ML Vulnerability Detection Patents: Who Leads, Where the Gaps Are 2026
Machine Learning for Vulnerability Detection: Patents Mapped Across Filers, Classes and Time
Filings peaked in 2021 at 339 and have since eased to 238 by 2024, a -30% move over that span — publication lag means 2025-2026 figures will still fill in. Annual filings rose from 81 in 2017 to a peak of 339 in 2021, then declined to 238 by 2024 — a -30% move over that three-year span. Counts for 2025 and 2026 are shown as published but will understate true filing activity, since publication typically lags filing by around 18 months.
- 1ORACLE INT CORP2.5%
- 2MICROSOFT TECHNOLOGY LICENSING LLC2.5%
- 3TEMPUS AI INC2.2%
- 4INTERNATIONAL BUSINESS MACHINE CORPORATION2.1%
- 5STRONG FORCE TX PORTFOLIO 2018 LLC1.9%
See the full machine learning for vulnerability detection analysis in Eureka
- The complete ranking, not just the top five
- Every IPC branch with its share of the corpus
- The most-cited records, and where claim space is still thin
Common questions about this landscape
Who holds the most patents in machine learning for vulnerability detection?
The ranked leader in this dataset holds 56 of the 2,271 records in scope, with the field tapering gradually rather than dropping off sharply — fifth place sits at 43 records and tenth at 28. The top 5 assignees combined account for 11.1% of all records, and the top 10 for 18.4%, which means no single company controls the space. The assignee mix includes both dedicated security vendors and large general-purpose technology and AI companies whose broader portfolios happen to touch code analysis.
Is patent filing in ML-based vulnerability detection still growing?
Filings rose steadily from 81 in 2017 to a peak of 339 in 2021, then eased to 238 by 2024, a -30% move over that three-year span. That is a cooling from an unusually high peak rather than clear evidence of decline, and the 2025-2026 figures in any raw trend chart will understate real activity because patent publication typically lags filing by around 18 months. Treat the last one to two years of any filing trend as provisional.
What technology areas does this patent landscape actually cover?
The core of the field sits in general digital-processing and AI-model claims: G06F and G06N each appear on close to half of the 2,271 records in scope. Beyond that, image and data-recognition classes (G06V, G06K, G06T) and domain-specific classes like G16H healthcare informatics and G16B bioinformatics also show meaningful overlap, since detection models for code often reuse claim structures built for medical-imaging or bioinformatics classifiers. Because a single record can carry several IPC classes, these shares add up to more than 100% and should not be read as mutually exclusive categories.
Disclaimer. This analysis is based on Patsnap Eureka data drawn from a limited snapshot of global patent records and is provided for general information and reference only. Patent data carries inherent limitations — recent filings are under-counted because of publication lag, counts may be on a record or family basis, classification and applicant-name data may contain errors or duplicates, and the underlying search query defines the scope shown — so the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing here is an exhaustive prior-art, novelty, freedom-to-operate or validity search, nor does it constitute legal, financial or professional advice, and it should not be relied upon as such. Verify independently and review with qualified patent and legal professionals before acting on it.
Method: Filing trend and technology composition. Derived from a Patsnap search on Machine Learning for Vulnerability Detection covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish. Every share divides by all records in scope. Data: Patsnap Eureka. See the full landscape report.