[{"data":1,"prerenderedAt":814},["ShallowReactive",2],{"/en-us/blog/secure-rust-development-with-gitlab":3,"navigation-en-us":36,"banner-en-us":445,"footer-en-us":455,"blog-post-authors-en-us-Fernando Diaz":697,"blog-related-posts-en-us-secure-rust-development-with-gitlab":711,"blog-promotions-en-us":751,"next-steps-en-us":804},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":12,"path":28,"publishedDate":24,"seo":29,"stem":32,"tagSlugs":33,"__hash__":35},"blogPosts/en-us/blog/secure-rust-development-with-gitlab.yml","Secure Rust Development With Gitlab",[7],"fernando-diaz",null,"engineering",{"slug":11,"featured":12,"template":13},"secure-rust-development-with-gitlab",true,"BlogPost",{"title":15,"description":16,"authors":17,"tags":19,"heroImage":23,"category":9,"date":24,"body":25},"Secure Rust development with GitLab","Learn how GitLab supports Rust development through its CI/CD capabilities, security scanning, dedicated Rust integrations, AI features, and more.",[18],"Fernando Diaz",[20,21,22],"community","open source","tutorial","https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314674/tct6zf6evw0xgddd2vo3.png","2025-09-02","Rust has emerged as one of the most beloved programming languages due to its performance, memory safety, and concurrency features. As Rust adoption continues to grow, many developers are looking for robust CI/CD platforms to support their Rust projects.\nGitLab's appeal to Rust developers extends beyond simple code hosting. The platform offers robust [CI/CD](https://about.gitlab.com/topics/ci-cd/) capabilities that align perfectly with Rust's emphasis on safety, performance, and reliability. GitLab makes it easy to create repositories and use off-the-shelf Docker containers to put together custom CI jobs. Developers can easily set up automated testing, cross-platform builds, and documentation generation. The platform's integrated approach to DevSecOps resonates with Rust's philosophy of providing comprehensive tooling out of the box.\n## About the demo application\nBeing interested in how mortgage rates affect monthly payments and how hard it is to to afford a house in the current times, I decided to write a [mortgage calculator](https://gitlab.com/gitlab-da/tutorials/security-and-governance/devsecops/rust/mortgage-calculator) in Rust, which I will use as an example throughout this tutorial. Feel free to [import this project](https://docs.gitlab.com/user/project/import/repo_by_url/) and follow along.\nThe mortgage calculator will help users calculate monthly mortgage payments, including principal, interest, property taxes, insurance, PMI, and HOA fees. It provides a modern, intuitive GUI using the [egui](https://www.egui.rs/) framework, as well as a CLI for running it in the terminal.\n![Mortgage calculator GUI](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314538/l5bjnzqvpoyikuyxpx2a.png)\nThis application contains a [`.gitlab-ci.yml`](https://gitlab.com/gitlab-da/tutorials/security-and-governance/devsecops/rust/mortgage-calculator/-/blob/main/.gitlab-ci.yml?ref_type=heads) that generates a pipeline, which will build, test, package, scan, and deploy the software. We will go over this pipeline definition in detail in the sections below.\n![Mortgage Calculator Pipeline](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314540/llmsfoaupedhkem0hjqp.png)\n## Building and testing Rust applications\nGitLab's Docker-based CI/CD system excels at Rust development workflows, providing a robust foundation for compilation, testing, and code quality checks. The platform's caching mechanisms are particularly valuable for Rust projects, which can have lengthy compilation times due to the language's thorough optimization and safety checking processes.\n### Building\nRust's excellent cross-compilation capabilities combined with GitLab's flexible CI/CD system create a powerful solution for building applications across multiple platforms. This is particularly valuable for Rust applications that need to run on various operating systems and architectures without sacrificing performance or requiring platform-specific code.\n**Note:** You can learn more about the `.gitlab-ci.yml` by reading the [CI/CD YAML syntax reference](https://docs.gitlab.com/ci/yaml/).\n```yaml\n# Cache configuration to speed up builds by reusing dependencies cache:\n  key: $CI_COMMIT_REF_SLUG                               # Use branch name as cache key\n  paths:\n    - .cargo/                                            # Cache Cargo registry and git dependencies\n    - target/                                            # Cache compiled artifacts\n\n# Base template for Rust jobs - shared configuration .rust-template:\n  image: rust:$RUST_VERSION-slim                         # Use slim Rust image for faster downloads\n  before_script:\n    # Install system dependencies required for building the Rust application\n    - apt-get update && apt-get install -y pkg-config libssl-dev libgtk-3-dev libxcb-shape0-dev libxcb-xfixes0-dev\n\n# Template for cross-compilation build jobs .build-template:\n  extends: .rust-template                                # Inherit from rust-template\n  stage: build                                           # Execute during build stage\n  script:\n    - rustup target add $TARGET                          # Add the target platform for cross-compilation\n    - cargo build --release --target $TARGET             # Build optimized release binary for target platform\n\n# Build for Linux x86_64 (primary target platform) build-linux:\n  extends: .build-template                               # Use build template configuration\n  variables:\n    TARGET: x86_64-unknown-linux-gnu                     # Linux 64-bit target\n  artifacts:\n    paths:\n      - target/$TARGET/release/mortgage-calculator       # Save the compiled binary\n    expire_in: 1 week                                    # Keep artifacts for 1 week\n  allow_failure: false                                   # This build must succeed\n\n# Build for Windows x86_64 (cross-compilation) build-windows:\n  extends: .build-template                               # Use build template configuration\n  variables:\n    TARGET: x86_64-pc-windows-gnu                        # Windows 64-bit target\n  artifacts:\n    paths:\n      - target/$TARGET/release/mortgage-calculator       # Save the compiled binary\n    expire_in: 1 week                                    # Keep artifacts for 1 week\n  allow_failure: true                                    # Allow this build to fail (cross-compilation can be tricky)\n\n# Build for macOS x86_64 (cross-compilation) build-macos:\n  extends: .build-template                               # Use build template configuration\n  variables:\n    TARGET: x86_64-apple-darwin                          # macOS 64-bit target\n  artifacts:\n    paths:\n      - target/$TARGET/release/mortgage-calculator       # Save the compiled binary\n    expire_in: 1 week                                    # Keep artifacts for 1 week\n  allow_failure: true                                    # Allow this build to fail (cross-compilation can be tricky)\n\n```\nThis GitLab CI configuration defines three build jobs that cross-compile a Rust mortgage calculator application for different platforms:\n* `build-linux` creates a Linux x86_64 binary (required to pass) * `build-windows` creates Windows binaries (allowed to fail) * `build-macos` creates macOS x86_64 binaries (allowed to fail)\nAll builds use shared templates for dependency caching and consistent build environments.\n### Testing\nGitLab CI/CD streamlines code testing through its integrated pipeline system that automatically triggers test suites whenever code is pushed to the repository. Developers can define multiple types of tests — unit tests, integration tests, linting, and formatting checks — all within a single `.gitlab-ci.yml` configuration file, with each test running in isolated Docker containers to ensure consistent environments.\n```yaml\n# Run unit tests test:unit:\n  extends: .rust-template                                # Use Rust template configuration\n  stage: test                                            # Execute during test stage\n  script:\n    - cargo test --verbose                               # Run all unit tests with verbose output\n\n# Run integration tests using the compiled binary test:integration:\n  extends: .rust-template                                # Use Rust template configuration\n  stage: test                                            # Execute during test stage\n  script:\n    # Test the compiled binary with sample inputs and verify expected output\n    - target/x86_64-unknown-linux-gnu/release/mortgage-calculator --cli calculate --property-value 350000 --down-payment 70000 --interest-rate 5.0 | grep -q \"TOTAL MONTHLY PAYMENT\"\n  needs:\n    - build-linux                                        # Depends on Linux build job completing\n\n# Run Clippy linter for code quality checks test:clippy:\n  extends: .rust-template                                # Use Rust template configuration\n  stage: test                                            # Execute during test stage\n  script:\n    - rustup component add clippy                        # Install Clippy linter\n    - cargo clippy -- -D warnings                       # Run Clippy and treat warnings as errors\n  allow_failure: true                                    # Allow linting failures (can be improved over time)\n\n# Check code formatting test:format:\n  extends: .rust-template                                # Use Rust template configuration\n  stage: test                                            # Execute during test stage\n  script:\n    - rustup component add rustfmt                       # Install Rust formatter\n    - cargo fmt -- --check                              # Check if code is properly formatted\n  allow_failure: true                                    # Allow formatting failures (can be improved over time)\n\n```\nThis GitLab CI configuration creates four test jobs that validate a Rust mortgage calculator application:\n* `test:unit` runs unit tests * `test:integration` executes the compiled Linux binary with sample inputs to verify functionality * `test:clippy` performs code quality linting (allowed to fail) * `test:format` checks code formatting compliance (allowed to fail)\n## Package and Container Registries\nGitLab's [Package Registry](https://docs.gitlab.com/user/packages/package_registry/) provides a secure solution to the common challenge of sharing internal libraries and proprietary code within organizations. This capability is essential for enterprises and teams that need to maintain artifacts while leveraging the broader Rust ecosystem.\nThe registry supports [generic artifacts](https://docs.gitlab.com/user/packages/generic_packages/) with fine-grained access controls that align with GitLab's project permissions. This means teams can share libraries securely across projects while maintaining intellectual property protection and compliance requirements.\nAdditonally, we can containerize our application and store the container images in GitLab's built-in [Container Registry](https://docs.gitlab.com/user/packages/container_registry/).\n### Publishing to GitLab Package Registry\nThis section of our `.gitlab-ci.yml` demonstrates how to package and publish Rust applications as tar archives to GitLab's generic package registry using CI/CD automation.\n```yaml\n# Package application as tar archive package:tar:\n  image: alpine/curl:8.12.1                             # Lightweight image with curl for uploading\n  stage: package                                         # Execute during package stage\n  variables:\n    PACKAGE_NAME: mortgage-calculator.tar.gz             # Name of the archive file\n  script:\n    # Create tar archive of the Linux binary\n    - tar -czvf $PACKAGE_NAME target/x86_64-unknown-linux-gnu/release/mortgage-calculator\n    # Upload archive to GitLab Package Registry using API\n    - |\n      curl -v --location --header \"JOB-TOKEN: $CI_JOB_TOKEN\" \\\n      --upload-file $PACKAGE_NAME \\\n      \"$CI_API_V4_URL/projects/$CI_PROJECT_ID/packages/generic/tar/$CI_COMMIT_BRANCH/$PACKAGE_NAME\"\n  artifacts:\n    paths:\n      - target/x86_64-unknown-linux-gnu/release/mortgage-calculator  # Save binary\n      - mortgage-calculator.tar.gz                      # Save archive\n    expire_in: 1 week                                    # Keep artifacts for 1 week\n  needs:\n    - build-linux                                        # Depends on Linux build completing\n\n```\nThis GitLab CI configuration defines one packaging job `package:tar` that creates a compressed tar archive of the Linux mortgage calculator binary and uploads it to GitLab's Package Registry, while also saving both the binary and archive as pipeline artifacts.\n![Package Registry](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314541/uqnejcipnge3r1dngotm.png)\n### Publishing to GitLab Container Registry\nThe below shows the process of building Dockerfiles and publishing Docker images to GitLab's Container Registry with proper tagging and authentication.\n```yaml\n# Package application as Docker image package:docker:\n  image: docker:24.0                                     # Use Docker image for building containers\n  stage: package                                         # Execute during package stage\n  services:\n    - docker:24.0-dind                                   # Docker-in-Docker service for building images\n  before_script:\n    # Login to GitLab Container Registry\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n  script:\n    - docker build -t $DOCKER_IMAGE_NAME:$DOCKER_IMAGE_TAG .  # Build Docker image with commit SHA tag\n    - docker tag $DOCKER_IMAGE_NAME:$DOCKER_IMAGE_TAG $DOCKER_IMAGE_NAME:latest  # Also tag as latest\n    - docker push $DOCKER_IMAGE_NAME:$DOCKER_IMAGE_TAG  # Push tagged image to registry\n    - docker push $DOCKER_IMAGE_NAME:latest             # Push latest image to registry\n\n```\nThis GitLab CI configuration defines one Docker packaging job `package:docker` that builds a Docker image of the mortgage calculator application, tags it with both the commit SHA and \"latest,\" and then pushes both tagged versions to  GitLab's Container Registry.\n![Container Registry](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314537/nlodhvdnpqccf0cryfqu.png)\n## Security scanning\nGitLab security scanning provides comprehensive protection that goes beyond Rust's built-in memory safety guarantees. While Rust prevents many common security vulnerabilities at compile time, applications still need protection against dependency vulnerabilities, unsafe code blocks, and logical security issues.\nThe platform's Static Application Security Testing (SAST) integrates seamlessly with Rust's toolchain, providing automated security analysis as part of the CI/CD pipeline This proactive approach catches security issues before they reach production, supporting both compliance requirements and secure development practices.\nGitLab's comprehensive security features including SAST, dependency scanning, secret detection and more can easily be implemented via templates, as seen below. **Note:** Additional configuration is required to [enable SAST for Rust](https://docs.gitlab.com/user/application_security/sast/#scan-a-rust-application).\n```yaml\n# Include GitLab's security scanning templates for DevSecOps include:\n  - template: Jobs/SAST.gitlab-ci.yml                    # Static Application Security Testing\n  - template: Jobs/Dependency-Scanning.latest.gitlab-ci.yml  # Scan dependencies for vulnerabilities\n  - template: Jobs/Container-Scanning.gitlab-ci.yml      # Scan Docker containers for vulnerabilities\n  - template: Jobs/SAST-IaC.gitlab-ci.yml               # Infrastructure as Code security scanning\n  - template: Jobs/Secret-Detection.gitlab-ci.yml        # Detect secrets in source code\n\n```\nSecurity scanners can be configured similar to how you would configure any GitLab job:\n```yaml\n# Configure Semgrep SAST scanning for Rust files semgrep-sast:\n  rules:\n    - if: $CI_COMMIT_BRANCH                              # Run on any branch\n      exists:\n        - \"**/*.rs\"                                      # Only if Rust files exist\n  variables:\n    SAST_EXCLUDED_PATHS: \".cargo/**\"                     # Exclude Cargo cache from scanning\n\n# Scan Docker container for security vulnerabilities container_scanning:\n  stage: container-security                              # Execute during container-security stage\n  variables:\n    CS_IMAGE: $DOCKER_IMAGE_NAME:$DOCKER_IMAGE_TAG      # Image to scan\n    CS_DOCKERFILE_PATH: Dockerfile                       # Path to Dockerfile for context\n  needs:\n    - package:docker                                     # Depends on Docker image being built\n\n```\nWhen vulnerabilites are detected in a merge request (MR), you can see all the vulnerabilites detected and use the provided information to either resolve or dismiss vulnerabilities.\n![Vulnerability MR view](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314544/gcicke3ltvbcv57mr8zr.png)\nYou also can add [Security Policies](http://docs.gitlab.com/user/application_security/policies/) to require approval before vulnerable code can be merged, or to force scanners to run regardless of what is in the `.gitlab-ci.yml`.\n![Merge Request Approval Policy](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314538/c95nwocol03lonrr6r4n.png)\nYou can triage all the vulnerabilities found in your default branch by using the [Vulnerability Report](https://docs.gitlab.com/user/application_security/vulnerability_report/):\n![Vulnerability Report](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314544/b0nctigbv1ddpzizkp9x.png)\n## Documentation with GitLab Pages\n[GitLab Pages](https://docs.gitlab.com/user/project/pages/) provides an excellent platform for hosting Rust documentation, integrating seamlessly with Cargo's built-in documentation generation. This creates a powerful workflow where API documentation, project guides, and examples are automatically generated and deployed with every code change.\nThe combination of `cargo doc` and GitLab Pages enables teams to maintain up-to-date documentation without manual intervention, ensuring that documentation stays synchronized with code changes. This is particularly valuable for Rust projects where comprehensive documentation is essential to understand complex APIs and safety contracts.\n### Automated documentation deployment\nThe following code shows the CI/CD configuration for automatically generating and deploying Rust documentation using `cargo doc` and GitLab Pages.\n```yaml\n# Generate and publish documentation using GitLab Pages build-documentation:\n  extends: .rust-template                                # Use Rust template configuration\n  stage: build                                           # Execute during build stage\n  variables:\n    GIT_SUBMODULE_STRATEGY: recursive                    # Clone submodules recursively if needed\n  pages: true                                            # Enable GitLab Pages deployment\n  script:\n    - cargo doc --no-deps                                # Generate documentation without dependencies\n    - mv target/doc public                               # Move docs to public directory for Pages\n  artifacts:\n    paths:\n      - public                                           # GitLab Pages serves from public directory\n  rules:\n    - if: $CI_COMMIT_REF_NAME == $CI_DEFAULT_BRANCH     # Only run on default branch (main/master)\n  environment:\n    name: documentation                                  # Environment name for tracking\n    url: $CI_PAGES_URL/mortgage_calculator/index.html   # Documentation URL\n  allow_failure: true                                    # Allow documentation build to fail\n\n```\nOnce the job is complete, you can see the deployed documentation by visiting the [GitLab Environment](https://docs.gitlab.com/ci/environments/) it has been deployed to.\n![Pages environment](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314541/aofa6wwjugeyeshuwg9r.png)\nThis allows you to manage multiple versions of the documentation in different envrionments. The documentation will be deployed consistent with the `cargo doc` output:\n![Pages build](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314541/go0tmljjzoxq5bujsdbv.png)\n## Deploy anywhere\nOne of GitLab's greatest strengths is its infrastructure-agnostic approach to deployment. Whether your organization runs on traditional on-premises servers, modern cloud platforms, hybrid environments, or edge computing infrastructure, GitLab's CI/CD system can deploy Rust applications seamlessly across any target environment.\nGitLab's deployment flexibility stems from its container-first approach and extensive integration ecosystem. The platform supports deployment to virtually any infrastructure that can run containers, virtual machines, or bare-metal applications. This versatility is particularly valuable for Rust applications, which often need to run in diverse environments ranging from resource-constrained embedded systems to high-performance cloud clusters.\n### Kubernetes deployment\nGitLab simplifies Kubernetes deployments by providing built-in cluster integration and pre-configured Docker images that include essential tools like Helm and kubectl, eliminating the need for developers to set up complex deployment environments.\n```yaml\n# Deploy application to Kubernetes cluster deploy:kubernetes:\n  stage: deploy                                          # Execute during deploy stage\n  image: registry.gitlab.com/gitlab-org/cluster-integration/helm-install-image:helm-3.10.0-kube-1.24.6-alpine-3.15  # Image with Helm and kubectl\n  variables:\n    HELM_HOST: \"localhost:44134\"                         # Helm host configuration\n    HELM_DEPLOY_NAME: mortgage-calc-$CI_COMMIT_REF_NAME  # Deployment name based on branch\n    HELM_DEPLOY_NAMESPACE: calc-app                      # Kubernetes namespace for deployment\n    KUBE_CONTEXT: $CI_PROJECT_PATH:rust-mortgage-calculator  # Kubernetes context to use\n  script:\n    - kubectl config use-context $KUBE_CONTEXT          # Set the kubectl context\n    # Deploy using Helm with custom values and Docker image\n    - helm upgrade --install $HELM_DEPLOY_NAME chart -f chart/values.yaml\n      --namespace $HELM_DEPLOY_NAMESPACE\n      --create-namespace\n      --set image=$DOCKER_IMAGE_NAME:$DOCKER_IMAGE_TAG\n      --set calc.name=$HELM_DEPLOY_NAME \n  needs:\n    - package:docker                                     # Depends on Docker image being available\n\n```\nThis GitLab CI configuration defines one deployment job `deploy:kubernetes` that uses Helm to deploy the mortgage calculator application to a Kubernetes cluster, creating or upgrading the deployment in a dedicated namespace while using the Docker image built in the previous packaging stage.\n![Kubernetes output](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314538/bgmbh4qyfxcnnlqvsitc.png)\n## GitLab Duo AI features\n[GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/) AI features provide significant advantages for Rust development by offering intelligent code suggestions and explanations specifically tailored to the language's unique syntax and patterns.\nThe GitLab platform supports Rust as one of its [directly-supported languages](https://docs.gitlab.com/user/project/repository/code_suggestions/supported_extensions/#supported-languages-by-ide) for every IDE, ensuring high-quality code completion and generation that understands Rust's ownership model, memory safety principles, and idiomatic patterns. \n### GitLab Duo Code Suggestions\nGitLab Duo's ability to provide contextual code suggestions while typing helps developers navigate Rust's sometimes complex syntax more efficiently, reducing the learning curve for newcomers and accelerating productivity for experienced developers.\n![Code Suggestions](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314538/uvy6hmzvyd0mnqeic9tq.png)\n### GitLab Duo Chat\n[GitLab Duo Chat](https://about.gitlab.com/blog/gitlab-duo-chat-gets-agentic-ai-makeover/) complements the code suggestions by offering conversational assistance for explaining Rust code sections, debugging compiler errors, and providing guidance on best practices. This is particularly valuable in Rust development where compiler error messages, while helpful, can sometimes be overwhelming for developers transitioning from other languages. The AI can help interpret Rust's detailed error messages and suggest fixes, making the development process more efficient by reducing the time spent deciphering compilation issues.\n![GitLab Duo Chat](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314537/depxztu1h89bez3ylwk3.png)\nGitLab Duo Chat can also be used directly from Vulnerability Report to explain a vulnerability. GitLab Duo [Vulnerability Explanation](https://about.gitlab.com/the-source/ai/understand-and-resolve-vulnerabilities-with-ai-powered-gitlab-duo/) represents a significant advancement in making application security more accessible and actionable for development teams. Rather than simply flagging potential issues with cryptic error codes. or technical jargon, AI breaks down each vulnerability's nature, potential impact, and remediation steps in terms that developers at all skill levels can quickly grasp. This democratization of security knowledge accelerates the remediation process, reduces the back-and-forth between security and development teams, and ultimately helps organizations ship more secure code faster:\n![Vulnerability Explain 1](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405091/rrcenbfazhhulmrp99yx.png) \u003Cp>\u003C/p>\n![Vulnerability Explain 2](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405094/b3o4lkexyn9lp41ib8ye.png) \u003Cp>\u003C/p>\n![Vulnerability Explain 3](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405095/y56wq8j5tg10t4dgbgfq.png) \u003Cp>\u003C/p>\n![Vulnerability Explain 4](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405090/mpc1mst4ydijpqdtlljm.png) \u003Cp>\u003C/p>\nGitLab Duo also provides [Agentic Chat](https://docs.gitlab.com/user/gitlab_duo_chat/agentic_chat/), which serves as an intelligent development companion for Rust applications, offering context-aware assistance throughout the entire development lifecycle. Developers can leverage its conversational interface to generate Rust code snippets, scaffold new Rust projects with appropriate `Cargo.toml` configurations, and much more.\n### GitLab Duo Vulnerability Resolution\nGitLab Duo [Vulnerability Resolution](https://docs.gitlab.com/user/application_security/vulnerabilities/#vulnerability-resolution) uses AI to automatically generate specific code fixes for detected security issues, dramatically reducing remediation time from hours to minutes. AI analyzes vulnerable code patterns and proposes precise patches tailored to the project's context, language, and dependencies while maintaining code functionality and style consistency. This automation is particularly effective for common vulnerabilities like SQL injection and cross-site scripting, enabling development teams to maintain velocity while significantly improving their security posture without disrupting the development workflow.\n![Duo Remediate Example 1](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405090/blpwclp68igekkecbyna.png) \u003Cp>\u003C/p>\n![Duo Remediate Example 2](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405090/avvncsspwyirtk14jdbe.png) \u003Cp>\u003C/p>\n### GitLab Duo Code Review\nGitLab Duo's [AI-powered code review](https://docs.gitlab.com/user/project/merge_requests/duo_in_merge_requests/#have-gitlab-duo-review-your-code) enhances the development process by providing intelligent, automated feedback on MRs before human reviewers engage. AI analyzes code changes for potential bugs, security vulnerabilities, performance issues, and adherence to coding standards, offering contextual suggestions and explanations that help developers catch issues early. By augmenting traditional peer reviews with consistent, immediate AI insights, this feature reduces the burden on senior developers, accelerates the review cycle, and ensures that basic quality checks are consistently applied across all code contributions, ultimately improving code quality while allowing human reviewers to focus on higher-level architectural and business logic concerns.\n![Duo Code Review 1](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405090/hewxrp2f22mf2fe4daaa.png) \u003Cp>\u003C/p>\n![Duo Code Review 2](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405091/qbw1gi0l4ngysnyjwoy8.png) \u003Cp>\u003C/p>\nThese are just some of the AI feautures that can be used to allow you to ship more secure Rust software faster than ever. To learn about all the GitLab AI features provided, visit the [GitLab Duo solution page](https://about.gitlab.com/gitlab-duo-agent-platform/).\n## The GitLab advantage for Rust\nGitLab provides a complete development platform that matches Rust's comprehensive approach:\n**Integrated Workflow:** - **Single Platform:** Code, CI/CD, security, and deployment in one place - **Rust-optimized:** Docker-based builds perfect for Rust's toolchain - **Security First:** Built-in security scanning - **Enterprise-ready:** Scalable infrastructure for large teams\n**Performance Benefits:** - **Efficient Caching:** Speeds up Rust's longer compilation times - **Parallel Builds:** Maximizes GitLab Runner efficiency - **Artifact Management:** Streamlined binary distribution\n**Developer Experience:** - **Familiar Tools:** Leverage standard Rust tooling (Cargo, Clippy, rustfmt) - **Visual Feedback:** Comprehensive dashboards and reporting - **Automation:** Reduces manual deployment and testing overhead - **GitLab Duo AI:** Ship more secure software faster with AI throughout the entire software development lifecycle\nGitLab's platform capabilities perfectly complement Rust's strengths, creating an ecosystem where safety, performance, and developer productivity converge. Rust applications on GitLab represent the cutting edge of software development—powered by a platform that understands and enhances the Rust development experience.\nTo learn more about the benefits of GitLab, sign up for a [free trial of GitLab Ultimate with Duo Enterprise](https://about.gitlab.com/free-trial/).\n","yml",{},"/en-us/blog/secure-rust-development-with-gitlab",{"config":30,"title":15,"description":16},{"noIndex":31},false,"en-us/blog/secure-rust-development-with-gitlab",[20,34,22],"open-source","XKS11Ye4pzS11BiIQwVhXAi1ZzgxHQVXOhA3BOpdsrc",{"data":37},{"logo":38,"freeTrial":43,"sales":48,"login":53,"items":58,"search":365,"minimal":396,"duo":415,"switchNav":424,"pricingDeployment":435},{"config":39},{"href":40,"dataGaName":41,"dataGaLocation":42},"/","gitlab logo","header",{"text":44,"config":45},"Get free trial",{"href":46,"dataGaName":47,"dataGaLocation":42},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free 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Where they diverge is what happens when your delivery needs get real: a monorepo with a dozen services, microservices spread across multiple repositories, deployments to dozens of environments, or a platform team trying to enforce standards without becoming a bottleneck.\n  \nGitLab's pipeline execution model was designed for that complexity. Parent-child pipelines, DAG execution, dynamic pipeline generation, multi-project triggers, merge request pipelines with merged results, and CI/CD Components each solve a distinct class of problems. Because they compose, understanding the full model unlocks something more than a faster pipeline. In this article, you'll learn about the five patterns where that model stands out, each mapped to a real engineering scenario with the configuration to match.\n  \nThe configs below are illustrative. The scripts use echo commands to keep the signal-to-noise ratio low. Swap them out for your actual build, test, and deploy steps and they are ready to use.\n\n\n## 1. Monorepos: Parent-child pipelines + DAG execution\n\n\nThe problem: Your monorepo has a frontend, a backend, and a docs site. Every commit triggers a full rebuild of everything, even when only a README changed.\n\n\nGitLab solves this with two complementary features: [parent-child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#parent-child-pipelines) (which let a top-level pipeline spawn isolated sub-pipelines) and [DAG execution via `needs`](https://docs.gitlab.com/ci/yaml/#needs) (which breaks rigid stage-by-stage ordering and lets jobs start the moment their dependencies finish).\n\n\nA parent pipeline detects what changed and triggers only the relevant child pipelines:\n\n```yaml\n# .gitlab-ci.yml\nstages:\n  - trigger\n\ntrigger-services:\n  stage: trigger\n  trigger:\n    include:\n      - local: '.gitlab/ci/api-service.yml'\n      - local: '.gitlab/ci/web-service.yml'\n      - local: '.gitlab/ci/worker-service.yml'\n    strategy: depend\n```\n\n\nEach child pipeline is a fully independent pipeline with its own stages, jobs, and artifacts. The parent waits for all of them via [strategy: depend](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#wait-for-downstream-pipeline-to-complete) so you get a single green/red signal at the top level, with full drill-down into each service's pipeline. This organizational separation is the bigger win for large teams: each service owns its pipeline config, changes in one cannot break another, and the complexity stays manageable as the repo grows.\n\n\nOne thing worth knowing: when you pass [multiple files to a single `trigger: include:`](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#combine-multiple-child-pipeline-configuration-files), GitLab merges them into a single child pipeline configuration. This means jobs defined across those files share the same pipeline context and can reference each other with `needs:`, which is what makes the DAG optimization possible. If you split them into separate trigger jobs instead, each would be its own isolated pipeline and cross-file `needs:` references would not work.\n\n\nCombine this with `needs:` inside each child pipeline and you get DAG execution. Your integration tests can start the moment the build finishes, without waiting for other jobs in the same stage.\n\n```yaml\n# .gitlab/ci/api-service.yml\nstages:\n  - build\n  - test\n\nbuild-api:\n  stage: build\n  script:\n    - echo \"Building API service\"\n\ntest-api:\n  stage: test\n  needs: [build-api]\n  script:\n    - echo \"Running API tests\"\n```\n\n\nWhy it matters: Teams with large monorepos typically report significant reductions in pipeline runtime after switching to DAG execution, since jobs no longer wait on unrelated work in the same stage. Parent-child pipelines add the organizational layer that keeps the configuration maintainable as the repo and team grow.\n\n![Local downstream pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738759/Blog/Imported/hackathon-fake-blog-post-s/image3_vwj3rz.png \"Local downstream pipelines\")\n\n## 2. Microservices: Cross-repo, multi-project pipelines\n\n\nThe problem: Your frontend lives in one repo, your backend in another. When the frontend team ships a change, they have no visibility into whether it broke the backend integration and vice versa.\n\n\nGitLab's [multi-project pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#multi-project-pipelines) let one project trigger a pipeline in a completely separate project and wait for the result. The triggering project gets a linked downstream pipeline right in its own pipeline view.\n\n\nThe frontend pipeline builds an API contract artifact and publishes it, then triggers the backend pipeline. The backend fetches that artifact directly using the [Jobs API](https://docs.gitlab.com/ee/api/jobs.html#download-a-single-artifact-file-from-specific-tag-or-branch) and validates it before allowing anything to proceed. If a breaking change is detected, the backend pipeline fails and the frontend pipeline fails with it.\n\n```yaml\n# frontend repo: .gitlab-ci.yml\nstages:\n  - build\n  - test\n  - trigger-backend\n\nbuild-frontend:\n  stage: build\n  script:\n    - echo \"Building frontend and generating API contract...\"\n    - mkdir -p dist\n    - |\n      echo '{\n        \"api_version\": \"v2\",\n        \"breaking_changes\": false\n      }' > dist/api-contract.json\n    - cat dist/api-contract.json\n  artifacts:\n    paths:\n      - dist/api-contract.json\n    expire_in: 1 hour\n\ntest-frontend:\n  stage: test\n  script:\n    - echo \"All frontend tests passed!\"\n\ntrigger-backend-pipeline:\n  stage: trigger-backend\n  trigger:\n    project: my-org/backend-service\n    branch: main\n    strategy: depend\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n```\n\n```yaml\n# backend repo: .gitlab-ci.yml\nstages:\n  - build\n  - test\n\nbuild-backend:\n  stage: build\n  script:\n    - echo \"All backend tests passed!\"\n\nintegration-test:\n  stage: test\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"pipeline\"\n  script:\n    - echo \"Fetching API contract from frontend...\"\n    - |\n      curl --silent --fail \\\n        --header \"JOB-TOKEN: $CI_JOB_TOKEN\" \\\n        --output api-contract.json \\\n        \"${CI_API_V4_URL}/projects/${FRONTEND_PROJECT_ID}/jobs/artifacts/main/raw/dist/api-contract.json?job=build-frontend\"\n    - cat api-contract.json\n    - |\n      if grep -q '\"breaking_changes\": true' api-contract.json; then\n        echo \"FAIL: Breaking API changes detected - backend integration blocked!\"\n        exit 1\n      fi\n      echo \"PASS: API contract is compatible!\"\n```\n\n\nA few things worth noting in this config. The `integration-test` job uses `$CI_PIPELINE_SOURCE == \"pipeline\"` to ensure it only runs when triggered by an upstream pipeline, not on a standalone push to the backend repo. The frontend project ID is referenced via `$FRONTEND_PROJECT_ID`, which should be set as a [CI/CD variable](https://docs.gitlab.com/ci/variables/) in the backend project settings to avoid hardcoding it.\n\n\nWhy it matters: Cross-service breakage that previously surfaced in production gets caught in the pipeline instead. The dependency between services stops being invisible and becomes something teams can see, track, and act on.\n\n\n![Cross-project pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738762/Blog/Imported/hackathon-fake-blog-post-s/image4_h6mfsb.png \"Cross-project pipelines\")\n\n\n## 3. Multi-tenant / matrix deployments: Dynamic child pipelines\n\n\nThe problem: You deploy the same application to 15 customer environments, or three cloud regions, or dev/staging/prod. Updating a deploy stage across all of them one by one is the kind of work that leads to configuration drift. Writing a separate pipeline for each environment is unmaintainable from day one.\n\n\nGitLab's [dynamic child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#dynamic-child-pipelines) let you generate a pipeline at runtime. A job runs a script that produces a YAML file, and that YAML becomes the pipeline for the next stage. The pipeline structure itself becomes data.\n\n\n```yaml\n# .gitlab-ci.yml\nstages:\n  - generate\n  - trigger-environments\n\ngenerate-config:\n  stage: generate\n  script:\n    - |\n      # ENVIRONMENTS can be passed as a CI variable or read from a config file.\n      # Default to dev, staging, prod if not set.\n      ENVIRONMENTS=${ENVIRONMENTS:-\"dev staging prod\"}\n      for ENV in $ENVIRONMENTS; do\n        cat > ${ENV}-pipeline.yml \u003C\u003C EOF\n      stages:\n        - deploy\n        - verify\n      deploy-${ENV}:\n        stage: deploy\n        script:\n          - echo \"Deploying to ${ENV} environment\"\n      verify-${ENV}:\n        stage: verify\n        script:\n          - echo \"Running smoke tests on ${ENV}\"\n      EOF\n      done\n  artifacts:\n    paths:\n      - \"*.yml\"\n    exclude:\n      - \".gitlab-ci.yml\"\n\n.trigger-template:\n  stage: trigger-environments\n  trigger:\n    strategy: depend\n\ntrigger-dev:\n  extends: .trigger-template\n  trigger:\n    include:\n      - artifact: dev-pipeline.yml\n        job: generate-config\n\ntrigger-staging:\n  extends: .trigger-template\n  needs: [trigger-dev]\n  trigger:\n    include:\n      - artifact: staging-pipeline.yml\n        job: generate-config\n\ntrigger-prod:\n  extends: .trigger-template\n  needs: [trigger-staging]\n  trigger:\n    include:\n      - artifact: prod-pipeline.yml\n        job: generate-config\n  when: manual\n```\n\n\nThe generation script loops over an `ENVIRONMENTS` variable rather than hardcoding each environment separately. Pass in a different list via a CI variable or read it from a config file and the pipeline adapts without touching the YAML. The trigger jobs use [extends:](https://docs.gitlab.com/ci/yaml/#extends) to inherit shared configuration from `.trigger-template`, so `strategy: depend` is defined once rather than repeated on every trigger job. Add a new environment by updating the variable, not by duplicating pipeline config. Add [when: manual](https://docs.gitlab.com/ci/yaml/#when) to the production trigger and you get a promotion gate baked right into the pipeline graph.\n\n\nWhy it matters: SaaS companies and platform teams use this pattern to manage dozens of environments without duplicating pipeline logic. The pipeline structure itself stays lean as the deployment matrix grows.\n\n\n![Dynamic pipeline](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738765/Blog/Imported/hackathon-fake-blog-post-s/image7_wr0kx2.png \"Dynamic pipeline\")\n\n\n## 4. MR-first delivery: Merge request pipelines, merged results, and workflow routing\n\n\nThe problem: Your pipeline runs on every push to every branch. Expensive tests run on feature branches that will never merge. Meanwhile, you have no guarantee that what you tested is actually what will land on `main` after a merge.\n\n\nGitLab has three interlocking features that solve this together:\n\n\n*   [Merge request pipelines](https://docs.gitlab.com/ci/pipelines/merge_request_pipelines/) run only when a merge request exists, not on every branch push. This alone eliminates a significant amount of wasted compute.\n\n*   [Merged results pipelines](https://docs.gitlab.com/ci/pipelines/merged_results_pipelines/) go further. GitLab creates a temporary merge commit (your branch plus the current target branch) and runs the pipeline against that. You are testing what will actually exist after the merge, not just your branch in isolation.\n\n*   [Workflow rules](https://docs.gitlab.com/ci/yaml/workflow/) let you define exactly which pipeline type runs under which conditions and suppress everything else. The `$CI_OPEN_MERGE_REQUESTS` guard below prevents duplicate pipelines firing for both a branch and its open MR simultaneously.\n\n\nWith those three working together, here is what a tiered pipeline looks like:\n\n```yaml\n# .gitlab-ci.yml\nworkflow:\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH && $CI_OPEN_MERGE_REQUESTS\n      when: never\n    - if: $CI_COMMIT_BRANCH\n    - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\nstages:\n  - fast-checks\n  - expensive-tests\n  - deploy\n\nlint-code:\n  stage: fast-checks\n  script:\n    - echo \"Running linter\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nunit-tests:\n  stage: fast-checks\n  script:\n    - echo \"Running unit tests\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nintegration-tests:\n  stage: expensive-tests\n  script:\n    - echo \"Running integration tests (15 min)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\ne2e-tests:\n  stage: expensive-tests\n  script:\n    - echo \"Running E2E tests (30 min)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nnightly-comprehensive-scan:\n  stage: expensive-tests\n  script:\n    - echo \"Running full nightly suite (2 hours)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\ndeploy-production:\n  stage: deploy\n  script:\n    - echo \"Deploying to production\"\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n      when: manual\n```\n\nWith this setup, the pipeline behaves differently depending on context. A push to a feature branch with no open MR runs lint and unit tests only. Once an MR is opened, the workflow rules switch from a branch pipeline to an MR pipeline, and the full integration and E2E suite runs against the merged result. Merging to `main` queues a manual production deployment. A nightly schedule runs the comprehensive scan once, not on every commit.\n\n\nWhy it matters: Teams routinely cut CI costs significantly with this pattern, not by running fewer tests, but by running the right tests at the right time. Merged results pipelines catch the class of bugs that only appear after a merge, before they ever reach `main`.\n\n\n![Conditional pipelines (within a branch with no MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738768/Blog/Imported/hackathon-fake-blog-post-s/image6_dnfcny.png \"Conditional pipelines (within a branch with no MR)\")\n\n\n\n![Conditional pipelines (within an MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738772/Blog/Imported/hackathon-fake-blog-post-s/image1_wyiafu.png \"Conditional pipelines (within an MR)\")\n\n\n\n![Conditional pipelines (on the main branch)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738774/Blog/Imported/hackathon-fake-blog-post-s/image5_r6lkfd.png \"Conditional pipelines (on the main branch)\")\n\n## 5. Governed pipelines: CI/CD Components\n\n\nThe problem: Your platform team has defined the right way to build, test, and deploy. But every team has their own `.gitlab-ci.yml` with subtle variations. Security scanning gets skipped. Deployment standards drift. Audits are painful.\n\n\nGitLab [CI/CD Components](https://docs.gitlab.com/ci/components/) let platform teams publish versioned, reusable pipeline building blocks. Application teams consume them with a single `include:` line and optional inputs — no copy-paste, no drift. Components are discoverable through the [CI/CD Catalog](https://docs.gitlab.com/ci/components/#cicd-catalog), which means teams can find and adopt approved building blocks without needing to go through the platform team directly.\n\n\nHere is a component definition from a shared library:\n\n```yaml\n# templates/deploy.yml\nspec:\n  inputs:\n    stage:\n      default: deploy\n    environment:\n      default: production\n---\ndeploy-job:\n  stage: $[[ inputs.stage ]]\n  script:\n    - echo \"Deploying $APP_NAME to $[[ inputs.environment ]]\"\n    - echo \"Deploy URL: $DEPLOY_URL\"\n  environment:\n    name: $[[ inputs.environment ]]\n```\nAnd here is how an application team consumes it:\n\n```yaml\n# Application repo: .gitlab-ci.yml\nvariables:\n  APP_NAME: \"my-awesome-app\"\n  DEPLOY_URL: \"https://api.example.com\"\n\ninclude:\n  - component: gitlab.com/my-org/component-library/build@v1.0.6\n  - component: gitlab.com/my-org/component-library/test@v1.0.6\n  - component: gitlab.com/my-org/component-library/deploy@v1.0.6\n    inputs:\n      environment: staging\n\nstages:\n  - build\n  - test\n  - deploy\n```\n\nThree lines of `include:` replace hundreds of lines of duplicated YAML. The platform team can push a security fix to `v1.0.7` and teams opt in on their own schedule — or the platform team can pin everyone to a minimum version. Either way, one change propagates everywhere instead of needing to be applied repo by repo.\n\n\nPair this with [resource groups](https://docs.gitlab.com/ci/resource_groups/) to prevent concurrent deployments to the same environment, and [protected environments](https://docs.gitlab.com/ci/environments/protected_environments/) to enforce approval gates - and you have a governed delivery platform where compliance is the default, not the exception.\n\n\nWhy it matters: This is the pattern that makes GitLab CI/CD scale across hundreds of teams. Platform engineering teams enforce compliance without becoming a bottleneck. Application teams get a fast path to a working pipeline without reinventing the wheel.\n\n\n![Component pipeline (imported jobs)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738776/Blog/Imported/hackathon-fake-blog-post-s/image2_pizuxd.png \"Component pipeline (imported jobs)\")\n\n## Putting it all together\n\nNone of these features exist in isolation. The reason GitLab's pipeline model is worth understanding deeply is that these primitives compose:\n\n*   A monorepo uses parent-child pipelines, and each child uses DAG execution\n\n*   A microservices platform uses multi-project pipelines, and each project uses MR pipelines with merged results\n\n*   A governed platform uses CI/CD components to standardize the patterns above across every team\n\n\nMost teams discover one of these features when they hit a specific pain point. The ones who invest in understanding the full model end up with a delivery system that actually reflects how their engineering organization works, not a pipeline that fights it.\n\n## Other patterns worth exploring\n\n\nThe five patterns above cover the most common structural pain points, but GitLab's pipeline model goes further. A few others worth looking into as your needs grow:\n\n\n*   [Review apps with dynamic environments](https://docs.gitlab.com/ci/environments/) let you spin up a live preview for every feature branch and tear it down automatically when the MR closes. Useful for teams doing frontend work or API changes that need stakeholder sign-off before merging.\n\n*   [Caching and artifact strategies](https://docs.gitlab.com/ci/caching/) are often the fastest way to cut pipeline runtime after the structural work is done. Structuring `cache:` keys around dependency lockfiles and being deliberate about what gets passed between jobs with [artifacts:](https://docs.gitlab.com/ci/yaml/#artifacts) can make a significant difference without changing your pipeline shape at all.\n\n*   [Scheduled and API-triggered pipelines](https://docs.gitlab.com/ci/pipelines/schedules/) are worth knowing about because not everything should run on a code push. Nightly security scans, compliance reports, and release automation are better modeled as scheduled or [API-triggered](https://docs.gitlab.com/ci/triggers/) pipelines with `$CI_PIPELINE_SOURCE` routing the right jobs for each context.\n\n## How to get started\n\nModern software delivery is complex. Teams are managing monorepos with dozens of services, coordinating across multiple repositories, deploying to many environments at once, and trying to keep standards consistent as organizations grow. GitLab's pipeline model was built with all of that in mind.\n\nWhat makes it worth investing time in is how well the pieces fit together. Parent-child pipelines bring structure to large codebases. Multi-project pipelines make cross-team dependencies visible and testable. Dynamic pipelines turn environment management into something that scales gracefully. MR-first delivery with merged results ensures confidence at every step of the review process. And CI/CD Components give platform teams a way to share best practices across an entire organization without becoming a bottleneck.\n\nEach of these features is powerful on its own, and even more so when combined. GitLab gives you the building blocks to design a delivery system that fits how your team actually works, and grows with you as your needs evolve.\n\n> [Start a free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/) to use pipeline logic today.\n\n## Read more\n\n*   [Variable and artifact sharing in GitLab parent-child pipelines](https://about.gitlab.com/blog/variable-and-artifact-sharing-in-gitlab-parent-child-pipelines/)\n*   [CI/CD inputs: Secure and preferred method to pass parameters to a pipeline](https://about.gitlab.com/blog/ci-cd-inputs-secure-and-preferred-method-to-pass-parameters-to-a-pipeline/)\n*   [Tutorial: How to set up your first GitLab CI/CD component](https://about.gitlab.com/blog/tutorial-how-to-set-up-your-first-gitlab-ci-cd-component/)\n*   [How to include file references in your CI/CD components](https://about.gitlab.com/blog/how-to-include-file-references-in-your-ci-cd-components/)\n*   [FAQ: GitLab CI/CD Catalog](https://about.gitlab.com/blog/faq-gitlab-ci-cd-catalog/)\n*   [Building a GitLab CI/CD pipeline for a monorepo the easy way](https://about.gitlab.com/blog/building-a-gitlab-ci-cd-pipeline-for-a-monorepo-the-easy-way/)\n*   [A CI/CD component builder's journey](https://about.gitlab.com/blog/a-ci-component-builders-journey/)\n*   [CI/CD Catalog goes GA: No more building pipelines from scratch](https://about.gitlab.com/blog/ci-cd-catalog-goes-ga-no-more-building-pipelines-from-scratch/)","5 ways GitLab pipeline logic solves real engineering problems","Learn how to scale CI/CD with composable patterns for monorepos, microservices, environments, and governance.",[718],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[105,722,22,723],"DevOps platform","features",{"featured":12,"template":13,"slug":725},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":727,"config":737},{"title":728,"description":729,"authors":730,"heroImage":732,"date":733,"body":734,"category":9,"tags":735},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[731],"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 difficult 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/)",[22,736,723],"product",{"featured":31,"template":13,"slug":738},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":740,"config":749},{"title":741,"description":742,"authors":743,"heroImage":745,"date":746,"category":9,"tags":747,"body":748},"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.",[744],"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",[20,619,21],"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-agent-platform/) 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":750,"featured":31,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":752},[753,767,778,790],{"id":754,"categories":755,"header":757,"text":758,"button":759,"image":764},"ai-modernization",[756],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":760,"config":761},"Get your AI maturity score",{"href":762,"dataGaName":763,"dataGaLocation":240},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":765},{"src":766},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":768,"categories":769,"header":770,"text":758,"button":771,"image":775},"devops-modernization",[736,565],"Are you just managing tools or shipping innovation?",{"text":772,"config":773},"Get your DevOps maturity score",{"href":774,"dataGaName":763,"dataGaLocation":240},"/assessments/devops-modernization-assessment/",{"config":776},{"src":777},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":779,"categories":780,"header":782,"text":758,"button":783,"image":787},"security-modernization",[781],"security","Are you trading speed for security?",{"text":784,"config":785},"Get your security maturity score",{"href":786,"dataGaName":763,"dataGaLocation":240},"/assessments/security-modernization-assessment/",{"config":788},{"src":789},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":791,"paths":792,"header":795,"text":796,"button":797,"image":802},"github-azure-migration",[793,794],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":798,"config":799},"See how GitLab compares to GitHub",{"href":800,"dataGaName":801,"dataGaLocation":240},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":803},{"src":777},{"header":805,"blurb":806,"button":807,"secondaryButton":812},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":808,"config":809},"Get your free trial",{"href":810,"dataGaName":47,"dataGaLocation":811},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":501,"config":813},{"href":51,"dataGaName":52,"dataGaLocation":811},1776442985323]