[{"data":1,"prerenderedAt":866},["ShallowReactive",2],{"/en-us/blog/introducing-spamcheck-data-driven-anti-abuse":3,"navigation-en-us":42,"banner-en-us":452,"footer-en-us":462,"blog-post-authors-en-us-Jayson Salazar|Alexander Dietrich|Alex Groleau|Ethan Urie|Juliet Wanjohi":704,"blog-related-posts-en-us-introducing-spamcheck-data-driven-anti-abuse":764,"blog-promotions-en-us":804,"next-steps-en-us":856},{"id":4,"title":5,"authorSlugs":6,"body":12,"categorySlug":13,"config":14,"content":18,"description":12,"extension":31,"isFeatured":16,"meta":32,"navigation":33,"path":34,"publishedDate":28,"seo":35,"stem":39,"tagSlugs":40,"__hash__":41},"blogPosts/en-us/blog/introducing-spamcheck-data-driven-anti-abuse.yml","Introducing Spamcheck Data Driven Anti Abuse",[7,8,9,10,11],"jayson-salazar","alexander-dietrich","alex-groleau","ethan-urie","juliet-wanjohi",null,"security",{"slug":15,"featured":16,"template":17},"introducing-spamcheck-data-driven-anti-abuse",false,"BlogPost",{"title":19,"description":20,"authors":21,"heroImage":27,"date":28,"body":29,"category":13,"tags":30},"Introducing Spamcheck: A data-driven, anti-abuse engine","How we built, tested and deployed a new tool on GitLab that fights spam and abuse.",[22,23,24,25,26],"Jayson Salazar","Alexander Dietrich","Alex Groleau","Ethan Urie","Juliet Wanjohi","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749669841/Blog/Hero%20Images/lionello-delpiccolo-unsplash.jpg","2021-08-19","\n\nSpam and abuse are a very real concern for us at GitLab – and likely every company providing services at scale. That's why our [Trust and Safety team](https://handbook.gitlab.com/handbook/security/security-operations/trustandsafety) works hard every day to detect, mitigate, and lessen the effects of spam. Back in October 2020, the GitLab Security team [detailed some of the ways they combat spam and abuse](/blog/how-we-work-to-detect-and-mitigate-spam/) and referenced new approaches we were developing and testing to help us better detect and prevent spam.\n\n**We're excited to introduce Spamcheck, our new anti-spam engine. Spamcheck has been enabled for all projects on GitLab.com, which runs 14.1, and will be targeting inclusion of Spamcheck in the [14.6 release for our GitLab self-managed customers](https://gitlab.com/gitlab-org/omnibus-gitlab/-/issues/6259).**\n\nThis tool was designed, tested and integrated by our GitLab [Security Automation](https://handbook.gitlab.com/handbook/security/security-engineering/automation/) team (with amazing partnership from our Trust and Safety team and our [Create](https://handbook.gitlab.com/handbook/engineering/development/dev/#create) and [Plan](https://handbook.gitlab.com/handbook/product/categories/#plan-stage) Engineering Development teams here at GitLab) with the purpose of improving GitLab's resilience to spam and abuse – in respect to both user experience and infrastructure robustness. We're continuing to develop and mature Spamcheck, but this two-part blog series will detail the development of this tool thus far (part one) and the [tech stack behind it (part two)](https://about.gitlab.com/blog/deep-dive-tech-stack-behind-spamcheck/).\n\n## Testing, prototyping, and more testing\n\nAt its core, spam prevention is a data-driven and a product-driven process. It is data-driven because information about each artifact, such as issues and users, must be captured, processed, and acted upon to actually prevent spam and abuse. On the other hand, GitLab itself must be iteratively improved on in order to support the consumption of data necessary for abuse prevention, the enactment of moderation tasks, and much more.\n\nWith this process in mind, one of our first steps was to create a spam testbed. The spam testbed was where we instrumented existing product and infrastructure components to passively extract spam from the most affected namespaces into separate GitLab instances – this was our laboratory. This approach allowed us to try different spam-handling methods and analyse the effect on the product's behavior, measuring and identifying the components and improvements we would need further down the road. Our testbed allowed us to understand what parts of the codebase we could reuse, what Spamcheck's future architecture should look like, and what development process would allow us to move fast without too much breakage.\n\nAfter we had a testbed that architecturally resembled what we believed Spamcheck should look like, we started working on a prototype that leveraged a technology stack that would provide the stability, flexibility, and performance we needed – but this was no simple feat. There were many moving parts that hindered a frictionless development experience. Our toolchain was convoluted, due in part to [gRPC](https://en.wikipedia.org/wiki/GRPC), [ProtoBuf](https://en.wikipedia.org/wiki/Protocol_Buffers) definitions, and [GoLang's](https://en.wikipedia.org/wiki/Go_(programming_language)) somewhat immature tooling – but also because of the complexity of our [GitLab development kit](https://gitlab.com/gitlab-org/gitlab-development-kit/-/blob/main/README.md). This toolchain complexity required a robust build and set-up process that would work locally for our developers but that could also be reused in our CI/CD pipelines. Our next blog post will dive deeper into our tech stack, so stay tuned!\n\n## Building the tool\n\nWe developed Spamcheck in a loosely coupled manner from the get-go, only relying on data coming from production to learn, prototype, and build out the service. However, as we gained confidence in our approach, we started getting closer to production again, integrating Spamcheck as one would any other service. This process required defining and introducting features carefully, e.g., gRPC-client code, UI components for handling Spamcheck's service URL and API keys, and also the definition of milestones and safeguards we wanted in place before completely integrating with production.\n\nOnce we had those features in place, we deployed Spamcheck to GitLab's staging environment. In staging, we carefully measured how GitLab and all monitored namespaces interacted with Spamcheck, and improved on its progressive roll-out capabilities, such as project and email-address filtering to improve quality assurance and control how many projects and users would be analyzed by the service.\n\n## Deploying to production, iteratively\n\nAfter successfully delivering Spamcheck to staging and processing more data, we started planning Spamcheck's deployment to production. Since Spamcheck was a new service, we wanted to control the volume of incoming requests for predictions to ensure that our service remained performant and didn't slow down issue creation on GitLab.com.\n\nWe started with a small scope of projects that we checked for spam while assessing the accuracy of our predictions and building out a project allowlist/denylist feature. The integration with GitLab involved refactoring our existing spam handling code to take Spamcheck's responses into account. In this case, we used Akismet alongside Spamcheck and applied all the spam verdicts to decide what GitLab should block and what it should allow.\n\nWe started operating in monitoring mode, looking to avoid disruptions and other surprises as we moved to production. This meant verdicts rendered by Spamcheck would still be logged for the purposes of data collection, but ultimately no action would be taken based on this output. This helped us fine-tune our development and delivery as well as our detection and measurement processes. Once we were comfortable with the performance and accuracy, we increased scope and progressively expanded our list of monitored projects to all public projects on GitLab.com.\n\n## Using data to inform and improve\n\nWe aimed for Spamcheck's creation to be a heavily data-informed process and we'd encourage anyone implementing similar features to do the same.\n\nWhat this means is mostly logs, logs, and more logs.\n\nDuring design, prototyping, and now in production, we logged anything we could so we can measure, learn, and iterate. In the beginning, we measured things like:\n\n- The number of issues created\n- Quantity of expected modification events\n- How many spammy issues we were seeing, including their cadence and characterizing their content\n- Assessed the most affected namespaces, and more.\n\nAs we progressed, we also measured the effectiveness of Spamcheck vs. [Akismet](https://akismet.com/) – which is another spam protection service – including their response times, peaks of spam activity, etc. This allowed us to move forward with confidence and helped us build tools like dashboards to enable information sharing with other teams, and helped us double-check assumptions and continually iterate on Spamcheck.\n\nEarly analysis of our metrics indicate we're outperforming Akismet when GitLab.com is being attacked during spam waves. However our false positive rate is slightly higher than Akismet during normal day-to-day operations, which triggers a reCAPTCHA in our frontend. We're now working on reducing our false positive rate by improving our ML models, automating training processes, and setting static rules. We'll provide a more detailed analysis of our performance versus Akismet in the second blog post.\n\n## Cross-organizational collaboration\n\nBuilding, testing, iterating on, and implementing Spamcheck was truly a collaborative effort between our Security Automation team and multiple teams across GitLab. We'd like to thank [Roger Ostrander](/company/team/#rostrander) from our Trust and Safety team, [Chad Wooley](/company/team/#cwoolley-gitlab) from our Create: Editor team, and [Charlie Ablett](/company/team/#cablett) from our Plan: Product Planning team and [Stan Hu](/company/team/#stanhu), Engineering Fellow, for their many contributions to the successful launch of Spamcheck.\n\nIn our next blog, we'll dive deep into the technology stack that supports Spamcheck, how we selected some of the supporting infrastructure and frameworks, provide some deeper analysis into the tool's performance and what lies ahead in development.\n\nCover image by [Lionello DelPiccolo](https://unsplash.com/@liodp?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on 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vulnerability noise at scale with auto-dismiss policies","Learn how to cut through scanner noise and focus on the vulnerabilities that matter most with GitLab security, including use cases and templates.",[770],"Grant Hickman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1774375772/kpaaaiqhokevxxeoxvu0.png","2026-03-25",[13,774,569,775,776],"tutorial","features","product","Security scanners are essential, but not every finding requires action. Test code, vendored dependencies, generated files, and known false positives create noise that buries the vulnerabilities that actually matter. Security teams waste hours manually dismissing the same irrelevant findings across projects and pipelines. They experience slower triage, alert fatigue, and developer friction that undermines adoption of security scanning itself.\n\nGitLab's auto-dismiss vulnerability policies let you codify your triage decisions once and apply them automatically on every default-branch pipeline. Define criteria based on file path, directory, or vulnerability identifier (CVE, CWE), choose a dismissal reason, and let GitLab handle the rest.\n\n## Why auto-dismiss?\nAuto-dismiss vulnerability policies enable security teams to:\n- **Eliminate triage noise**: Automatically dismiss findings in test code, vendored dependencies, and generated files.\n- **Enforce decisions at scale**: Apply policies centrally to dismiss known false positives across your entire organization.\n- **Maintain audit transparency**: Every auto-dismissed finding includes a documented reason and links back to the policy that triggered it.\n- **Preserve the record**: Unlike scanner exclusions, dismissed vulnerabilities remain in your report, so you can revisit decisions if conditions change.\n\n## How auto-dismiss policies work\n\n1. **Define your policy** in a vulnerability management policy YAML file. Specify match criteria (file path, directory, or identifier) and a dismissal reason.\n\n2. **Merge and activate.** Create the policy via **Secure > Policies > New  policy > Vulnerability management policy**. Merge the MR to enable it.\n3. **Run your pipeline.** On every default-branch pipeline, matching vulnerabilities are automatically set to \"Dismissed\" with the specified reason. Up to 1,000 vulnerabilities are processed per run.\n4. **Measure the impact.** Filter your vulnerability report by status \"Dismissed\" to see exactly what was cleaned up and validate that the right findings are being handled.\n\n## Use cases with ready-to-use configurations\n\nEach example below includes a policy configuration you can copy, customize, and apply immediately.\n\n### 1. Dismiss test code vulnerabilities\n\nSAST and dependency scanners flag hardcoded credentials, insecure fixtures, and dev-only dependencies in test directories. These are not production risks.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss test code vulnerabilities\"\n    description: \"Auto-dismiss findings in test directories\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"test/**/*\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"tests/**/*\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"spec/**/*\"\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"__tests__/*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: used_in_tests\n\n```\n\n### 2. Dismiss vendored and third-party code\n\nVulnerabilities in `vendor/`, `third_party/`, or checked-in `node_modules` are managed upstream and not actionable for your team.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss vendored dependency findings\"\n    description: \"Findings in vendored code are managed upstream\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"vendor/*\"\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"third_party/*\"\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"vendored/*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: not_applicable\n\n```\n\n### 3. Dismiss known false positive CVEs\n\nCertain CVEs are repeatedly flagged but don't apply to your usage context. Teams dismiss these manually every time they appear. Replace the example CVEs below with your own.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss known false positive CVEs\"\n    description: \"CVEs confirmed as false positives for our environment\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2023-44487\"\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2024-29041\"\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2023-26136\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: false_positive\n\n```\n\n### 4. Dismiss generated and auto-created code\n\nProtobuf, gRPC, OpenAPI generators, and ORM scaffolding tools produce files with flagged patterns that cannot be patched by your team.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss generated code findings\"\n    description: \"Generated files are not authored by us\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"generated/*\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"**/*.pb.go\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"**/*.generated.*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: not_applicable\n\n```\n\n### 5. Dismiss infrastructure-mitigated vulnerabilities\n\nVulnerability classes like XSS (CWE-79) or SQL injection (CWE-89) that are already addressed by WAF rules or runtime protection. Only use this when mitigating controls are verified and consistently enforced.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss CWEs mitigated by WAF\"\n    description: \"XSS and SQLi mitigated by WAF rules\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CWE-79\"\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CWE-89\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: mitigating_control\n\n```\n\n### 6. Dismiss CVE families across your organization\n\nA wave of related CVEs for a widely-used library your team has assessed? Apply at the group level to dismiss them across dozens of projects. The wildcard pattern (e.g., `CVE-2021-44*`) matches all CVEs with that prefix.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Accept risk for log4j CVE family\"\n    description: \"Log4j CVEs mitigated by version pinning and WAF\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2021-44*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: acceptable_risk\n\n```\n\n## Quick reference\n\n| Parameter | Details |\n|-----------|---------|\n| **Criteria types** | `file_path` (glob patterns, e.g., `test/**/*`), `directory` (e.g., `vendor/*`), `identifier` (CVE/CWE with wildcards, e.g., `CVE-2023-*`) |\n| **Dismissal reasons** | `acceptable_risk`, `false_positive`, `mitigating_control`, `used_in_tests`, `not_applicable` |\n| **Criteria logic** | Multiple criteria within a rule = AND (must match all). Multiple rules within a policy = OR (match any). |\n| **Limits** | 3 criteria per rule, 5 rules per policy, 5 policies per security policy project. Vulnerabilty management policy actions process 1000 vulnerabilities per pipeline run in the target project, until all matching vulnerabilities are processed. |\n| **Affected statuses** | Needs triage, Confirmed |\n| **Scope** | Project-level or group-level (group-level applies across all projects) |\n\n## Getting started\nHere's how to get started with auto-dismiss policies:\n\n1. **Identify the noise.** Open your vulnerability report and sort by \"Needs triage.\" Look for patterns: test files, vendored code, the same CVE across projects.\n\n2. **Pick a scenario.** Start with whichever use case above accounts for the most findings.\n\n3. **Record your baseline.** Note the number of \"Needs triage\" vulnerabilities before creating a policy.\n\n4. **Create and enable.** Navigate to **Secure > Policies > New policy > Vulnerability management policy**. Paste the configuration from the use case above, then merge the MR.\n\n5. **Validate results.** After the next default-branch pipeline, filter by status \"Dismissed\" to confirm the right findings were handled.\n\nFor full configuration details, see the [vulnerability management policy documentation](https://docs.gitlab.com/user/application_security/policies/vulnerability_management_policy/#auto-dismiss-policies).\n\n> Ready to take control of vulnerability noise? [Start a free GitLab Ultimate trial](https://about.gitlab.com/free-trial/) and configure your first auto-dismiss policy today.\n",{"slug":779,"featured":33,"template":17},"auto-dismiss-vulnerability-management-policy",{"content":781,"config":790},{"title":782,"description":783,"authors":784,"heroImage":786,"date":787,"body":788,"category":13,"tags":789},"GitLab 18.10 brings AI-native triage and remediation ","Learn about GitLab Duo Agent Platform capabilities that cut noise, surface real vulnerabilities, and turn findings into proposed fixes.",[785],"Alisa Ho","https://res.cloudinary.com/about-gitlab-com/image/upload/v1773843921/rm35fx4gylrsu9alf2fx.png","2026-03-19","GitLab 18.10 introduces new AI-powered security capabilities focused on improving the quality and speed of vulnerability management. Together, these features can help reduce the time developers spend investigating false positives and bring automated remediation directly into their workflow, so they can fix vulnerabilities without needing to be security experts.\n\nHere is what’s new:\n\n* [**Static Application Security Testing (SAST) false positive detection**](https://docs.gitlab.com/user/application_security/vulnerabilities/false_positive_detection/) **is now generally available.** This flow uses an LLM for agentic reasoning to determine the likelihood that a vulnerability is a false positive or not, so security and development teams can focus on remediating critical vulnerabilities first.  \n* [**Agentic SAST vulnerability resolution**](https://docs.gitlab.com/user/application_security/vulnerabilities/agentic_vulnerability_resolution/) **is now in beta.** Agentic SAST vulnerability resolution automatically creates a merge request with a proposed fix for verified SAST vulnerabilities, which can shorten time to remediation and reduce the need for deep security expertise.  \n* [**Secret false positive detection**](https://docs.gitlab.com/user/application_security/vulnerabilities/secret_false_positive_detection/) **is now in beta.** This flow brings the same AI-powered noise reduction to secret detection, flagging dummy and test secrets to save review effort.\n\nThese flows are available to GitLab Ultimate customers using GitLab Duo Agent Platform. \n\n## Cut triage time with SAST false positive detection\n\nTraditional SAST scanners flag every suspicious code pattern they find, regardless of whether code paths are reachable or frameworks already handle the risk. Without runtime context, they cannot distinguish a real vulnerability from safe code that just looks dangerous.\n\nThis means developers could spend hours investigating findings that turn out to be false positives. Over time, that can erode confidence in the report and slow down the teams responsible for fixing real risks.\n\nAfter each SAST scan, GitLab Duo Agent Platform automatically analyzes new critical and high severity findings and attaches:\n\n* A confidence score indicating how likely the finding is to be a false positive  \n* An AI-generated explanation describing the reasoning  \n* A visual badge that makes “Likely false positive” versus “Likely real” easy to scan in the UI\n\nThese findings appear in the [Vulnerability Report](https://docs.gitlab.com/user/application_security/vulnerability_report/), as shown below. You can filter the report to focus on findings marked as “Not false positive” so teams can spend their time addressing real vulnerabilities instead of sifting through noise.\n\n![Vulnerability report](https://res.cloudinary.com/about-gitlab-com/image/upload/v1773844787/i0eod01p7gawflllkgsr.png)\n\n\nGitLab Duo Agent Platform's assessment is a recommendation. You stay in control of every false positive to determine if it is valid, and you can audit the agent's reasoning at any time to build confidence in the model. \n\n\n## Turn vulnerabilities into automated fixes\n\nKnowing that a vulnerability is real is only half the work.  Remediation still requires understanding the code path, writing a safe patch, and making sure nothing else breaks.\n\nIf the vulnerability is identified as likely not be a false positive by the SAST false positive detection flow, the Agentic SAST vulnerability resolution flow automatically:\n\n1. Reads the vulnerable code and surrounding context from your repository  \n2. Generates high-quality proposed fixes  \n3. Validates fixes through automated testing   \n4. Opens a merge request with a proposed fix that includes:  \n   * Concrete code changes  \n   * A confidence score  \n   * An explanation of what changed and why\n\nIn this demo, you’ll see how GitLab can automatically take a SAST vulnerability all the way from detection to a ready-to-review merge request. Watch how the agent reads the code, generates and validates a fix, and opens an MR with clear, explainable changes so developers can remediate faster without being security experts.\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1174573325?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"GitLab 18.10 AI SAST False Positive Auto Remediation\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\nAs with any AI-generated suggestion, you should review the proposed merge request carefully before merging.\n\n## Surface real secrets\n\nSecret detection is only useful if teams trust the results. When reports are full of test credentials, placeholder values, and example tokens, developers may waste time reviewing noise instead of fixing real exposures. That can slow remediation and decrease confidence in the scan.\n\nSecret false positive detection helps teams focus on the secrets that matter so they can reduce risk faster. When it runs on the default branch, it will automatically:\n\n1. Analyze each finding to spot likely test credentials, example values, and dummy secrets  \n2. Assign a confidence score for whether the finding is a real risk or a likely false positive  \n3. Generate an explanation for why the secret is being treated as real or noise  \n4. Add a badge in the Vulnerability Report so developers can see the status at a glance\n\nDevelopers can also trigger this analysis manually from the Vulnerability Report by selecting **“Check for false positive”** on any secret detection finding, helping them clear out findings that do not pose risk and focus on real secrets sooner.\n\n## Try AI-powered security today\n\nGitLab 18.10 introduces capabilities that cover the full vulnerability workflow, from cutting false positive noise in SAST and secret detection to automatically generating merge requests with proposed fixes.\n\nTo see how AI-powered security can help cut review time and turn findings into ready-to-merge fixes, [start a free trial of GitLab Duo Agent Platform today](https://about.gitlab.com/gitlab-duo-agent-platform/?utm_medium=blog&utm_source=blog&utm_campaign=eg_global_x_x_security_en_).",[776,13,775],{"featured":16,"template":17,"slug":791},"gitlab-18-10-brings-ai-native-triage-and-remediation",{"content":793,"config":802},{"title":794,"description":795,"authors":796,"tags":798,"heroImage":799,"category":13,"date":800,"body":801},"A complete guide to GitLab Container Scanning","Explore GitLab's various container scanning methods and learn how to secure containers at every lifecycle stage.",[797],"Fernando Diaz",[13,774],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-03-05","Container vulnerabilities don't wait for your next deployment. They can emerge at any\npoint, including when you build an image or while containers run in production.\nGitLab addresses this reality with multiple container scanning approaches, each designed\nfor different stages of your container lifecycle.\n\nIn this guide, we'll explore the different types of container scanning GitLab offers,\nhow to enable each one, and common configurations to get you started.\n\n## Why container scanning matters\n\nSecurity vulnerabilities in container images create risk throughout your application\nlifecycle. Base images, OS packages, and application dependencies can all harbor\nvulnerabilities that attackers actively exploit. Container scanning detects these risks\nearly, before they reach production, and provides remediation paths when available.\n\nContainer scanning is a critical component of Software Composition Analysis (SCA),\nhelping you understand and secure the external dependencies your containerized\napplications rely on.\n\n## The five types of GitLab Container Scanning\n\nGitLab offers five distinct container scanning approaches, each serving a specific\npurpose in your security strategy.\n\n\n### 1. Pipeline-based Container Scanning\n\n* What it does: Scans container images during your CI/CD pipeline execution,\ncatching vulnerabilities before deployment\n\n* Best for: Shift-left security, blocking vulnerable images from reaching production \n\n* Tier availability: Free, Premium, and Ultimate (with enhanced features in Ultimate)  \n\n* [Documentation](https://docs.gitlab.com/user/application_security/container_scanning/)\n\n\nGitLab uses the Trivy security scanner to analyze container images for\nknown vulnerabilities. When your pipeline runs, the scanner examines your images\nand generates a detailed report.\n\n\n#### How to enable pipeline-based Container Scanning \n\n**Option A: Preconfigured merge request**  \n\n* Navigate to **Secure > Security configuration** in your project.\n* Find the \"Container Scanning\" row.\n* Select **Configure with a merge request**.\n* This automatically creates a merge request with the necessary configuration.  \n\n**Option B: Manual configuration**  \n\n* Add the following to your `.gitlab-ci.yml`:\n\n```yaml\ninclude:\n  - template: Jobs/Container-Scanning.gitlab-ci.yml\n```  \n\n#### Common configurations\n\n**Scan a specific image:**\n\nTo scan a specific image, overwrite the `CS_IMAGE` variable in the `container_scanning` job.\n\n```yaml\ninclude:\n  - template: Jobs/Container-Scanning.gitlab-ci.yml\n\ncontainer_scanning:\n  variables:\n    CS_IMAGE: myregistry.com/myapp:latest\n```\n\n**Filter by severity threshold:**\n\nTo only find vulnerabilities with a certain severity criteria, overwrite the\n`CS_SEVERITY_THRESHOLD` variable in the `container_scanning` job. In the example\nbelow, only vulnerabilities with a severity of **High** or greater will be displayed.\n\n\n```yaml\ninclude:\n  - template: Jobs/Container-Scanning.gitlab-ci.yml\n\ncontainer_scanning:\n  variables:\n    CS_SEVERITY_THRESHOLD: \"HIGH\"\n```\n\n#### Viewing vulnerabilities in a merge request\n\nViewing Container Scanning vulnerabilities directly within merge requests makes security\nreviews seamless and efficient. Once Container Scanning is configured in your CI/CD\npipeline, GitLab automatically display detected vulnerabilities in the merge request's\n[Security widget](https://docs.gitlab.com/user/project/merge_requests/widgets/#application-security-scanning). \n\n\n![Container Scanning vulnerabilities displayed in MR](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772547514/lt6elcq6jexdhqatdy8l.png \"Container Scanning vulnerabilities displayed in MR\")\n\n\n\n* Navigate to any merge request and scroll to the \"Security Scanning\" section to see a summary of\nnewly introduced and existing vulnerabilities found in your container images.\n\n* Click on a **Vulnerability** to access detailed information about the finding, including severity level,\naffected packages, and available remediation guidance.\n\n\n![GitLab Security View details in MR](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772547514/hplihdlekc11uvpfih1p.png)\n\n\n\n![GitLab Security View details in MR](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772547513/jnxbe7uld8wfeezboifs.png \"Container Scanning vulnerability details in MR\")\n\n\nThis visibility enables developers and security teams to catch and address container\nvulnerabilities before they reach production, making security an integral part of your\ncode review process rather than a separate gate.\n\n\n#### Viewing vulnerabilities in Vulnerability Report\n\nBeyond merge request reviews, GitLab provides a centralized\n[Vulnerability Report](https://docs.gitlab.com/user/application_security/vulnerability_report/) that gives security teams comprehensive visibility across all Container Scanning findings in your project.\n\n\n![Vulnerability Report sorted by Container Scanning](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772547524/gagau279fzfgjpnvipm5.png \"Vulnerability Report sorted by Container Scanning\")\n\n\n* Access this report by navigating to **Security & Compliance > Vulnerability Report** in your\nproject sidebar.\n\n* Here you'll find an aggregated view of all container vulnerabilities detected across your branches, with powerful filtering options to sort by severity, status, scanner type, or specific container images.\n\n* You can click on a vulnerabilty to access its Vulnerablity page.\n\n\n![Vulnerability page - 1st view](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772547520/e1woxupyoajhrpzrlylj.png)\n\n\n![Vulnerability page - 2nd view](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772547521/idzcftcgjc8eryixnbjn.png)\n\n\n![Vulnerability page - 3rd view](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772547522/mbbwbbprtf9anqqola10.png \"Vunerability Details for a Container Scanning vulnerability\")\n\n\n[Vulnerability Details](https://docs.gitlab.com/user/application_security/vulnerabilities/)\nshows exactly which container images and layers are impacted, making it easier to trace the\nvulnerability back to its source. You can assign vulnerabilities to team members, change\ntheir status (detected, confirmed, resolved, dismissed), add comments for collaboration,\nand link related issues for tracking remediation work.\n\nThis workflow transforms vulnerability management from a spreadsheet exercise into an integrated part of your development process, ensuring that container security findings are tracked, prioritized, and resolved systematically.\n\n#### View the Dependency List\n\nGitLab's [Dependency List](https://docs.gitlab.com/user/application_security/dependency_list/)\nprovides a comprehensive software bill of materials (SBOM) that catalogs every component within\nyour container images, giving you complete transparency into your software supply chain.\n\n* Navigate to **Security & Compliance > Dependency List** to access an inventory of all packages,\nlibraries, and dependencies detected by Container Scanning across your project.\n\n* This view is invaluable for understanding what's actually running inside your containers, from base OS\npackages to application-level dependencies.\n\n\n![GitLab Dependency List](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772547513/vjg6dk3nhajqamplroji.png \"GitLab Dependency List (SBOM)\")\n\n\nYou can filter the list by package manager, license type, or vulnerability status to quickly\nidentify which components pose security risks or compliance concerns. Each dependency entry\nshows associated vulnerabilities, allowing you to understand security issues in the context\nof your actual software components rather than as isolated findings.\n\n\n### 2. Container Scanning for Registry\n\n* What it does: Automatically scans images pushed to your GitLab Container Registry\nwith the `latest` tag\n\n* Best for: Continuous monitoring of registry images without manual pipeline triggers  \n\n* Tier availability: Ultimate only \n\n* [Documentation](https://docs.gitlab.com/user/application_security/container_scanning/#container-scanning-for-registry) \n\n\nWhen you push a container image tagged `latest`, GitLab's security policy bot\nautomatically triggers a scan against the default branch. Unlike pipeline-based\nscanning, this approach works with Continuous Vulnerability Scanning to monitor\nfor newly published advisories.\n\n#### How to enable Container Scanning for Registry\n\n1. Navigate to **Secure > Security configuration**.\n2. Scroll to the **Container Scanning For Registry** section.\n3. Toggle the feature on.\n\n![Container Scanning for Registry](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772547512/vntrlhtmsh1ecnwni5ji.png \"Toggle for Container Scanning for Registry\")\n\n#### Prerequisites\n\n- Maintainer role or higher in the project\n- Project must not be empty (requires at least one commit on the default branch)\n- Container Registry notifications must be configured\n- Package Metadata Database must be configured (enabled by default on GitLab.com)\n\nVulnerabilities appear under the **Container Registry vulnerabilities** tab in your\nVulnerability Report.\n\n\n### 3. Multi-Container Scanning\n\n* What it does: Scans multiple container images in parallel within a single pipeline \n* Best for: Microservices architectures and projects with multiple container images  \n* Tier availability: Free, Premium, and Ultimate (currently in Beta)  \n* [Documentation](https://docs.gitlab.com/user/application_security/container_scanning/multi_container_scanning/) \n\nMulti-Container Scanning uses dynamic child pipelines to run scans concurrently, significantly reducing overall pipeline execution time when you need to scan multiple images.\n\n#### How to enable Multi-Container scanning\n\n1. Create a `.gitlab-multi-image.yml` file in your repository root:\n\n```yaml\nscanTargets:\n  - name: alpine\n    tag: \"3.19\"\n  - name: python\n    tag: \"3.9-slim\"\n  - name: nginx\n    tag: \"1.25\"\n```\n\n2. Include the template in your `.gitlab-ci.yml`:\n\n```yaml\ninclude:\n  - template: Jobs/Multi-Container-Scanning.latest.gitlab-ci.yml\n```\n\n#### Advanced configuration\n\n**Scan images from private registries:**\n\n```yaml\nauths:\n  registry.gitlab.com:\n    username: ${CI_REGISTRY_USER}\n    password: ${CI_REGISTRY_PASSWORD}\n\nscanTargets:\n  - name: registry.gitlab.com/private/image\n    tag: latest\n```\n\n**Include license information:**\n\n```yaml\nincludeLicenses: true\n\nscanTargets:\n  - name: postgres\n    tag: \"15-alpine\"\n```\n\n\n### 4. Continuous Vulnerability Scanning\n\n* What it does: Automatically creates vulnerabilities when new security advisories are published, no pipeline required \n\n* Best for: Proactive security monitoring between deployments\n\n* Tier availability: Ultimate only\n\n* [Documentation](https://docs.gitlab.com/user/application_security/continuous_vulnerability_scanning/)  \n\nTraditional scanning only catches vulnerabilities at scan time. But what happens\nwhen a new CVE is published tomorrow for a package you scanned yesterday? Continuous\nVulnerability Scanning solves this by monitoring the GitLab Advisory Database and\nautomatically creating vulnerability records when new advisories affect your components.\n\n\n#### How it works\n\n1. Your Container Scanning or Dependency Scanning job generates a CycloneDX SBOM.\n\n2. GitLab registers your project's components from this SBOM.\n\n3. When new advisories are published, GitLab checks if your components are affected.\n\n4. Vulnerabilities are automatically created in your vulnerability report.\n\n\n#### Key considerations\n\n- Scans run via background jobs (Sidekiq), not CI pipelines.\n\n- Only advisories published within the last 14 days are considered for new component detection.\n\n- Vulnerabilities use \"GitLab SBoM Vulnerability Scanner\" as the scanner name.\n\n- To mark vulnerabilities as resolved, you still need to run a pipeline-based scan.\n\n\n### 5. Operational Container Scanning\n\n* What it does: Scans running containers in your Kubernetes cluster on a\nscheduled cadence\n\n* Best for: Post-deployment security monitoring and runtime vulnerability detection  \n\n* Tier availability: Ultimate only\n\n* [Documentation](https://docs.gitlab.com/user/clusters/agent/vulnerabilities/)\n\n\nOperational Container Scanning bridges the gap between build-time security and\nruntime security. Using the GitLab Agent for Kubernetes, it scans containers\nactually running in your clusters—catching vulnerabilities that emerge after\ndeployment.\n\n#### How to enable Operational Container Scanning\n\nIf you are using the [GitLab Kubernetes Agent](https://docs.gitlab.com/user/clusters/agent/install/), you can add the following to your agent configuration file:\n\n```yaml\ncontainer_scanning:\n  cadence: '0 0 * * *'  # Daily at midnight\n  vulnerability_report:\n    namespaces:\n      include:\n        - production\n        - staging\n```\n\n\nYou can also create a [scan execution policy](https://docs.gitlab.com/user/clusters/agent/vulnerabilities/#enable-via-scan-execution-policies) that enforces scanning on a schedule by the GitLab Kubernetes Agent.\n\n\n![Scan execution policy - Operational Container Scanning](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772547515/gsgvjcq4sas4dfc8ciqk.png \"Scan execution policy conditions for Operational Container Scanning\")\n\n#### Viewing results\n\n* Navigate to **Operate > Kubernetes clusters**.\n\n* Select the **Agent** tab, and choose your agent.\n\n* Then select the **Security** tab to view cluster vulnerabilities.\n\n* Results also appear under the **Operational Vulnerabilities** tab in the **Vulnerability Report**.\n\n\n## Enhancing posture with GitLab Security Policies\n\nGitLab Security Policies enable you to enforce consistent security standards across your container workflows through automated, policy-driven controls. These policies shift security left by embedding requirements directly into your development pipeline, ensuring vulnerabilities are caught and addressed before code reaches production.\n\n#### Scan execution and pipeline policies\n\n[Scan execution policies](https://docs.gitlab.com/user/application_security/policies/scan_execution_policies/) automate when and how Container Scanning runs across your projects. Define policies that trigger container scans on every merge request, schedule recurring scans of your main branch, and more. These policies ensure comprehensive coverage without relying on developers to manually configure scanning in each project's CI/CD pipeline.\n\nYou can specify which scanner versions to use and configure scanning parameters centrally, maintaining consistency across your organization while adapting to new container security threats.\n\n![Scan execution policy configuration](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772547517/z36dntxslqem9udrynvx.png \"Scan execution policy configuration\")\n\n\n[Pipeline execution policies](https://docs.gitlab.com/user/application_security/policies/pipeline_execution_policies/) provide flexible controls for injecting (or overriding) custom jobs into a pipeline based on your compliance needs.\n\nUse these policies to automatically inject Container Scanning jobs into your pipeline, fail builds when container vulnerabilities exceed your risk tolerance, trigger additional security checks for specific branches or tags, or enforce compliance requirements for container images destined for production environments. Pipeline execution policies act as automated guardrails, ensuring your security standards are consistently applied across all container deployments without manual intervention.\n\n![Pipeline execution policy](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772547517/ddhhugzcr2swptgodof2.png \"Pipeline execution policy actions\")\n\n#### Merge request approval policies\n\n[Merge request approval policies](https://docs.gitlab.com/user/application_security/policies/merge_request_approval_policies/) enforce security gates by requiring designated approvers to review and sign off on merge requests containing container vulnerabilities.\n\nConfigure policies that block merge when critical or high-severity vulnerabilities are detected, or require security team approval for any merge request introducing new container findings. These policies prevent vulnerable container images from advancing through your pipeline while maintaining development velocity for low-risk changes.\n\n![Merge request approval policy performing block in MR](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772547513/hgnbc1vl4ssqafqcyuzg.png \"Merge request approval policy performing block in MR\")\n\n\n## Choosing the right approach\n\n| Scanning Type | When to Use | Key Benefit |\n|--------------|-------------|-------------|\n| Pipeline-based | Every build | Shift-left security, blocks vulnerable builds |\n| Registry scanning | Continuous monitoring | Catches new CVEs in stored images |\n| Multi-container | Microservices | Parallel scanning, faster pipelines |\n| Continuous vulnerability | Between deployments | Proactive advisory monitoring |\n| Operational | Production monitoring | Runtime vulnerability detection |\n\n\n\nFor comprehensive security, consider combining multiple approaches. Use\npipeline-based scanning to catch issues during development, container\nscanning for registry for continuous monitoring, and operational scanning\nfor production visibility.\n\n## Get started today\n\nThe fastest path to container security is enabling pipeline-based scanning:\n\n1. Navigate to your project's **Secure > Security configuration**.\n2. Click **Configure with a merge request** for Container Scanning.\n3. Merge the resulting merge request.\n4. Your next pipeline will include vulnerability scanning.\n\nFrom there, layer in additional scanning types based on your security requirements\nand GitLab tier.\n\nContainer security isn't a one-time activity, it's an ongoing process.\nWith GitLab's comprehensive container scanning capabilities, you can detect\nvulnerabilities at every stage of your container lifecycle, from build to runtime.\n\n> For more information on how GitLab can help enhance your security posture, visit the [GitLab Security and Governance Solutions Page](https://about.gitlab.com/solutions/application-security-testing/).\n",{"slug":803,"featured":33,"template":17},"complete-guide-to-gitlab-container-scanning",{"promotions":805},[806,820,831,842],{"id":807,"categories":808,"header":810,"text":811,"button":812,"image":817},"ai-modernization",[809],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":813,"config":814},"Get your AI maturity score",{"href":815,"dataGaName":816,"dataGaLocation":246},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":818},{"src":819},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":821,"categories":822,"header":823,"text":811,"button":824,"image":828},"devops-modernization",[776,572],"Are you just managing tools or shipping innovation?",{"text":825,"config":826},"Get your DevOps maturity score",{"href":827,"dataGaName":816,"dataGaLocation":246},"/assessments/devops-modernization-assessment/",{"config":829},{"src":830},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":832,"categories":833,"header":834,"text":811,"button":835,"image":839},"security-modernization",[13],"Are you trading speed for security?",{"text":836,"config":837},"Get your security maturity score",{"href":838,"dataGaName":816,"dataGaLocation":246},"/assessments/security-modernization-assessment/",{"config":840},{"src":841},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":843,"paths":844,"header":847,"text":848,"button":849,"image":854},"github-azure-migration",[845,846],"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. 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