patch preparation. Microsoft argues that growing discovery can create review queues rather than increase the rate of shipped fixes.Measured costs of automated validation; Shift 1
Findings that cite this record
Key questions this bears on
- Is AI shifting the balance between finding and fixing vulnerabilities?Discovery is ahead. Verification, triage, coordinated disclosure and safe repair remain bottlenecks between AI findings and deployed fixes.
Sources
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