[{"data":1,"prerenderedAt":805},["ShallowReactive",2],{"/en-us/blog/building-gitlab-with-gitlab-api-fuzzing-workflow":3,"navigation-en-us":41,"banner-en-us":441,"footer-en-us":451,"blog-post-authors-en-us-Mike Eddington|Eugene Lim":693,"blog-related-posts-en-us-building-gitlab-with-gitlab-api-fuzzing-workflow":718,"assessment-promotions-en-us":757,"next-steps-en-us":795},{"id":4,"title":5,"authorSlugs":6,"body":9,"categorySlug":10,"config":11,"content":15,"description":9,"extension":29,"isFeatured":13,"meta":30,"navigation":31,"path":32,"publishedDate":22,"seo":33,"stem":37,"tagSlugs":38,"__hash__":40},"blogPosts/en-us/blog/building-gitlab-with-gitlab-api-fuzzing-workflow.yml","Building Gitlab With Gitlab Api Fuzzing Workflow",[7,8],"mike-eddington","eugene-lim",null,"engineering",{"slug":12,"featured":13,"template":14},"building-gitlab-with-gitlab-api-fuzzing-workflow",false,"BlogPost",{"title":16,"description":17,"authors":18,"heroImage":21,"date":22,"body":23,"category":10,"tags":24},"Building GitLab with GitLab: Web API Fuzz Testing","Our new series shows how we dogfood new DevSecOps platform features to ready them for you. First up, security testing.",[19,20],"Mike Eddington","Eugene Lim","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659740/Blog/Hero%20Images/building-gitlab-with-gitlab-no-type.png","2023-05-09","At GitLab, we try to [dogfood everything](https://handbook.gitlab.com/handbook/product/product-processes/#dogfood-everything) to help us better understand the product, pain points, and configuration issues. We use what we learn to build a more efficient, feature-rich platform and user experience. In this first installment of our “Building GitLab with GitLab” series, we will focus on security testing. We constantly strive to improve our security testing coverage and integrate it into our DevSecOps lifecycle. These considerations formed the motivation for the API fuzzing dogfooding project at GitLab. By sharing our lessons from building this workflow, we hope other teams can also learn how to integrate GitLab’s Web API Fuzz Testing and solve some common challenges.\n\n## What is Web API Fuzz Testing?\n\nWeb API Fuzz Testing involves generating and sending various unexpected input parameters to a web API in an attempt to trigger unexpected behavior and errors in the API backend. By analyzing these errors, you can discover bugs and potential security issues missed by other scanners that focus on specific vulnerabilities. GitLab's Web API Fuzz Testing complements and should be run in addition to GitLab Secure’s other security scanners such as static application security testing ([SAST](https://docs.gitlab.com/ee/user/application_security/sast/)) and dynamic application security testing ([DAST](https://docs.gitlab.com/ee/user/application_security/dast/)) APIs.\n\n## Auto-generating an OpenAPI specification\nTo run the Web API Fuzzing Analyzer, you need one of the following:\n* OpenAPI Specification - Version 2 or 3\n* GraphQL Schema\n* HTTP Archive (HAR)\n* Postman Collection - Version 2.0 or 2.1\n\nAt the start of the API fuzzing project, the [API Vision working group](https://handbook.gitlab.com/handbook/company/structure/working-groups/api-vision/) was also working on an issue to automatically document [GitLab’s REST API endpoints in an OpenAPI specification](https://gitlab.com/groups/gitlab-org/-/epics/8636), so we worked with our colleague Andy Soiron on implementing it. Because GitLab uses the [grape](https://github.com/ruby-grape/grape) API framework, Andy had already identified and [tested](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/95877) the [grape-swagger](https://github.com/ruby-grape/grape-swagger) gem that auto-generates an OpenAPI v2 specification based on existing grape annotations. For example, the following API endpoint code:\n\n```text\n\n     Class.new(Grape::API) do\n       format :json\n       desc 'This gets something.'\n       get '/something' do\n         { bla: 'something' }\n       end\n       add_swagger_documentation\n     end\n\n```\nWill be parsed by grape-swagger into:\n\n```text\n{\n  // rest of OpenAPI v2 specification\n  …\n  \"paths\": {\n    \"/something\": {\n      \"get\": {\n        \"description\": \"This gets something.\",\n        \"produces\": [\n          \"application/json\"\n        ],\n        \"operationId\": \"getSomething\",\n        \"responses\": {\n          \"200\": {\n            \"description\": \"This gets something.\"\n          }\n        }\n      }\n    }\n  }\n}\n```\n\n\nHowever, with almost 2,000 API operations with different requirements and formats, a lot of additional work needed to be done to resolve edge cases that did not meet the requirements of grape-swagger or the OpenAPI format. For example, one simple case was API endpoints that accept file parameters, such as the [upload metric image endpoint](https://docs.gitlab.com/ee/api/issues.html#upload-metric-image). GitLab uses the [Workhorse](https://gitlab.com/gitlab-org/gitlab/tree/master/workhorse) smart reverse proxy to handle \"large\" HTTP requests such as file uploads. As such, file parameters must be of the type WorkhorseFile:\n\n\n```text\nnamespace ':id/issues/:issue_iid/metric_images' do\n            …\n            desc 'Upload a metric image for an issue' do\n              success Entities::IssuableMetricImage\n            end\n            params do\n              requires :file, type: ::API::Validations::Types::WorkhorseFile, desc: 'The image file to be uploaded'\n              optional :url, type: String, desc: 'The url to view more metric info'\n              optional :url_text, type: String, desc: 'A description of the image or URL'\n            end\n            post do\n              require_gitlab_workhorse!\n\n```\n\nBecause grape-swagger does not recognize what OpenAPI type WorkhorseFile corresponds to, it excludes the parameter from its output. We fixed this by adding a grape-swagger-specific documentation to override the type during generation:\n\n```text\n\n             requires :file, type: ::API::Validations::Types::WorkhorseFile, desc: 'The image file to be uploaded', documentation: { type: 'file' }\n\n```\n\nHowever, not all edge cases could be resolved with a simple match-and-replace in the grape annotations. For example, Ruby on Rails supports wildcard segment parameters. A route like `get 'books/*section/:title'` would match`books/some/section/last-words-a-memoir`. In addition, the URI would be parsed such that the `section` path parameter would have the value `some/section` and the `title` path parameter would have the value `last-words-a-memoir`.\n\nCurrently, grape-swagger does not recognize these wildcard segments as path parameters. For example, the route would generate:\n\n```text\n\"paths\": {\n  \"/api/v2/books/*section/{title}\": {\n    \"get\": {\n    ...\n      \"parameters\": [\n         {\n           \"in\": \"query\", \"name\": \"*section\"\n           ...\n  }\n}\n```\n\nInstead of the expected:\n\n```text\n\"paths\": {\n  \"/api/v2/books/{section}/{title}\": {\n    \"get\": {\n    ...\n      \"parameters\": [\n         {\n           \"in\": \"path\", \"name\": \"section\"\n           ...\n  }\n}\n```\n\nAs such, we also needed to make several patches to grape-swagger, which we forked while waiting for the changes to be accepted upstream. Nevertheless, with lots of careful checking and cooperation across teams, we managed to get the OpenAPI specification generated for most of the endpoints.\n\n## Performance tuning\n\nWith the OpenAPI specification, we could now begin with the API fuzzing. GitLab already uses the [Review Apps](https://docs.gitlab.com/ee/ci/review_apps/) feature to generate testing environments for some feature changes, providing a readily available fuzzing target. However, given the large number of endpoints, it would be impossible to expect a standard shared runner to complete fuzzing in a single job. The Web API Fuzz Testing documentation includes a [performance tuning section](https://docs.gitlab.com/ee/user/application_security/api_fuzzing/#performance-tuning-and-testing-speed) that recommends the following:\n\n* using a multi-CPU Runner\n* excluding slow operations\n* splitting a test into multiple jobs\n* excluding operations in feature branches, but not default branch\n\nThe first recommendation was easy to implement with a dedicated fuzzing runner. We recommend doing this for large scheduled fuzzing workflows, especially if you select the Long-100 fuzzing profile. We also began excluding slow operations by checking the job logs for the time taken to complete each operation. Along the way, we identified other endpoints that needed to be excluded, such as the [revoke token endpoint](https://docs.gitlab.com/ee/api/personal_access_tokens.html#revoke-a-personal-access-token) that prematurely ended the fuzzing session.\n\nSplitting the test into multiple jobs took the most effort due to the requirements of the OpenAPI format. Each OpenAPI document includes a required set of objects and fields, so it is not simply a matter of splitting after a fixed number of lines. Additionally, each operation relies on entities defined in the definitions object, so we needed to ensure that when splitting the OpenAPI specification, the entities required by the endpoints were included. We also wrote a quick script to fill the example parameter data with actual data from the testing environment, such as project IDs.\n\nWhile it was possible to run these scripts locally, then push the split jobs and OpenAPI specifications to the repository, this created a large number of changes every time we updated the original OpenAPI specification. Instead, we adapted the workflow to use dynamically generated child pipelines that would split the OpenAPI document in a CI job, then generate a child pipeline with jobs for each split document. This made iterating a lot easier and more agile. We have uploaded [the scripts and pipeline configuration](https://gitlab.com/eugene_lim/api-fuzzing-dogfooding) for reference.\n\nBy tweaking the number of parallel jobs and fuzzing profile, we were eventually able to achieve a reasonably comprehensive fuzzing session in an acceptable time frame. When tuning your own fuzzing workflow, balancing these trade-offs is essential.\n\n## Triaging the API fuzzing findings\n\nWith the fuzzing done, we were now confronted with hundreds of findings. Unlike DAST analyzers that try to detect specific vulnerabilities, Web API Fuzz Testing looks for unexpected behavior and errors that may not necessarily be vulnerabilities. This is why fuzzing faults discovered by the API Fuzzing Analyzer show up as vulnerabilities with a severity of “Unknown.” This requires more involved triaging.\n\nFortunately, the Web API fuzzer also outputs Postman collections as artifacts in the Vulnerability Report page. These collections allow you to quickly repeat requests that triggered a fault during fuzzing. For this stage of the fuzzing workflow, we recommend that you set up a local instance of the application so that you can easily check logs and debug specific faults. In this case, we ran the [GitLab Development Kit](https://gitlab.com/gitlab-org/gitlab-development-kit).\n\nMany of the faults occurred due to a lack of error handling for unexpected inputs. We created issues from the Vulnerability Report page, and if we found that a particular fault had the same root cause as a previously triaged fault, we linked the vulnerability to the original issue instead.\n\n## Lessons learned\n\nThe API fuzzing dogfooding project turned out to be a fruitful exercise that benefited other workstreams at GitLab, such as the API documentation project. In addition, tuning and triaging helped us identify key pain points in the process for improvement. Automated API documentation generation is difficult even with OpenAPI, particularly on a long-lived codebase. GitLab’s existing annotations and tests helped speed up documentation via a distributed, asynchronous workflow across multiple teams. In addition, many GitLab features such as Review Apps, Vulnerability Reports, and dynamically generated child pipelines helped us build a robust fuzzing workflow.\n\nThere are still many improvements that can be made to the workflow. Moving to OpenAPI v3 could improve endpoint coverage. The Secure team also wrote a [HAR Recorder](https://gitlab.com/gitlab-org/security-products/har-recorder) tool that could help generate HAR files on the fly instead of relying on static documentation. For now, due to the high compute cost of fuzzing thousands of operations in GitLab’s API, the workflow is better suited to a scheduled pipeline instead of GitLab’s core pipeline.\n\nFor teams that have already implemented several layers of static and dynamic checks and want to take further steps to increase coverage, we recommend trying a Web API fuzzing exercise as a way to validate assumptions and discover “unknown unknowns” in your code.\n\nWe encourage you to get familiar with API fuzzing and let us know how it works for you. If you face any issues or have any feedback, please file an issue at the [issue tracker on GitLab.com](https://gitlab.com/gitlab-org/gitlab/-/issues/). Use the `~\"Category:API Security\"` label when opening a new issue regarding API fuzzing to ensure it is quickly reviewed by the appropriate team members.\n",[25,26,25,27,28],"inside GitLab","security","testing","tutorial","yml",{},true,"/en-us/blog/building-gitlab-with-gitlab-api-fuzzing-workflow",{"title":16,"description":17,"ogTitle":16,"ogDescription":17,"noIndex":13,"ogImage":21,"ogUrl":34,"ogSiteName":35,"ogType":36,"canonicalUrls":34},"https://about.gitlab.com/blog/building-gitlab-with-gitlab-api-fuzzing-workflow","https://about.gitlab.com","article","en-us/blog/building-gitlab-with-gitlab-api-fuzzing-workflow",[39,26,39,27,28],"inside-gitlab","q430vq40BVpx3Q5iNh-j19eyaSQ_f9p61jThsGDmGzI",{"data":42},{"logo":43,"freeTrial":48,"sales":53,"login":58,"items":63,"search":371,"minimal":402,"duo":421,"pricingDeployment":431},{"config":44},{"href":45,"dataGaName":46,"dataGaLocation":47},"/","gitlab logo","header",{"text":49,"config":50},"Get free 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to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[724],"Tim Rizzi","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","2026-03-12","If you're a platform engineer, you've probably had this conversation:\n  \n*\"Security says we need to use hardened base images.\"*\n\n*\"Great, where do I configure credentials for yet another registry?\"*\n\n*\"Also, how do we make sure everyone actually uses them?\"*\n\nOr this one:\n\n*\"Why are our builds so slow?\"*\n\n*\"We're pulling the same 500MB image from Docker Hub in every single job.\"*\n\n*\"Can't we just cache these somewhere?\"*\n\nI've been working on [Container Virtual Registry](https://docs.gitlab.com/user/packages/virtual_registry/container/) at GitLab specifically to solve these problems. It's a pull-through cache that sits in front of your upstream registries — Docker Hub, dhi.io (Docker Hardened Images), MCR, and Quay — and gives your teams a single endpoint to pull from. Images get cached on the first pull. Subsequent pulls come from the cache. Your developers don't need to know or care which upstream a particular image came from.\n\nThis article shows you how to set up Container Virtual Registry, specifically with Docker Hardened Images in mind, since that's a combination that makes a lot of sense for teams concerned about security and not making their developers' lives harder.\n\n## What problem are we actually solving?\n\nThe Platform teams I usually talk to manage container images across three to five registries:\n\n* **Docker Hub** for most base images\n* **dhi.io** for Docker Hardened Images (security-conscious workloads)\n* **MCR** for .NET and Azure tooling\n* **Quay.io** for Red Hat ecosystem stuff\n* **Internal registries** for proprietary images\n\nEach one has its own:\n\n* Authentication mechanism\n* Network latency characteristics\n* Way of organizing image paths\n\nYour CI/CD configs end up littered with registry-specific logic. Credential management becomes a project unto itself. And every pipeline job pulls the same base images over the network, even though they haven't changed in weeks.\n\nContainer Virtual Registry consolidates this. One registry URL. One authentication flow (GitLab's). Cached images are served from GitLab's infrastructure rather than traversing the internet each time.\n\n## How it works\n\nThe model is straightforward:\n\n```text\nYour pipeline pulls:\n  gitlab.com/virtual_registries/container/1000016/python:3.13\n\nVirtual registry checks:\n  1. Do I have this cached? → Return it\n  2. No? → Fetch from upstream, cache it, return it\n\n```\n\nYou configure upstreams in priority order. When a pull request comes in, the virtual registry checks each upstream until it finds the image. The result gets cached for a configurable period (default 24 hours).\n\n```text\n┌─────────────────────────────────────────────────────────┐\n│                    CI/CD Pipeline                       │\n│                          │                              │\n│                          ▼                              │\n│   gitlab.com/virtual_registries/container/\u003Cid>/image   │\n└─────────────────────────────────────────────────────────┘\n                           │\n                           ▼\n┌─────────────────────────────────────────────────────────┐\n│            Container Virtual Registry                   │\n│                                                         │\n│  Upstream 1: Docker Hub ────────────────┐               │\n│  Upstream 2: dhi.io (Hardened) ────────┐│               │\n│  Upstream 3: MCR ─────────────────────┐││               │\n│  Upstream 4: Quay.io ────────────────┐│││               │\n│                                      ││││               │\n│                    ┌─────────────────┴┴┴┴──┐            │\n│                    │        Cache          │            │\n│                    │  (manifests + layers) │            │\n│                    └───────────────────────┘            │\n└─────────────────────────────────────────────────────────┘\n```\n\n## Why this matters for Docker Hardened Images\n\n[Docker Hardened Images](https://docs.docker.com/dhi/) are great because of the minimal attack surface, near-zero CVEs, proper software bills of materials (SBOMs), and SLSA provenance. If you're evaluating base images for security-sensitive workloads, they should be on your list.\n\nBut adopting them creates the same operational friction as any new registry:\n\n* **Credential distribution**: You need to get Docker credentials to every system that pulls images from dhi.io.\n* **CI/CD changes**: Every pipeline needs to be updated to authenticate with dhi.io.\n* **Developer friction**: People need to remember to use the hardened variants.\n* **Visibility gap**: It's difficulat to tell if teams are actually using hardened images vs. regular ones.\n\nVirtual registry addresses each of these:\n\n**Single credential**: Teams authenticate to GitLab. The virtual registry handles upstream authentication. You configure Docker credentials once, at the registry level, and they apply to all pulls.\n\n**No CI/CD changes per-team**: Point pipelines at your virtual registry. Done. The upstream configuration is centralized.\n\n**Gradual adoption**: Since images get cached with their full path, you can see in the cache what's being pulled. If someone's pulling `library/python:3.11` instead of the hardened variant, you'll know.\n\n**Audit trail**: The cache shows you exactly which images are in active use. Useful for compliance, useful for understanding what your fleet actually depends on.\n\n## Setting it up\n\nHere's a real setup using the Python client from this demo project.\n\n### Create the virtual registry\n\n```python\nfrom virtual_registry_client import VirtualRegistryClient\n\nclient = VirtualRegistryClient()\n\nregistry = client.create_virtual_registry(\n    group_id=\"785414\",  # Your top-level group ID\n    name=\"platform-images\",\n    description=\"Cached container images for platform teams\"\n)\n\nprint(f\"Registry ID: {registry['id']}\")\n# You'll need this ID for the pull URL\n```\n\n### Add Docker Hub as an upstream\n\nFor official images like Alpine, Python, etc.:\n\n```python\ndocker_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://registry-1.docker.io\",\n    name=\"Docker Hub\",\n    cache_validity_hours=24\n)\n```\n\n### Add Docker Hardened Images (dhi.io)\n\nDocker Hardened Images are hosted on `dhi.io`, a separate registry that requires authentication:\n\n```python\ndhi_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-docker-username\",\n    password=\"your-docker-access-token\",\n    cache_validity_hours=24\n)\n```\n\n### Add other upstreams\n\n```python\n# MCR for .NET teams\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://mcr.microsoft.com\",\n    name=\"Microsoft Container Registry\",\n    cache_validity_hours=48\n)\n\n# Quay for Red Hat stuff\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://quay.io\",\n    name=\"Quay.io\",\n    cache_validity_hours=24\n)\n```\n\n### Update your CI/CD\n\nHere's a `.gitlab-ci.yml` that pulls through the virtual registry:\n\n```yaml\nvariables:\n  VIRTUAL_REGISTRY_ID: \u003Cyour_virtual_registry_ID>\n\n  \nbuild:\n  image: docker:24\n  services:\n    - docker:24-dind\n  before_script:\n    # Authenticate to GitLab (which handles upstream auth for you)\n    - echo \"${CI_JOB_TOKEN}\" | docker login -u gitlab-ci-token --password-stdin gitlab.com\n  script:\n    # All of these go through your single virtual registry\n    \n    # Official Docker Hub images (use library/ prefix)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/library/alpine:latest\n    \n    # Docker Hardened Images from dhi.io (no prefix needed)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/python:3.13\n    \n    # .NET from MCR\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/dotnet/sdk:8.0\n```\n\n### Image path formats\n\nDifferent registries use different path conventions:\n\n| Registry | Pull URL Example |\n|----------|------------------|\n| Docker Hub (official) | `.../library/python:3.11-slim` |\n| Docker Hardened Images (dhi.io) | `.../python:3.13` |\n| MCR | `.../dotnet/sdk:8.0` |\n| Quay.io | `.../prometheus/prometheus:latest` |\n\n### Verify it's working\n\nAfter some pulls, check your cache:\n\n```python\nupstreams = client.list_registry_upstreams(registry['id'])\nfor upstream in upstreams:\n    entries = client.list_cache_entries(upstream['id'])\n    print(f\"{upstream['name']}: {len(entries)} cached entries\")\n\n```\n\n## What the numbers look like\n\nI ran tests pulling images through the virtual registry:\n\n| Metric | Without Cache | With Warm Cache |\n|--------|---------------|-----------------|\n| Pull time (Alpine) | 10.3s | 4.2s |\n| Pull time (Python 3.13 DHI) | 11.6s | ~4s |\n| Network roundtrips to upstream | Every pull | Cache misses only |\n\n\n\n\nThe first pull is the same speed (it has to fetch from upstream). Every pull after that, for the cache validity period, comes straight from GitLab's storage. No network hop to Docker Hub, dhi.io, MCR, or wherever the image lives.\n\nFor a team running hundreds of pipeline jobs per day, that's hours of cumulative build time saved.\n\n## Practical considerations\nHere are some considerations to keep in mind:\n\n### Cache validity\n\n24 hours is the default. For security-sensitive images where you want patches quickly, consider 12 hours or less:\n\n```python\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-username\",\n    password=\"your-token\",\n    cache_validity_hours=12\n)\n```\n\nFor stable, infrequently-updated images (like specific version tags), longer validity is fine.\n\n### Upstream priority\n\nUpstreams are checked in order. If you have images with the same name on different registries, the first matching upstream wins.\n\n### Limits\n\n* Maximum of 20 virtual registries per group\n* Maximum of 20 upstreams per virtual registry\n\n## Configuration via UI\n\nYou can also configure virtual registries and upstreams directly from the GitLab UI—no API calls required. Navigate to your group's **Settings > Packages and registries > Virtual Registry** to:\n\n* Create and manage virtual registries\n* Add, edit, and reorder upstream registries\n* View and manage the cache\n* Monitor which images are being pulled\n\n## What's next\n\nWe're actively developing:\n\n* **Allow/deny lists**: Use regex to control which images can be pulled from specific upstreams.\n\nThis is beta software. It works, people are using it in production, but we're still iterating based on feedback.\n\n## Share your feedback\n\nIf you're a platform engineer dealing with container registry sprawl, I'd like to understand your setup:\n\n* How many upstream registries are you managing?\n* What's your biggest pain point with the current state?\n* Would something like this help, and if not, what's missing?\n\nPlease share your experiences in the [Container Virtual Registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/work_items/589630).\n## Related resources\n- [New GitLab metrics and registry features help reduce CI/CD bottlenecks](https://about.gitlab.com/blog/new-gitlab-metrics-and-registry-features-help-reduce-ci-cd-bottlenecks/#container-virtual-registry)\n- [Container Virtual Registry documentation](https://docs.gitlab.com/user/packages/virtual_registry/container/)\n- [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)",[28,729,730],"product","features",{"featured":13,"template":14,"slug":732},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":734,"config":744},{"title":735,"description":736,"authors":737,"heroImage":739,"date":740,"category":10,"tags":741,"body":743},"How IIT Bombay students are coding the future with GitLab","At GitLab, we often talk about how software accelerates innovation. But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[738],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[263,615,742],"open source","The GitLab team recently had the privilege of judging the **iHack Hackathon** at **IIT Bombay's E-Summit**. The energy was electric, the coffee was flowing, and the talent was undeniable. But what struck us most wasn't just the code — it was the sheer determination of students to solve real-world problems, often overcoming significant logistical and financial hurdles to simply be in the room.\n\n\nThrough our [GitLab for Education program](https://about.gitlab.com/solutions/education/), we aim to empower the next generation of developers with tools and opportunity. Here is a look at what the students built, and how they used GitLab to bridge the gap between idea and reality.\n\n## The challenge: Build faster, build securely\n\nThe premise for the GitLab track of the hackathon was simple: Don't just show us a product; show us how you built it. We wanted to see how students utilized GitLab's platform — from Issue Boards to CI/CD pipelines — to accelerate the development lifecycle.\n\nThe results were inspiring.\n\n## The winners\n\n### 1st place: Team Decode — Democratizing Scientific Research\n\n**Project:** FIRE (Fast Integrated Research Environment)\n\nTeam Decode took home the top prize with a solution that warms a developer's heart: a local-first, blazing-fast data processing tool built with [Rust](https://about.gitlab.com/blog/secure-rust-development-with-gitlab/) and Tauri. They identified a massive pain point for data science students: existing tools are fragmented, slow, and expensive.\n\nTheir solution, FIRE, allows researchers to visualize complex formats (like NetCDF) instantly. What impressed the judges most was their \"hacker\" ethos. They didn't just build a tool; they built it to be open and accessible.\n\n**How they used GitLab:** Since the team lived far apart, asynchronous communication was key. They utilized **GitLab Issue Boards** and **Milestones** to track progress and integrated their repo with Telegram to get real-time push notifications. As one team member noted, \"Coordinating all these technologies was really difficult, and what helped us was GitLab... the Issue Board really helped us track who was doing what.\"\n\n![Team Decode](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/epqazj1jc5c7zkgqun9h.jpg)\n\n### 2nd place: Team BichdeHueDost — Reuniting to Solve Payments\n\n**Project:** SemiPay (RFID Cashless Payment for Schools)\n\nThe team name, BichdeHueDost, translates to \"Friends who have been set apart.\" It's a fitting name for a group of friends who went to different colleges but reunited to build this project. They tackled a unique problem: handling cash in schools for young children. Their solution used RFID cards backed by a blockchain ledger to ensure secure, cashless transactions for students.\n\n**How they used GitLab:** They utilized [GitLab CI/CD](https://about.gitlab.com/topics/ci-cd/) to automate the build process for their Flutter application (APK), ensuring that every commit resulted in a testable artifact. This allowed them to iterate quickly despite the \"flaky\" nature of cross-platform mobile development.\n\n![Team BichdeHueDost](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/pkukrjgx2miukb6nrj5g.jpg)\n\n### 3rd place: Team ZenYukti — Agentic Repository Intelligence\n\n**Project:** RepoInsight AI (AI-powered, GitLab-native intelligence platform)\n\nTeam ZenYukti impressed us with a solution that tackles a universal developer pain point: understanding unfamiliar codebases. What stood out to the judges was the tool's practical approach to onboarding and code comprehension: RepoInsight-AI automatically generates documentation, visualizes repository structure, and even helps identify bugs, all while maintaining context about the entire codebase.\n\n**How they used GitLab:** The team built a comprehensive CI/CD pipeline that showcased GitLab's security and DevOps capabilities. They integrated [GitLab's Security Templates](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/Security) (SAST, Dependency Scanning, and Secret Detection), and utilized [GitLab Container Registry](https://docs.gitlab.com/user/packages/container_registry/) to manage their Docker images for backend and frontend components. They created an AI auto-review bot that runs on merge requests, demonstrating an \"agentic workflow\" where AI assists in the development process itself.\n\n![Team ZenYukti](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/ymlzqoruv5al1secatba.jpg)\n\n## Beyond the code: A lesson in inclusion\n\nWhile the code was impressive, the most powerful moment of the event happened away from the keyboard.\n\nDuring the feedback session, we learned about the journey Team ZenYukti took to get to Mumbai. They traveled over 24 hours, covering nearly 1,800 kilometers. Because flights were too expensive and trains were booked, they traveled in the \"General Coach,\" a non-reserved, severely overcrowded carriage.\n\nAs one student described it:\n\n*\"You cannot even imagine something like this... there are no seats... people sit on the top of the train. This is what we have endured.\"*\n\nThis hit home. [Diversity, Inclusion, and Belonging](https://handbook.gitlab.com/handbook/company/culture/inclusion/) are core values at GitLab. We realized that for these students, the barrier to entry wasn't intellect or skill, it was access.\n\nIn that moment, we decided to break that barrier. We committed to reimbursing the travel expenses for the participants who struggled to get there. It's a small step, but it underlines a massive truth: **talent is distributed equally, but opportunity is not.**\n\n![hackathon class together](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380252/o5aqmboquz8ehusxvgom.jpg)\n\n### The future is bright (and automated)\n\nWe also saw incredible potential in teams like Prometheus, who attempted to build an autonomous patch remediation tool (DevGuardian), and Team Arrakis, who built a voice-first job portal for blue-collar workers using [GitLab Duo](https://about.gitlab.com/gitlab-duo/) to troubleshoot their pipelines.\n\nTo all the students who participated: You are the future. Through [GitLab for Education](https://about.gitlab.com/solutions/education/), we are committed to providing you with the top-tier tools (like GitLab Ultimate) you need to learn, collaborate, and change the world — whether you are coding from a dorm room, a lab, or a train carriage. **Keep shipping.**\n\n> :bulb: Learn more about the [GitLab for Education program](https://about.gitlab.com/solutions/education/).\n",{"slug":745,"featured":13,"template":14},"how-iit-bombay-students-code-future-with-gitlab",{"content":747,"config":755},{"title":748,"description":749,"authors":750,"heroImage":751,"date":752,"category":10,"tags":753,"body":754},"Artois University elevates research and curriculum with GitLab Ultimate for Education","Artois University's CRIL leveraged the GitLab for Education program to gain free access to Ultimate, transforming advanced research and computer science curricula.",[738],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099203/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2820%29_2bJGC5ZP3WheoqzlLT05C5_1750099203484.png","2025-12-10",[615,263,729],"Leading academic institutions face a critical challenge: how to provide thousands of students and researchers with industry-standard, **full-featured DevSecOps tools** without compromising institutional control. Many start with basic version control, but the modern curriculum demands integrated capabilities for planning, security, and advanced CI/CD.\n\nThe **GitLab for Education program** is designed to solve this by providing access to **GitLab Ultimate** for qualifying institutions, allowing them to scale their operations and elevate their academic offerings. \n\nThis article showcases a powerful success story from the **Centre de Recherche en Informatique de Lens (CRIL)**, a joint laboratory of **Artois University** and CNRS in France. After years of relying solely on GitLab Community Edition (CE), the university's move to GitLab Ultimate through the GitLab for Education program immediately unlocked advanced capabilities, transforming their teaching, research, and contribution workflows virtually overnight. This story demonstrates why GitLab Ultimate is essential for institutions seeking to deliver advanced computer science and research curricula.\n\n## GitLab Ultimate unlocked: Managing scale and driving academic value\n\n**Artois University's** self-managed GitLab instance is a large-scale operation, supporting nearly **3,000 users** across approximately **19,000 projects**, primarily serving computer science students and researchers. While GitLab Community Edition was robust, the upgrade to GitLab Ultimate provided the sophisticated tooling necessary for managing this scale and facilitating advanced university-level work.\n\n***\"We can see the difference,\" says Daniel Le Berre, head of research at CRIL and the instance maintainer. \"It's a completely different product. Each week reveals new features that directly enhance our productivity and teaching.\"***\n\nThe institution joined the GitLab for Education program specifically because it covers both **instructional and non-commercial research use cases** and offers full access to Ultimate's features, removing significant cost barriers.\n\n### Key GitLab Ultimate benefits for students and researchers\n\n* **Advanced project management at scale:** Master's students now benefit from **GitLab Ultimate's project planning features**. This enables them to structure, track, and manage complex, long-term research projects using professional methodologies like portfolio management and advanced issue tracking that seamlessly roll up across their thousands of projects.\n\n* **Enhanced visibility:** Features like improved dashboards and code previews directly in Markdown files dramatically streamline tracking and documentation review, reducing administrative friction for both instructors and students managing large project loads.\n\n## Comprehensive curriculum: From concepts to continuous delivery\n\nGitLab Ultimate is deeply integrated into the computer science curriculum, moving students beyond simple `git` commands to practical **DevSecOps implementation**.\n\n* **Git fundamentals:** Students begin by visualizing concepts using open-source tools to master Git concepts.\n\n* **Full CI/CD implementation:** Students use GitLab CI for rigorous **Test-Driven Development (TDD)** in their software projects. They learn to build, test, and perform quality assurance using unit and integration testing pipelines—core competency made seamless by the integrated platform.\n\n* **DevSecOps for research and documentation:** The university teaches students that DevSecOps principles are vital for all collaborative work. Inspired by earlier work in Delft, students manage and produce critical research documentation (PDFs from Markdown files) using GitLab, incorporating quality checks like linters and spell checks directly in the CI pipeline. This ensures high-quality, reproducible research output.\n\n* **Future-proofing security skills:** The GitLab Ultimate platform immediately positions the institution to incorporate advanced DevSecOps features like SAST and DAST scanning as their research and development code projects grow, ensuring students are prepared for industry security standards.\n\n## Accelerating open source contributions with GitLab Duo\n\nAccess to the full GitLab platform, including our AI capabilities, has empowered students to make impactful contributions to the wider open source community faster than ever before.\n\nTwo Master's students recently completed direct contributions to the GitLab product, adding the **ORCID identifier** into user profiles. Working on GitLab.com, they leveraged **GitLab Duo's AI chat and code suggestions** to navigate the codebase efficiently.\n\n***\"This would not have been possible without GitLab Duo,\" Daniel Le Berre notes. \"The AI features helped students, who might have lacked deep codebase knowledge, deliver meaningful contributions in just two weeks.\"***\n\nThis demonstrates how providing students with cutting-edge tools **accelerates their learning and impact**, allowing them to translate classroom knowledge into real-world contributions immediately.\n\n## Empowering open research and institutional control\n\nThe stability of the self-managed instance at Artois University is key to its success. This model guarantees **institutional control and stability** — a critical factor for long-term research preservation.\n\nThe institution's expertise in this area was recently highlighted in a major 2024 study led by CRIL, titled: \"[Higher Education and Research Forges in France - Definition, uses, limitations encountered and needs analysis](https://hal.science/hal-04208924v4)\" ([Project on GitLab](https://gitlab.in2p3.fr/coso-college-codes-sources-et-logiciels/forges-esr-en)). The research found that the vast majority of public forges in French Higher Education and Research relied on **GitLab**. This finding underscores the consensus among academic leaders that self-hosted solutions are essential for **data control and longevity**, especially when compared to relying on external, commercial forges.\n\n## Unlock GitLab Ultimate for your institution today\n\nThe success story of **Artois University's CRIL** proves the transformative power of the GitLab for Education program. By providing **free access to GitLab Ultimate**, we enable large-scale institutions to:\n\n1.  **Deliver a modern, integrated DevSecOps curriculum.**\n\n2.  **Support advanced, collaborative research projects with Ultimate planning features.**\n\n3.  **Empower students to make AI-assisted open source contributions.**\n\n4.  **Maintain institutional control and data longevity.**\n\nIf your academic institution is ready to equip its students and researchers with the complete DevSecOps platform and its most advanced features, we invite you to join the program.\n\nThe program provides **free access to GitLab Ultimate** for qualifying instructional and non-commercial research use cases.\n\n**Apply now [online](https://about.gitlab.com/solutions/education/join/).**\n",{"slug":756,"featured":31,"template":14},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"promotions":758},[759,773,784],{"id":760,"categories":761,"header":763,"text":764,"button":765,"image":770},"ai-modernization",[762],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":766,"config":767},"Get your AI maturity score",{"href":768,"dataGaName":769,"dataGaLocation":245},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":771},{"src":772},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":774,"categories":775,"header":776,"text":764,"button":777,"image":781},"devops-modernization",[729,561],"Are you just managing tools or shipping innovation?",{"text":778,"config":779},"Get your DevOps maturity score",{"href":780,"dataGaName":769,"dataGaLocation":245},"/assessments/devops-modernization-assessment/",{"config":782},{"src":783},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":785,"categories":786,"header":787,"text":764,"button":788,"image":792},"security-modernization",[26],"Are you trading speed for security?",{"text":789,"config":790},"Get your security maturity score",{"href":791,"dataGaName":769,"dataGaLocation":245},"/assessments/security-modernization-assessment/",{"config":793},{"src":794},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":796,"blurb":797,"button":798,"secondaryButton":803},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":799,"config":800},"Get your free trial",{"href":801,"dataGaName":52,"dataGaLocation":802},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":497,"config":804},{"href":56,"dataGaName":57,"dataGaLocation":802},1773350830005]