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Run your analysis now →https://www.patsnap.com/resources/blog/rd-blog/fuzzing-and-dynamic-testing-patent-landscape/ · Patsnap · data cut-off 2026-07-31 · downloaded from the live page
Patent Landscape · Program Analysis & Vulnerability DiscoveryFuzzing and dynamic testing patents: mapping the coverage-guided testing field
- One clear leader, then a steep drop. the top-ranked assignee holds 9 filings against a fifth-place and tenth-place count of just 1 each, across 14 ranked companies.
- Filings cluster in one IPC subclass. 90.0% of the 20 records in scope carry a G06F electric-digital-data-processing class, with every other subclass at 5.0% or 15.0%.
- Activity peaked in 2020, not now. the trend tops out at 9 filings in 2020, and publication lag means the most recent years understate real filing activity.
20
Published Records
US
Leading Jurisdiction
14
Active Filers Ranked
2020
Peak Filing Year
Published byPatsnap Research··7 min readSourced from Patsnap Eureka
Field Overview
What this patent set covers
This landscape tracks patent families combining coverage-guided fuzzing, input mutation or seed corpus generation with supporting mechanics such as code coverage tracking, sanitizer instrumentation, test harness construction, crash deduplication, execution feedback loops or automated input generation. It spans automotive and embedded-systems security testing, general-purpose software vulnerability discovery, and hybrid fuzzing systems that combine symbolic execution with feedback-driven mutation.
The scope is deliberately narrow: 20 published records sit inside it, ranked across 14 assignees. That is a small, technical corner of program-analysis IP rather than a broad software-testing category, so read every share and ranking against that denominator.
Filing activity and technology composition, 2017-2026
Source: Patsnap Eureka. Assignee ranking and totals. Derived from a Patsnap search on Fuzzing and Dynamic Testing covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
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The Data
Filing trend and technology composition
Two views of the same 20-record set: how filing activity moved year over year, and which IPC subclasses the claims actually sit in.
Filing trend, 2017-2026
Filings were flat at zero in 2017, rose to a peak of 9 in 2020, and the most recent years cannot be read as decline given typical 18-month publication lag; treat 2025-2026 counts as incomplete rather than as a drop-off.
IPC subclass composition
G06F dominates at 90.0% of the 20 records in scope, consistent with a field centred on digital data processing and software testing mechanics. G06N (AI-based computing) appears in 15.0% of records, hinting at machine-learning-assisted fuzzing; the remaining subclasses (B65B, E06B, G06Q, H01R, H02B, H04L) each sit at 5.0%, marking single-record excursions into domain-specific applications such as packaging, access control hardware and power switchgear testing.
Shares are the percentage of the 20 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Source: Patsnap Eureka. Filing trend and technology composition. Derived from a Patsnap search on Fuzzing and Dynamic Testing covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.
Go deeper on Fuzzing and Dynamic Testing with Eureka
This page is one run against one query. Ask Eureka your own question about fuzzing and dynamic testing and every answer comes back with the patent numbers behind it.
Try EurekaRepresentative Filing
A representative claim in this space
Representative Record · EP4137977A1
Coverage-guided fuzzing of remote embedded devices using a debugger
ARGUS CYBER SECURITY LTD
The invention refers to a method of testing a program, the method comprising: collecting coverage data by means of a debugger operatively connected to a device executing the program; and utilizing the coverage data to control a fuzzing engine used for testing the program. Furthermore, the invention refers to a corresponding debugger, fuzzing engine and system.Filed by Argus Cyber Security; the original title is in German. Translation is descriptive, not a verbatim legal translation.
Most-cited records in scope
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US11620129B1 | Agent-based detection of fuzzing activity associated with a target program | 13 |
| 2 | US10599558B1 | System and method for identifying inputs to trigger software bugs | 13 |
| 3 | US20200394311A1 | Vulnerability driven hybrid test system for application programs | 13 |
| 4 | US20210279562A1 | Standardizing disparate data points | 11 |
| 5 | US20210326246A1 | Constraint guided hybrid test system for application programs | 9 |
| 6 | EP4137977A1 | Coverage guided fuzzing of remote embedded devices using a debugger | 7 |
| 7 | US20230205677A1 | Method for fuzz testing | 4 |
| 8 | US20240354236A1 | Method for generating at least one new test case for a fuzzing software test | 3 |
| 9 | US20240354240A1 | Method for generating at least one new test case based on a black box fuzzing of a target program to be tested | 1 |
Citation counts favour older records inside this searched corpus and should be read as a signal of influence, not current importance.
Publication numbers are shown where the record carries one (9 of 9 rows); clicking a row searches Eureka by that number.
Source: Patsnap Eureka. Citation counts and representative records. Derived from a Patsnap search on Fuzzing and Dynamic Testing covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
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What the numbers mean for a filing decision
Three patterns stand out once you weight the record count, the assignee ranking and the IPC composition against each other.
Concentration
9 vs 1
leader filings vs fifth/tenth place
One leader, then a long tail
The top-ranked assignee holds 9 of the tracked filings while fifth and tenth place each hold just 1. That gap suggests one organisation built a sustained fuzzing patent programme while the rest of the 14 ranked assignees filed opportunistically, often around a single application area rather than a platform.
Read the leader's claims closely before filing adjacent art; the tail is thin enough that new entrants have room.
Technology mix
90.0%
of 20 records carry G06F
Claims sit almost entirely in general computing
Nearly all records in scope classify under G06F, the electric-digital-data-processing subclass, which is where coverage tracking, mutation engines and harness constructs get claimed. The 15.0% overlap with G06N points to a smaller but real cluster of AI-assisted fuzzing claims worth watching.
Domain-specific applications (packaging, doors/shutters, power switchboards) each appear once — thin enough to be exploratory rather than established art.
Timing
2020 peak
9 filings in the peak year
Activity peaked mid-decade, recent years are undercounted
Filing volume rose to 9 in 2020 and the years since cannot be read as a genuine slowdown, because publication typically lags filing by around 18 months. Anyone benchmarking recent competitive activity should treat 2025 and 2026 figures as provisional.
Re-check this trend after the next data refresh rather than concluding the field has cooled.
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Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to fuzzing and dynamic testing, with the prior art for and against each one.
Source: Patsnap Eureka. Co-assignee relationships and derived observations. Derived from a Patsnap search on Fuzzing and Dynamic Testing covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
Assignee Landscape
Who holds the claims
The ranking covers all 14 assignees the data endpoint returns for this scope — not a top-50 or top-100 cut — so the tail is genuinely short, not truncated.
Leader
9 filings
top-ranked assignee
A single organisation with sustained volume
One assignee accounts for 9 of the records in scope, well ahead of the rest of the ranked list. That volume points to a deliberate patent programme around fuzzing infrastructure rather than a single defensive filing.
Its claims are the first prior art to clear before filing in agent-based or harness-construction fuzzing.
Mid-field
1 filing
fifth-place assignee
A steep drop after the leader
Fifth place in the ranking holds just 1 filing, the same as tenth place. There is no meaningful second tier here — most ranked assignees are single-filing entrants rather than repeat players.
Useful signal for freedom-to-operate: beyond the leader, no one assignee blocks broad swathes of the field.
Collaboration
10 pairs
co-assignee pairs identified
Co-filing is occasional, not structural
Ten co-assignee pairs appear in the data, each linked at the minimum observed strength. This looks like individual joint-filing arrangements (academic or small-team collaborations) rather than any industry consortium pattern.
No pair recurs at meaningfully higher strength, so treat these as isolated rather than a signal of an alliance.
Recent-year filing momentum
| Assignee | Recent year | YoY |
|---|---|---|
| Baidu USA LLC | 0 | — |
| Robert Bosch GmbH | 0 | — |
| International Business Machines Corporation | 0 | — |
| RAHUL RAMAKRISHNAN | 0 | -100% |
| PLAXIDITYX LTD | 0 | — |
| NEXXON COMPUTERS PVT LTD | 0 | -100% |
| MS MAHALAKSHMI M | 0 | — |
| MS KOWSHIKA M | 0 | — |
Source: Patsnap Eureka. Assignee-level momentum. Derived from a Patsnap search on Fuzzing and Dynamic Testing covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
Next Steps
Where to take this next
The dataset points to specific follow-up work rather than a general conclusion about the field.
Check the leader's full claim set
With 9 filings against a fifth-place count of 1, the top assignee's claim language is the single most important prior art to map before drafting in agent-based fuzzing or harness construction.
Search assignee portfolios in EurekaWatch the G06N overlap
15.0% of records already cross into AI-based computing classification. If mutation-strategy learning is part of your roadmap, this is the smallest but most active sub-cluster to track for new filings.
Track IPC movement in EurekaRe-run the trend after the next refresh
Publication lag means 2025-2026 filing counts are provisional. Revisit the year-over-year picture once another data cycle closes to see whether 2020's peak has been matched or passed.
Set a landscape alert in EurekaSource: Patsnap Eureka. Forward-looking reading of the same dataset. Derived from a Patsnap search on Fuzzing and Dynamic Testing covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
FAQ
Common questions about fuzzing and dynamic-testing patents
This landscape tracks 20 published records that combine coverage-guided fuzzing, input mutation or seed corpus generation with supporting mechanics such as code coverage tracking, sanitizer instrumentation or crash deduplication. That is a narrow, technically defined slice of program-analysis IP, not a count of every software-testing patent. The ranking built from these records covers 14 assignees, so both the record count and the assignee count are small enough that a handful of filings can shift the picture.
One assignee leads the ranked list with 9 filings, well ahead of the rest of the 14 ranked companies; fifth place and tenth place each hold only 1 filing. That gap means there is a genuine leader in filing volume but no established second tier beneath them. Anyone assessing freedom-to-operate should focus review time on the leader's claims first, since the rest of the field is largely single-filing entrants.
Filing activity rose to a peak of 9 in 2020, and the years since should not be read as a decline. Patent publication typically lags the actual filing date by around 18 months, so the most recent years in any trend chart are always undercounted relative to true filing activity. A meaningful growth-rate figure is not computable from this window because too few complete years remain once that lag is accounted for.
90.0% of the 20 records in scope classify under G06F, the electric-digital-data-processing subclass covering coverage tracking, mutation engines and test harnesses. A smaller 15.0% overlap with G06N points to AI-assisted fuzzing techniques. Single records also appear in packaging, door/shutter hardware, business-process software, connectors, power switchboards and digital transmission, suggesting fuzzing methods are being adapted into a few domain-specific hardware and firmware testing contexts, though each of those adaptations is still only one filing deep.
The clearest openings sit in the IPC branches that appear only once in the 20-record set: AI-assisted mutation strategy selection, fuzzing harnesses for embedded access-control or power-switchgear hardware, and crash-deduplication approaches for hybrid symbolic-plus-feedback fuzzing systems. These branches show up in the technology mix but have not accumulated repeat filings, which is a reasonable proxy for open claim space rather than for low commercial relevance. A first claim there should engage directly with the leader's existing coverage-and-feedback architecture to avoid overlap.
Answers are grounded in the same dataset. Derived from a Patsnap search on Fuzzing and Dynamic Testing covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
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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.