[{"data":1,"prerenderedAt":815},["ShallowReactive",2],{"/en-us/blog/how-to-integrate-custom-security-scanners-into-gitlab":3,"navigation-en-us":38,"banner-en-us":448,"footer-en-us":458,"blog-post-authors-en-us-Fernando Diaz":699,"blog-related-posts-en-us-how-to-integrate-custom-security-scanners-into-gitlab":713,"blog-promotions-en-us":753,"next-steps-en-us":805},{"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":20,"seo":29,"stem":34,"tagSlugs":35,"__hash__":37},"blogPosts/en-us/blog/how-to-integrate-custom-security-scanners-into-gitlab.yml","How To Integrate Custom Security Scanners Into Gitlab",[7],"fernando-diaz",null,"security",{"slug":11,"featured":12,"template":13},"how-to-integrate-custom-security-scanners-into-gitlab",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How to integrate custom security scanners into GitLab","Learn how to extend the DevSecOps platform by adding custom security scanners to your workflows (includes an easy-to-follow tutorial).",[18],"Fernando Diaz","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097082/Blog/Hero%20Images/Blog/Hero%20Images/securitycheck_securitycheck.png_1750097081856.png","2024-02-27","GitLab, the most comprehensive DevSecOps platform, has everything you need to plan, manage, build, deploy, secure, govern, and monitor your applications. However, there are instances where you may want to extend GitLab with third-party or custom tools. For example, you might need to migrate to a DevSecOps platform from separate solutions, evaluate third-party tools, or integrate proprietary or custom-built solutions into GitLab.\n\nHere's what is covered:\n- [GitLab DevSecOps platform extensibility](#gitlab-devsecops-platform-extensibility)\n- [GitLab security scanner integration](#gitlab-security-scanner-integration)\n  - [Merge request security widget](#merge-request-security-widget)\n  - [Pipeline Security section](#pipeline-security-section)\n  - [Vulnerability Report](#vulnerability-report)\n  - [Vulnerability pages](#vulnerability-pages)\n  - [Security dashboard](#security-dashboard)\n  - [Scan Result Policy integration](#scan-result-policy-integration)\n- [Tutorial: Integrating custom security scanners](#tutorial-integrating-custom-security-scanners)\n  - [Creating a custom security scanner](#creating-a-custom-security-scanner)\n  - [Integrating a custom security scanner with GitLab](#integrating-a-custom-security-scanner-with-gitlab)\n\n## GitLab DevSecOps platform extensibility\n\nGitLab can be extended in many ways to support enhanced functionality that your organization may require. Some common examples of these integrations include:\n\n- external application integrations such as Jenkins and Slack\n- external issue tracking integrations such as Bugzilla and Jira\n- external authentication provider integrations such as LDAP and SAML\n- external security scanner integrations such as Fortify and Checkmarx\n- ability to respond to leaked secrets such as AWS and GCP access keys\n\nYou can see all the available integrations in the [Integrate with GitLab documentation](https://docs.gitlab.com/integration/). (Note: Not all integrations are listed in the documentation.)\n\n## GitLab security scanner integration\n\n[Third-party security scanners](https://docs.gitlab.com/integration/#security-improvements) or [custom-built security scanners](https://gitlab.com/gitlab-da/tutorials/security-and-governance/custom-scanner-integration) can be integrated into GitLab to populate the merge request widget, Pipeline Security section, Vulnerability Report, vulnerability pages, Security dashboard, and Scan Result Policies. Let's review each integration.\n\n### Merge request security widget\n\nA merge request contains a security widget which displays a summary of the newly detected vulnerabilities.\n\n![integrating security scanners - image 1](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097089/Blog/Content%20Images/Blog/Content%20Images/image4_aHR0cHM6_1750097088837.png)\n\n\u003Ccenter>\u003Cem>Merge request security widget\u003C/em>\u003C/center>\n\u003Cp>\u003C/p>\n\nWhen you click on a vulnerability, you will see a popup that contains the following information:\n- status\n- description\n- project\n- file\n- identifiers\n- severity\n- tool\n- scanner provider\n\n![integrating security scanners - image 2](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097089/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750097088838.png)\n\n\u003Ccenter>\u003Cem>Actionable vulnerabilities with details\u003C/em>\u003C/center>\n\n\u003Cp>\u003C/p>\n\nThese vulnerabilities are also actionable, which means that they can either be dismissed or a confidential issue can be created.\n\nThe results of a custom scanner can be used to populate the security widget. The vulnerability data is populated from the JSON schema the scanner emits.\n\n### Pipeline Security section\n\nAll enabled security analyzers run in the pipeline and output their results as artifacts. These artifacts are processed, including deduplication, and the results are listed on the Pipeline Security tab. From here, you can also download the resulting JSON files.\n\n![integrating security scanners - image 3](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097089/Blog/Content%20Images/Blog/Content%20Images/image11_aHR0cHM6_1750097088840.png)\n\n\u003Ccenter>\u003Cem>Pipeline Security tab\u003C/em>\u003C/center>\n\u003Cp>\u003C/p>\n\nThe results of a custom scanner can be used to populate the Pipeline Security tab. The columns are filled in using the JSON schema the scanner emits.\n\n### Vulnerability Report\n\nThe Vulnerability Report provides information about vulnerabilities from scans of the default branch, including:\n\n- totals of vulnerabilities per severity level\n- filters for common vulnerability attributes\n- details of each vulnerability, presented in tabular layout\n\n![integrating security scanners - image 4](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097089/Blog/Content%20Images/Blog/Content%20Images/image8_aHR0cHM6_1750097088842.png)\n\n\u003Ccenter>\u003Cem>Vulnerability Report\u003C/em>\u003C/center>\n\u003Cp>\u003C/p>\n\nThe results of a custom scanner on the default branch can be used to populate the Vulnerability Report.\n\n### Vulnerability pages\n\nClicking on a vulnerability present within the Vulnerability Report takes you to its vulnerability page. Each vulnerability in a project has a vulnerability page that provides details such as:\n\n- description\n- when it was detected\n- current status\n- location detected\n- available actions\n- linked issues\n- actions log\n- solutions\n- identifier\n- training\n\nYou can use the data provided in the vulnerability page to triage a detected vulnerability as well as assist in its remediation.\n\n![integrating security scanners - image 5](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097089/Blog/Content%20Images/Blog/Content%20Images/image5_aHR0cHM6_1750097088844.png)\n\n\u003Ccenter>\u003Cem>Vulnerability page for secret detection vulnerability\u003C/em>\u003C/center>\n\u003Cp>\u003C/p>\n\nThe results of a custom scanner can be used to populate the vulnerability page. The vulnerability data is populated from the JSON schema the scanner emits.\n\n### Security dashboard\n\nSecurity dashboards are used to assess the security posture of your applications. GitLab provides you with a collection of metrics, ratings, and charts for the vulnerabilities detected by the security scanners run on your project. The security dashboard provides data such as:\n\n- vulnerability trends over a 30-, 60-, or 90-day timeframe for all projects in a group\n- a letter grade rating for each project based on vulnerability severity\n- the total number of vulnerabilities detected within the last 365 days and their severity levels\n\n![integrating security scanners - image 6](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097089/Blog/Content%20Images/Blog/Content%20Images/image7_aHR0cHM6_1750097088846.png)\n\n\u003Ccenter>\u003Cem>Group-level Security dashboard\u003C/em>\u003C/center>\n\u003Cp>\u003C/p>\n\nFrom the group-level Security dashboard you can click on a project to access its specific Security dashboard, which provides the 365-day view.\n\n![integrating security scanners - image 7](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097089/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750097088847.png)\n\n\u003Ccenter>\u003Cem>Project-level Security dashboard\u003C/em>\u003C/center>\n\u003Cp>\u003C/p>\n\n### Scan Result Policy integration\n\nScan Result Policies are used to require approval based on the findings of one or more security scan jobs. This can prevent insecure code from being merged to production. Scan Result Policies are evaluated after a CI scanning job is fully executed, where policies are evaluated based on the job artifact reports that are published in the completed pipeline.\n\nFor example, you can create a Scan Result Policy that requires approval from project maintainers if a secret detection scanner finds any vulnerabilities. Here's how:\n\n1. On the left sidebar, select **Search or go to** and search for the project you wish to add a policy to.\n2. On the project left sidebar, go to **Secure > Policies**\n3. Select **New policy**\n4. In the **Scan result policy** section, select **Select policy**.\n5. Complete the fields:\n- Name: The name of the Policy\n- Description: The description of the Policy\n- Policy status: Whether it is enabled or not\n- Rules: The conditions that must be met for an action (require approval) to take place\n\n![integrating security scanners - image 8](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097089/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750097088849.png)\n\u003Ccenter>\u003Cem>Scan Result Policy rules\u003C/em>\u003C/center>\n\u003Cp>\u003C/p>\n\n- Actions: The action to be taken whenever the conditions in the rules (defined vulnerabilities/licenses detected) are met\n\n![integrating security scanners - image 9](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097089/Blog/Content%20Images/Blog/Content%20Images/image9_aHR0cHM6_1750097088850.png)\n\n\u003Ccenter>\u003Cem>Scan Result Policy actions\u003C/em>\u003C/center>\n\u003Cp>\u003C/p>\n\n- Override project approval settings: If selected, the following choices will overwrite project settings but only affect the branches selected in the policy\n\n![integrating security scanners - image 11](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097089/Blog/Content%20Images/Blog/Content%20Images/image6_aHR0cHM6_1750097088851.png)\n\n \u003Ccenter>\u003Cem>Scan Result Policy approval settings\u003C/em>\u003C/center>\n \u003Cp>\u003C/p>\n\n6. Press the \"Configure with a merge request\" button.\n\nOnce the Scan Result Policy has been merged, whenever you create a merge request and the criteria defined in the rules are met, then the defined action will be triggered. In this case, at least one approval will be required from a maintainer before the code can be merged.\n\n![integrated security scanner - image 10](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097089/Blog/Content%20Images/Blog/Content%20Images/image10_aHR0cHM6_1750097088852.png)\n\n\u003Ccenter>\u003Cem>Blocked merge request due to detected vulnerabilities\u003C/em>\u003C/center>\n\u003Cp>\u003C/p>\n\nThe results of a custom scanner can be fully integrated with Scan Result Policies. If the custom scanner detects a vulnerability, then approval will be required before the code can be merged. The scanner you select in a Scan Result Policy must be leveraging the appropriate JSON schema.\n\n## Tutorial: Integrating custom security scanners\n\nNow let’s get to the fun part – integrating a custom security scanner. In this tutorial, you will learn how to create a custom security scanner, as well as how to integrate it with GitLab. We will be leveraging the following projects:\n\n- [Fern Pattern Scanner](https://gitlab.com/gitlab-da/tutorials/security-and-governance/custom-scanner-integration/fern-pattern-scanner): Scans your files looking for specific patterns such as passwords, private keys, and social security numbers.\n- [Secret list](https://gitlab.com/gitlab-da/tutorials/security-and-governance/custom-scanner-integration/secret-list): Contains a list of user passwords, clients, and keys. This project is used to showcase how a custom security scanner can be integrated into GitLab.\n\nYou can watch the following video to see how the application was created and how it is used in detail:\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/timMbl5SP-w?si=R2DKtZ5MmBR1rQFL\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n### Creating a custom security scanner\n\nNow let’s create a custom scanner that can be integrated into GitLab. Before a custom scanner can be fully integrated with GitLab, the scanner must:\n- scan a directory for defined patterns\n- emit a JSON following the appropriate schema\n- be containerized and accessible\n- provide a template to allow it to be run on another project\n\nWhen the [Fern Pattern scanner](https://gitlab.com/gitlab-da/tutorials/security-and-governance/custom-scanner-integration/fern-pattern-scanner) is run on a project using the provided template, it performs the following steps:\n1. Loads a set of rules which define patterns (regex) to detect.\n- Allow rules to be configurable to meet the changing needs of your organization.\n2. Scans files for defined patterns.\n3. Emits a JSON report following the Secret Detection schema.\n- Go templates are used in this project to create a JSON.\n- Depending on what your scanner will look for, make sure you use the appropriate schema.\n\nOnce the JSON report is loaded as an artifact into GitLab, it will populate the merge request widget, Vulnerability Report, vulnerability pages, Scan Result Policies, and Security dashboards as defined above.\n\n### Integrating a custom security scanner with GitLab\n\nOnce you have created your custom scanner that meets all the needs for integration, you can run it on GitLab.\n\nRunning a custom scanner is as easy as adding a template. We can see how the Fern Pattern scanner template is loaded by examining the `.gitlab-ci.yml` in the [Secret List](https://gitlab.com/gitlab-da/tutorials/security-and-governance/custom-scanner-integration/secret-list) project.\n\n1. Create a [.gitlab-ci.yml file](https://docs.gitlab.com/ci/quick_start/#create-a-gitlab-ciyml-file) in the project you want the scanner to run on.\n2. Include the [Custom Scanner template](https://docs.gitlab.com/ci/yaml/includes/).\n    - You should also be able to configure the template with environment variables.\n3. Commit the file to the main branch.\n\nOnce the file has been committed, you can see that the custom scanner will run in your pipeline. Once the pipeline is complete, the scanner will populate all the areas defined above in the [GitLab security scanner integration](#gitlab-security-scanner-integration) section.\n\n## Read more\n\nCheck out these resources to learn more about GitLab and the other ways you can extend your DevSecOps platform:\n\n- [Security Scanner GitLab Integration](https://docs.gitlab.com/development/integrations/secure/)\n- [GitLab Partner Integrations](https://docs.gitlab.com/integration/)\n- [Custom Security Scanner Projects Group](https://gitlab.com/gitlab-da/tutorials/security-and-governance/custom-scanner-integration)\n- [Automatic Response to a Secret Leak](https://docs.gitlab.com/user/application_security/secret_detection/automatic_response/)\n",[23,9,24,25],"tutorial","testing","DevSecOps 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your pipeline for AI-discovered zero-days","AI is finding vulnerabilities faster than teams can patch. Learn how pipeline enforcement, automated triage, and AI remediation close the gap.",[719],"Omer Azaria","2026-04-20","Anthropic's [Mythos Preview model](https://red.anthropic.com/2026/mythos-preview/) recently identified thousands of zero-day vulnerabilities across every major operating system and web browser, including an OpenBSD bug that went undetected for 27 years. In testing, Mythos autonomously chained four vulnerabilities into a working browser exploit that escaped its sandbox. Anthropic is restricting access to Mythos, but the company’s head of offensive cyber research expects threats to have comparable tooling within six to twelve months.\n\nThe defender side of the equation hasn't kept pace. One third of exploited Common Vulnerabilities and Exposures (CVEs) in the first half of 2025 showed activity on or before disclosure day, before most teams even know there's something to patch. AI is compressing that window further, accelerating attackers and flooding teams with whitehat disclosures faster than they can triage. Defender tooling has improved, but most organizations can't operationalize it fast enough to close the gap between discovery and exploitation.\n\nWhen the window between disclosure and exploitation is measured in hours, the security team can't be the last line of defense. Security has to run where code enters the system: in the pipeline, on every merge request, enforced by policy. The fixes that can be automated should be. The ones that can't need to reach the right human faster than they do today.\n\n## Known vulnerabilities are already outpacing remediation\n\nThe bottleneck isn't detection, it's acting at scale on what teams already know. Sixty percent of breaches in the 2025 Verizon DBIR involved exploiting known vulnerabilities where a patch was already available. Teams couldn’t close them in time.\n\nThe backlog was untenable before Mythos. Developers spend [11 hours per month remediating vulnerabilities](https://about.gitlab.com/resources/developer-survey/) post-release instead of shipping new work. Over half of organizations have at least one open internet-facing vulnerability, and the median time to close half of those is 361 days. Exploitation takes hours, while remediation takes months.\n\nAI-assisted development is widening the gap, and stakeholders know it. By June 2025, AI-generated code was adding over 10,000 new security findings per month across Fortune 50 repositories, a 10x jump from six months earlier. Georgia Tech identified 34 [CVEs attributable to AI-generated code](https://research.gatech.edu/bad-vibes-ai-generated-code-vulnerable-researchers-warn) in March 2026, up from 6 in January, and that count reflects only the ones where AI authorship is clear. AI coding assistants hallucinate package names, reach for outdated patterns, and copy insecure examples from training data. More code, more dependencies, and more vulnerabilities per line are generated faster than security teams can review them.\n\nDefenders need to harness frontier AI models, too — not bolted onto the SDLC as external tooling, but running inside the same policies, approvals, and audit trail as the rest of the team. \n\n## Security at the speed of AI coding\n\nWhen a critical CVE drops, how quickly can your team confirm which projects are affected? How many tools does an alert cross before a developer can submit a fix?\n\nThe teams that benefit most from AI already have policies, enforcement, and controls embedded in their development workflows. AI amplifies that foundation. It doesn't replace it.\n\n**Enforcement at the point of change.** As exploitation windows compress, every line of code entering a repository needs to pass through a defined set of controls. Not a separate review, in a different tool, by a different team. Organizations need the ability to enforce security policies across every group and project, with the merge request as the enforcement point. Policies defined once, applied everywhere, with exceptions reviewed, approved, and logged.\n\n**Simple issues caught before the merge request, not during.** Hardcoded secrets, known-vulnerable imports, and deprecated API calls can be flagged in the IDE before a developer pushes a commit. Catching them at authoring time means fewer findings blocking the MR, so review cycles go to the findings that require cross-component context: reachability, exploitability, and architectural risk.\n\n**Triage automated by default, not by exception.** Embedding security into every merge request creates a volume problem. More scans, more findings, more noise reaching developers who aren’t trained to distinguish a reachable critical from a theoretical one. AI must handle false positive detection, reachability, exploitability context, and severity assessment before a developer sees the finding, so the findings they see actually warrant their time.\n\n**Remediation governed like any other change.** AI-based remediation compresses the timeline for closing vulnerabilities, but every generated fix must move through the same governance as a human-authored change: policies enforce scans, the right reviewers approve, and evidence is recorded. GitLab’s automated remediation capability proposes each fix in a merge request with a confidence score. The MR records which policy applied, which scans ran, what they found, and who approved. Human code and AI-generated code move through the same process, with the same audit trail.\n\n## What a ready pipeline looks like\n\nHere's how these pieces work together when a high-severity vulnerability is discovered and the clock is running.\n\nA proof-of-concept exploit for a vulnerability in a popular open-source package appears on a security mailing list. There’s no CVE, no National Vulnerability Database (NVD) entry, and no scanner signature yet. The security team finds out the usual way: someone shares it in Slack.\n\nA security engineer asks the security agent if the package is in use, which projects have affected versions, and whether any vulnerable call paths are reachable in production. The agent checks the dependency graph for every project, matches the affected versions and entry points from the disclosure, and returns a ranked list of exposed projects with details about reachability. There’s no need to search through repositories by hand or wait for a scanner update. The question, \"Are we exposed?\" is answered in minutes.\n\nThe engineer starts a remediation campaign for every exposed project. The remediation agent suggests fixes: version updates where a patched release is available, and targeted call-path patches where it is not. Scan execution policies are already in place for projects tagged SOC 2. The engineer hardens the rules to block merges on any merge request that introduces or keeps the affected dependency, and an approval policy now requires security sign-off on every fix. The agent's first proposed patch fails the pipeline when an integration test catches a regression. The agent revises the patch based on the test failure, and the second attempt passes. Developers review the changes, security signs off under the stricter policy, and merges proceed across the campaign.\n\nAt the next audit review, the security team presents a report showing how policies were enforced and risks were reduced during the campaign. It includes scan results, policies applied, approvers, and merge timestamps for every MR in every affected project. The evidence was automatically generated in flight, not assembled after the fact.\n\n## Close the gaps now\n\nMythos exists today, and comparable models will be in attacker hands within a year. Every month between now and then is a chance to strengthen your software supply chain.\n\nAsk these questions about your pipeline:\n\n* How do you enforce that security scans run on every merge request, not just the projects where teams configured them?\n\n* If a compromised package entered your dependency tree today, would your pipeline catch it before build?\n\n* When a scanner flags a critical finding, how many tool boundaries does it cross before a developer starts the fix?\n\n* If an AI agent proposed a code fix for a vulnerability, what process would that fix go through before reaching production, and is that process auditable?\n\n* When auditors ask for evidence that a specific policy was enforced on a specific change, how long does it take to produce?\n\nIf the answers expose gaps, address them now. [Talk to a GitLab solutions architect](https://about.gitlab.com/sales/) about the role of security governance in your development lifecycle.",[723,9,25],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":12,"template":13,"slug":726},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":728,"config":739},{"title":729,"description":730,"authors":731,"heroImage":733,"date":734,"category":9,"tags":735,"body":738},"Manage 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.",[732],"Grant Hickman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1774375772/kpaaaiqhokevxxeoxvu0.png","2026-03-25",[9,23,564,736,737],"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":740,"featured":12,"template":13},"auto-dismiss-vulnerability-management-policy",{"content":742,"config":751},{"title":743,"description":744,"authors":745,"heroImage":747,"date":748,"body":749,"category":9,"tags":750},"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.",[746],"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_).",[737,9,736],{"featured":30,"template":13,"slug":752},"gitlab-18-10-brings-ai-native-triage-and-remediation",{"promotions":754},[755,769,780,791],{"id":756,"categories":757,"header":759,"text":760,"button":761,"image":766},"ai-modernization",[758],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":762,"config":763},"Get your AI maturity score",{"href":764,"dataGaName":765,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":767},{"src":768},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":770,"categories":771,"header":772,"text":760,"button":773,"image":777},"devops-modernization",[737,567],"Are you just managing tools or shipping innovation?",{"text":774,"config":775},"Get your DevOps maturity score",{"href":776,"dataGaName":765,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":778},{"src":779},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":781,"categories":782,"header":783,"text":760,"button":784,"image":788},"security-modernization",[9],"Are you trading speed for security?",{"text":785,"config":786},"Get your security maturity score",{"href":787,"dataGaName":765,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":789},{"src":790},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":792,"paths":793,"header":796,"text":797,"button":798,"image":803},"github-azure-migration",[794,795],"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":799,"config":800},"See how GitLab compares to GitHub",{"href":801,"dataGaName":802,"dataGaLocation":242},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":804},{"src":779},{"header":806,"blurb":807,"button":808,"secondaryButton":813},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":809,"config":810},"Get your free trial",{"href":811,"dataGaName":49,"dataGaLocation":812},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":504,"config":814},{"href":53,"dataGaName":54,"dataGaLocation":812},1777404557445]