Adversarial Robustness Testing Patents: Top Companies & Trends 2026
Filing growth compares 2021 (1,231 records) with 2024 (1,617) — 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 9,603 records in scope (CR5), not by the ranked leaders only.
What the adversarial robustness testing patent record shows
Adversarial robustness testing covers the methods used to probe, stress and certify AI systems against manipulated inputs, black-box access constraints and edge-case failures before deployment. The patent record for this field spans 9,603 published records filed between 2015 and mid-2026, with visible acceleration from 2021 onward as model deployment scaled and regulatory attention on AI assurance grew. Filing activity is not evenly spread: one assignee holds a markedly larger share than any other, but the combined share held by the ranked leaders is modest against the full field, meaning most robustness-testing claims are still filed by entities outside the top ranks.
Because publication lags filing by roughly 18 months, the 2025 and 2026 figures in any trend line understate real filing activity for those years — the growth read that matters is the 2021-to-2024 span, where filings rose from 1,231 to 1,617, a 31% increase over three years that predates any lag distortion.
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Filing trends and technology composition
Two views of the same 9,603-record dataset: the pace at which robustness-testing patents have been filed year over year, and the IPC subclasses those filings actually claim into.
Filing trend, 2017–2026
Filings grew from 87 in 2017 to a peak of 1,745 in 2025, with the 2021→2024 span showing a documented +31% increase (1,231 to 1,617). The 2026 figure of 333 is a partial year and should be read as a floor, not a slowdown.
Technology composition by IPC subclass
G06N (AI-model computing) appears in 43.0% of the 9,603 records, ahead of G06F general digital data processing (28.2%) and G06T image processing (21.0%). Because records carry multiple IPC codes, these shares sum to more than 100% and should be read independently, not stacked.
Shares are the percentage of the 9,603 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: Adversarial Robustness Testing Patent Landscape with Eureka
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Try EurekaA representative adversarial robustness testing patent
Testing adversarial robustness of systems with limited access
An adversarial robustness testing method, system, and computer program product include testing, via an accelerator, a robustness of a black-box system under different access settings, where the testing includes tearing down the robustness testing to a subtask of a predetermined size.Filed by International Business Machines Corporation, published 2024-12-31 as US12182274B2.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US6850252B1 | Intelligent electronic appliance system and method | 4,059 |
| 2 | US7020701B1 | Method for collecting and processing data using internetworked wireless integrated network sensors (WINS) | 1,457 |
| 3 | US20070053513A1 | Intelligent electronic appliance system and method | 1,452 |
| 4 | US6735630B1 | Method for collecting data using compact internetworked wireless integrated network sensors (WINS) | 1,420 |
| 5 | US7813822B1 | Intelligent electronic appliance system and method | 1,198 |
| 6 | US6859831B1 | Method and apparatus for internetworked wireless integrated network sensor (WINS) nodes | 1,178 |
| 7 | US20200284883A1 | Component for a lidar sensor system, lidar sensor system, lidar sensor device, method for a lidar sensor syst… | 1,018 |
| 8 | US20190339688A1 | Methods and systems for data collection, learning, and streaming of machine signals for analytics and mainten… | 975 |
| 9 | US20200348662A1 | Platform for facilitating development of intelligence in an industrial internet of things system | 734 |
| 10 | US20210157312A1 | Intelligent vibration digital twin systems and methods for industrial environments | 716 |
Citation counts reflect influence within the searched corpus and skew toward older filings; they are not a measure of current commercial importance.
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Browse MCP servers →What the concentration and composition figures mean for filing strategy
The numbers point to a field with one dominant filer, a long tail of smaller entrants, and technology claims concentrated tightly around core AI-model computing rather than spread evenly across application domains.
One leader, then a long tail
The ranked leader holds 1,385 records on its own, but the top 5 assignees combined reach only 24.3% of the 9,603 records in scope, and the top 10 only 30.5%. That gap between the leader's individual share and the group share signals a field still open to new entrants below the top rank.
Claims cluster on AI-model computing
G06N draws 43.0% of records, well ahead of G06F (28.2%) and G06T (21.0%). Filers are claiming the AI-model layer itself far more than the surrounding data-processing or image-handling infrastructure around it.
Sustained growth through the last complete years
Filings rose from 1,231 in 2021 to 1,617 in 2024, a documented 31% increase over that three-year window. This is the most reliable growth read in the dataset since it ends before the ~18-month publication lag starts understating later years.
Application-specific claims trail the core
Healthcare informatics (G16H, 9.4%) and business process claims (G06Q, 8.1%) sit well below the core AI-model and image-processing classes, suggesting domain-specific robustness-testing claims remain comparatively under-filed relative to general-purpose methods.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to ai safety, evaluation & assurance: adversarial robustness testing patent landscape, with the prior art for and against each one.
Where to take this analysis next
The dataset points to a specific set of questions worth running down before committing filing budget or freedom-to-operate review time.
Map the white space beneath the leader
With the top 10 assignees holding only 30.5% of records, most of the field is unclaimed by any single dominant player. Run a targeted search on the specific sub-claims you plan to file to confirm the gap holds at that level of detail.
Explore white space in EurekaWatch the 2024–2025 filing cohort
The 2021–2024 growth rate of 31% is the last fully reliable signal; treat 2025 and 2026 counts as provisional until later publication catches up. Re-run this trend in six months for a fuller read on 2025.
Track filing trends in EurekaCheck freedom-to-operate against the representative claim
US12182274B2's black-box, limited-access testing claim is a useful anchor for any team building commercial robustness-testing tools. Compare your architecture against its subtask-teardown approach before finalising claim language.
Run a claim comparison in EurekaCommon questions on the adversarial robustness testing patent landscape
The dataset covers 9,603 published records filed or published between 2015 and mid-2026 under a search string targeting adversarial robustness testing terminology. Filing activity rose sharply from 87 records in 2017 to a peak of 1,745 in 2025, though the most recent year is always undercounted because publication lags filing by roughly 18 months. This figure counts patent families as tracked in the assignee ranking, which is the fairer unit for cross-jurisdiction comparison than raw document counts.
One assignee holds a clearly larger share than any other, at 1,385 records, with the next four ranked filers bringing the top 5 combined to 24.3% of the 9,603 records in scope. The top 10 combined reach 30.5%. That gap between the leader's individual position and the group share means the remaining 90 ranked companies, plus filers outside the ranking entirely, still account for the majority of the field, so no single company controls the space outright.
The largest concentration sits in G06N, AI-model computing, which touches 43.0% of the 9,603 records, followed by G06F general digital data processing at 28.2% and G06T image data processing at 21.0%. Smaller but notable shares appear in G06V image and video recognition (16.2%), H04L digital transmission (12.2%), and application-specific classes like G16H healthcare informatics (9.4%) and G06Q business processing (8.1%). Because a single record often carries several IPC codes, these percentages do not sum to 100% and should be read as independent measures of how often each class appears.
Yes, based on the last fully reliable window: filings rose from 1,231 in 2021 to 1,617 in 2024, a documented 31% increase over three years. The 2025 figure of 1,745 looks like a peak, but because publication lags filing by roughly 18 months, 2025 and especially the partial 2026 figure of 333 should be treated as still filling in rather than as evidence of a slowdown. Anyone tracking this field should expect the 2025–2026 counts to rise as more records publish.
The technology composition shows heavy concentration in core AI-model and image-processing classes but comparatively lighter filing in application-specific domains such as healthcare informatics (9.4% of records) and business-process applications (8.1%). Combined with an assignee landscape where the top 10 filers hold only 30.5% of all records, there is room for claims that apply robustness-testing methods to a specific vertical rather than competing directly on general-purpose testing infrastructure, where the largest filer already holds a strong position.
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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.