AI Red Teaming Patents: Leaders, Growth & White Space 2026
Filing growth compares 2021 (2,117 records) with 2024 (4,503) — a three-year span. 2024 is the most recent year we treat as complete: publication lags filing by roughly 18 months, so 2025 onwards are still filling in and any growth rate that ends there would understate the field. Top-5 share is the combined record count of the five largest assignees divided by all 54,469 records in scope (CR5), not by the ranked leaders only.
What the AI red teaming patent record actually shows
AI model red teaming — the practice of adversarially probing a model to find failure modes before it ships — has moved from a research technique into claimed IP. The search underlying this page pulls 54,469 published records filed or published between 2015 and mid-2026 that combine artificial intelligence or algorithmic subject matter with adversarial or red-teaming language. That volume alone says the phrase is no longer confined to safety papers; it is now a filing category with its own concentration pattern and its own open branches.
The record set skews recent and is still filling in: publication typically lags filing by around 18 months, so the 2025 and 2026 counts in any trend line understate what has actually been filed. Read the growth story through 2024, the last year that can be treated as complete, and treat everything after it as a floor, not a ceiling.
Filing trend and technology composition
Two views of the same 54,469-record corpus: how filing volume has moved year over year, and which IPC subclasses carry the claim density.
Steady climb, then a sharp step up
Annual filings rose from 102 records in 2017 to a peak of 4,503 in 2024, with the sharpest acceleration between 2021 and 2024 — a +113% increase over that span. The 2025 and 2026 figures (down to 459 for the partial 2026 year) reflect publication lag rather than a slowdown in actual filing activity.
Concentrated in AI computing and image processing
G06N (computing based on AI models) leads at 15.6% of the 54,469 records, followed by G06F (electric digital data processing) at 12.5%. Image and vision classes — G06T and G06V — together account for a meaningful share of the corpus, reflecting how much red-teaming activity is anchored in vision-model robustness testing rather than text-only evaluation. Healthcare informatics (G16H, 3.3%) and business-process classes (G06Q, 3.9%) show the technique diffusing into regulated and commercial domains beyond core model research.
Shares are the percentage of the 54,469 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on AI Safety, Evaluation & Assurance: AI Model Red Teaming Patent Landscape with Eureka
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Try EurekaA representative claim at the frontier
System and Method for Autonomous AI Red Teaming and Compliance Enforcement Using Adversarial Machine Learning
A fully autonomous and unsupervised system for automated AI red teaming that combines generative adversarial networks, reinforcement learning, and modular compliance logic to evaluate the robustness, reliability, and regulatory compliance of AI systems. The adaptive adversarial testing engine simulates real-world attacks on AI models, logs outcomes, generates audit reports, and helps align model behaviour with governance frameworks such as ISO 42001 and the NIST AI RMF, across vision, language and other modalities.Filed 2026 — one of the most recent records in scope, illustrating where claim drafting is heading rather than where the bulk of the corpus already sits.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US6850252B1 | Intelligent electronic appliance system and method | 4,059 |
| 2 | US6400996B1 | Adaptive pattern recognition based control system and method | 2,342 |
| 3 | US5875108A | Ergonomic man-machine interface incorporating adaptive pattern recognition based control system | 1,588 |
| 4 | US20070053513A1 | Intelligent electronic appliance system and method | 1,452 |
| 5 | US20200284883A1 | Component for a lidar sensor system, lidar sensor system, lidar sensor device, method for a lidar sensor syst… | 1,018 |
| 6 | US20190339688A1 | Methods and systems for data collection, learning, and streaming of machine signals for analytics and mainten… | 975 |
| 7 | US20180165554A1 | Semisupervised autoencoder for sentiment analysis | 797 |
| 8 | US20200348662A1 | Platform for facilitating development of intelligence in an industrial internet of things system | 734 |
| 9 | US20210157312A1 | Intelligent vibration digital twin systems and methods for industrial environments | 716 |
| 10 | US20210090694A1 | Data based cancer research and treatment systems and methods | 716 |
Citation counts reward older filings that have had more time to accumulate references — treat this table as a map of influence within the searched corpus, not a ranking of current technical importance.
Each row carries its publication number; clicking a row searches Eureka by that number.
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Four read-throughs from the ranking, the trend, and the most-cited records — useful for deciding where to file and who to watch.
The top of the field is real but narrow
The top five assignees combined hold 19.0% of all 54,469 records in scope, and the top ten hold 25.5%. That leaves roughly three-quarters of the corpus to entities outside the ranked leaders — a long tail rather than a duopoly, which matters for freedom-to-operate analysis: no single blocking position covers the field.
Acceleration is recent and steep
Filings rose from 2,117 in 2021 to 4,503 in 2024, a +113% increase over three years. That pace, combined with 2024 standing as the peak year so far, points to a field still being actively staked out rather than one settling into incremental refinement.
Core AI-model claims lead, vision claims follow
G06N (AI-model computing) and G06F (digital data processing) together anchor the corpus, but G06T and G06V — image generation and image/video recognition — carry a combined presence large enough to show that a substantial share of red-teaming IP is about probing vision systems, not just language models.
The most-cited prior art predates the current wave
The most-cited records in the corpus are older adaptive-control and pattern-recognition filings, not recent adversarial-testing patents. That is expected of any citation count inside a searched corpus — it rewards age, not current relevance — so use these records to understand foundational lineage, not to gauge who is active today.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to ai safety, evaluation & assurance: ai model red teaming patent landscape, with the prior art for and against each one.
Where to take this analysis
The numbers above set the boundaries of the field. Turning them into a filing or freedom-to-operate decision means going deeper on specific assignees, claims, or branches.
Map a specific assignee's claim scope
The ranking shows share of records, not claim breadth. Pulling the actual claim sets for any leader in the ranking shows whether their position is built on broad platform claims or narrow implementation patents.
Explore assignee claims in EurekaCheck freedom-to-operate before drafting
With three-quarters of the corpus sitting outside the ranked leaders, a freedom-to-operate check needs to cover the long tail, not just the names at the top.
Run an FTO search in EurekaTrack the under-claimed branches
Healthcare informatics and business-process classes show early diffusion of red-teaming techniques. Watching filing velocity in those classes flags where competitive pressure is about to build.
Set up a monitoring alert in EurekaCommon questions about AI red teaming patents
A search combining AI-model and algorithm terms with red-teaming and adversarial-testing language returns 54,469 published records dated between 2015 and mid-2026. That figure counts published documents, not unique inventions, and a substantial share of the corpus carries multiple IPC classes, so it should be read as the outer boundary of the field rather than a count of distinct technologies. The number is expected to grow further as 2025 and 2026 filings continue to publish.
The assignee ranking covers 100 companies, with the leader holding 4,410 records and the field dropping to 862 records by fifth place and 599 by tenth. The top five combined hold 19.0% of all 54,469 records in scope, and the top ten hold 25.5% — a meaningful but not dominant concentration. The remaining roughly three-quarters of records sit with entities outside the ranked leaders, indicating a genuinely fragmented competitive field rather than one controlled by a handful of players.
It is growing, sharply. Filings rose from 2,117 records in 2021 to 4,503 in 2024, a +113% increase over that three-year span, with 2024 standing as the peak year recorded so far. The apparent drop in 2025 and 2026 counts is a publication-lag artefact — patent applications typically take about 18 months to publish — not a real decline in filing activity, so those two years should not be read as a slowdown.
G06N, the class for computing arrangements based on AI models, leads at 15.6% of all 54,469 records, followed by G06F (general digital data processing) at 12.5%. Image and video-related classes — G06T and G06V — together represent a large share of the corpus, reflecting how much adversarial testing work targets vision models specifically. Because a single record can carry several IPC classes, these shares are each measured against the full record total and are not mutually exclusive.
The technology composition data shows lighter filing density in healthcare informatics (G16H, 3.3% of records) and business-process applications (G06Q, 3.9%) compared with core AI-model and image-processing classes. That gap suggests domain-specific adversarial-testing claims — for example, red-teaming methods tuned to clinical decision-support models or financial algorithms — are less contested than general-purpose testing frameworks. Combined with a long tail of assignees outside the top ten, there is room for claims that bind a red-teaming method to a specific regulated domain rather than claiming the general technique.
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.
Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company’s registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.