GitLab CI can tell you when your project is using software with known security holes, but it’s not as simple as just running a command; it’s about how the pipeline is structured to allow that check.
Let’s see it in action. Imagine a simple .gitlab-ci.yml file:
stages:
- build
- test
- scan
build_app:
stage: build
script:
- echo "Building the application..."
- echo "Dependencies installed: requests==2.28.1, Flask==2.1.2" # Example dependencies
test_app:
stage: test
script:
- echo "Running unit tests..."
- python -m unittest discover
scan_dependencies:
stage: scan
image: registry.gitlab.com/security-products/dependency-scanning:latest
script:
- echo "Scanning for vulnerable dependencies..."
- /analyzer run
artifacts:
reports:
dependency_scanning: gl-dependency-scanning-report.json
When this pipeline runs, the scan_dependencies job kicks off. It uses a special GitLab-provided Docker image (registry.gitlab.com/security-products/dependency-scanning:latest) that contains the dependency scanning tools. The /analyzer run command within this job is the magic. It inspects your project’s dependencies, queries a database of known vulnerabilities (like the National Vulnerability Database - NVD), and flags any matches. The results are then saved in a gl-dependency-scanning-report.json file, which GitLab understands and displays in the Merge Request or Pipeline view.
The core problem this solves is the "unknown unknown" of your software supply chain. You might be using a library for its functionality, unaware that a critical security flaw was discovered in a recent version. Manually tracking every dependency and its CVEs is a Sisyphean task. GitLab CI’s dependency scanning automates this, integrating security checks directly into your development workflow.
Internally, the dependency-scanning analyzer works by first identifying how your project manages dependencies. It looks for manifest files like requirements.txt (Python), package.json (Node.js), pom.xml (Java), Gemfile (Ruby), and so on. Once identified, it extracts the list of dependencies and their exact versions. For each dependency, it queries vulnerability databases. The report is then generated in a standardized JSON format that GitLab’s frontend can parse to highlight vulnerabilities, provide links to advisories, and even suggest remediation steps.
The image keyword in the CI job is crucial. It specifies the Docker image that contains the scanning tools. If this image is outdated, you won’t be detecting the latest vulnerabilities. You can find the latest stable image tag on GitLab’s Container Registry. For example, using registry.gitlab.com/security-products/dependency-scanning:15.9.2 would pin you to a specific version for reproducibility.
A common point of confusion is that the scanner needs to see your dependencies. If your build process installs dependencies in a way that isn’t captured by the scanner’s default behavior (e.g., installing into a custom path not checked by the analyzer), you might get a false negative. You can configure the scanner using environment variables to point it to specific directories or use different analyzers for more complex setups. For instance, for Python projects, you might need to ensure that pip install -r requirements.txt is run before the dependency scanning job in a preceding stage, or that the PYTHONPATH is correctly set for the scanner to find your installed packages.
The most surprising thing is how the vulnerability data is actually aggregated. It’s not just one monolithic database; the analyzers pull data from multiple sources, including NVD, GitHub Security Advisories, and sometimes vendor-specific advisories, then deduplicate and correlate them to provide a more comprehensive picture. This means a vulnerability might be flagged even if it’s not yet in the NVD, if it’s present in another source the analyzer consults.
The next frontier is understanding how to integrate custom vulnerability feeds or how to suppress known false positives effectively.