[{"data":1,"prerenderedAt":815},["ShallowReactive",2],{"/en-us/blog/the-ultimate-guide-to-token-management-at-gitlab":3,"navigation-en-us":39,"banner-en-us":449,"footer-en-us":459,"blog-post-authors-en-us-Hakeem Abdul-Razak":700,"blog-related-posts-en-us-the-ultimate-guide-to-token-management-at-gitlab":715,"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":27,"isFeatured":12,"meta":28,"navigation":12,"path":29,"publishedDate":20,"seo":30,"stem":35,"tagSlugs":36,"__hash__":38},"blogPosts/en-us/blog/the-ultimate-guide-to-token-management-at-gitlab.yml","The Ultimate Guide To Token Management At Gitlab",[7],"hakeem-abdul-razak",null,"security",{"slug":11,"featured":12,"template":13},"the-ultimate-guide-to-token-management-at-gitlab",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"The ultimate guide to token management at GitLab","Learn all the steps in the end-to-end process of identifying, managing, and securing tokens for improved security across the software development lifecycle.",[18],"Hakeem Abdul-Razak","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097408/Blog/Hero%20Images/Blog/Hero%20Images/AdobeStock_1097303277_6gTk7M1DNx0tFuovupVFB1_1750097407860.jpg","2025-02-25","Imagine this: You are an engineer at a growing tech company, and it’s 2 a.m. when you get an urgent call. A critical deployment pipeline has failed, and your team is scrambling to figure out why. After hours of digging, you realize someone revoked a personal access token belonging to an engineer who left the company a week ago. This token was tied to several key automation processes, and now your entire system is in chaos. How do you make sure it does not happen again?\n\nFollow this guide, which takes GitLab customers through the end-to-end process of identifying, managing, and securing their tokens. It is meant to be a handy supplement to the extensive [token overview documentation](https://docs.gitlab.com/ee/security/tokens) for GitLab administrators, developers, and security teams who need to ensure proper token management within their projects.\n\nHere's what is covered in this guide:\n- [How to select the right token for the job](#how-to-select-the-right-token-for-the-job)\n- [Token types](#token-types)\n- [Discovering your tokens](#discovering-your-tokens)\n    - [Credentials inventory](#credentials-inventory)\n- [Managing tokens in the GitLab UI and API](#managing-tokens-in-the-gitlab-ui-and-api)\n- [Token rotation and expiration management](#token-rotation-and-expiration-management)\n- [Token management best practices](#token-management-best-practices)\n    - [Service accounts](#service-accounts)\n\n## How to select the right token for the job\n\nChoosing the right token ensures optimal security and functionality based on your use case. Tokens can be used for authenticating API requests, automating CI/CD pipelines, integrating third-party tools, managing deployments and repositories, and more.\n\n![Token management guide - flow chart for tokens](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097435/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750097434869.png)\n\nFor the sake of simplicity, the chart illustrates a straightforward use case tied to single user ownership. For more information, check out our documentation of user roles and permissions at each [namespace](https://docs.gitlab.com/ee/user/permissions.html) (user/group) in your instance or top-level group. Example use cases could be as follows:\n- **Personal access tokens** ([PAT](https://docs.gitlab.com/user/profile/personal_access_tokens/#personal-access-token-scopes)) can be used by developers when a user's personal access and permissions are required. In this case, the credentials follow the status and permissions of the user, including the removal of access if the account loses access to a specific project or group (or is blocked entirely).   - **Project/group access tokens** ([PrAT](https://docs.gitlab.com/user/project/settings/project_access_tokens/#scopes-for-a-project-access-token)/[GrAT](https://docs.gitlab.com/user/group/settings/group_access_tokens/#scopes-for-a-group-access-token)) are recommended when access should be scoped to resources within a specific project/group, allowing anyone with a PrAT/GrAT to access those resources through mechanisms managed by assigned scopes.\n\n## Token types\n\nBelow is a list of GitLab tokens with their default prefixes and use cases. For more information, please visit the [GitLab Token overview page](https://docs.gitlab.com/ee/security/tokens/#available-scopes).\n| Tokens | Prefix  | Description |\n| :---: | :---: | :---: |\n| Personal access token | glpat | Access user-specific data |\n| OAuth 2.0 token |  gloas | Integrate with third-party applications using OAuth2.0 authentication protocol |\n| Impersonation token | glpat | Act on behalf of another user for administrative purposes |\n| Project access token | glpat | Access data from a specific project |\n| Group access token | glpat |  Access data from a specific group |\n| Deploy token | gldt |  Clone, push, and pull container registry images of a project without a user and a password |\n| Deploy keys | N/A | Allow read-only or read-write access to your repositories |\n| Runner authentication token | glrt | Authenticate GitLab Runners |\n| CI/CD job token  | glcbt | Automate CI/CD processes |\n| Trigger token | glptt | Trigger pipelines manually or programmatically |\n| Feed token | glft | Authenticate access to package/RSS feeds |\n| Incoming mail token  | glimt | Process incoming emails |\n| GitLab agent for Kubernetes token | glagent | Manage Kubernetes clusters via the GitLab agent |\n| SCIM tokens | glsoat | Enable SCIM integrations for user provisioning |\n| Feature flags client token | glffct | Enable feature flags programmatically |\n| Webhook token | N/A | User set secret token to secure webhook payloads and ensure that the requests are from GitLab |\n\n## Discovering your tokens\n\n### Credentials inventory\n\nOn GitLab Ultimate, administrators (GitLab Self-Managed) and top-level group owners of an enterprise organization (GitLab.com as of Version 17.5) can monitor the credentials in their namespace.\n\nThis inventory tracks token details such as:\n\n* Token type    * Available tokens on [GitLab.com](https://docs.gitlab.com/ee/user/group/credentials_inventory.html)    * Available tokens on [GitLab Self-Managed](https://docs.gitlab.com/ee/administration/credentials_inventory.html)  * Associated user accounts  * Token scopes, and creation and expiration dates  * Token last used IP addresses (as of GitLab 17.10)  * Token filtration based on the above user-defined parameters  * Ability to revoke and rotate those tokens\n\nA well-maintained credentials inventory helps identify over-permissioned tokens, and gives insight into credentials that may need to be rotated, ensuring a secure and efficient workflow.\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/A9ONfnwswd0?si=4VIEUgJaD4daj81b&start=105\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n#### Credentials inventory API\n\nAs a complement to the UI, there is [ongoing development](https://gitlab.com/groups/gitlab-org/-/epics/16343) to release a credentials inventory API through the new /group/:id/manage [endpoint](https://docs.gitlab.com/ee/api/members.html#list-all-members-of-a-group-or-project). The credentials accessible under this endpoint are limited to enterprise [users](https://docs.gitlab.com/ee/user/enterprise_user/), and can be accessed by the top-level group owner of an enterprise organization. An example of the future API call would be:\n\n```console\ncurl --header \"PRIVATE-TOKEN: \u003Cpat>\" \"https://verified_domain.com/api/v4/groups/\u003Cgroup_id>/manage/personal_access_tokens\"           ```\n### GitLab API\n\nThe GitLab API allows you to programmatically list and manage tokens within your organization. Key authentication-related endpoints support [various token types](https://docs.gitlab.com/ee/api/rest/authentication.html)), including personal, group, CI/CD tokens, and more. An example of using a personal access token to list all visible projects across GitLab for the authenticated user is:\n\n```console\ncurl --header \"PRIVATE-TOKEN: \u003Cyour_access_token>\" \\\n     \"https://gitlab.example.com/api/v4/projects\"\n\n```\n\nWatch this video to learn how to make API calls to the GitLab API.\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/0LsMC3ZiXkA?si=vj871YH610jwQdFc\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n### Finding where tokens are used\n\nCustomers can find where tokens are used in different ways:\n* under **User Profile > [Access Tokens](https://docs.gitlab.com/ee/user/profile/personal_access_tokens.html#view-the-time-at-and-ips-where-a-token-was-last-used)**\n* in credentials inventory\n* in audit events\n* via the API\nInformation on token usage is updated every 10 minutes for **last_used** and every minute for **last_used_ip**.\nThe ability to view IP addresses was introduced in GitLab 17.9, and is controlled by the **:pat_ip** feature flag. Follow these [steps to view the last time a token was used](https://docs.gitlab.com/ee/user/profile/personal_access_tokens.html#view-the-time-at-and-ips-where-a-token-was-last-used), along with its last five distinct IP addresses.\n\n![Token management guide - personal access tokens settings](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097435/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750097434870.png)\n\n## Managing tokens in the GitLab UI and API\nThe following table includes videos detailing a few token creations in the UI and demonstrates their usage via the API.\n\n| Tokens     | GitLab UI    | GitLab API    |\n| ---------- | ---------- | ---------- |\n| Personal access token | [Documentation](https://docs.gitlab.com/ee/user/profile/personal_access_tokens.html#create-a-personal-access-token) and [video](https://youtu.be/v5Nj3Jy4vaI?t=3)  | [Documentation](https://docs.gitlab.com/ee/api/personal_access_tokens.html) and [video](https://youtu.be/v5Nj3Jy4vaI?t=43)  |\n| Group access token | [Documentation](https://docs.gitlab.com/ee/user/group/settings/group_access_tokens.html#group-access-tokens) and [video](https://youtu.be/v5Nj3Jy4vaI?t=120)  | [Documentation](https://docs.gitlab.com/ee/api/group_access_tokens.html) and [video](https://youtu.be/v5Nj3Jy4vaI?t=157)  |\n| Project access token | [Documentation](https://docs.gitlab.com/ee/user/project/settings/project_access_tokens.html#project-access-tokens) and [video](https://youtu.be/v5Nj3Jy4vaI?t=254)  | [Documentation](https://docs.gitlab.com/ee/api/project_access_tokens.html) and [video](https://youtu.be/v5Nj3Jy4vaI?t=285)  |\n\n## Token rotation and expiration management\n\nImplementing token rotation and strict expiration policies reduces the risk of compromise and ensures compliance with security standards. Regular rotation and enforced expirations prevent stale credentials from becoming security vulnerabilities.\n\nPreviously, expired group and project access tokens were automatically deleted upon expiration, which made auditing and security reviews more challenging due to the lack of a record of inactive tokens. To address this, a [recent feature](https://gitlab.com/gitlab-org/gitlab/-/issues/462217) introduced the retention of inactive group and project access token records in the UI for 30 days after they became inactive. This enhancement aims to allow teams to track token usage, expiration, and revocation for better compliance and monitoring.\n\nTo be more proactive in your token rotation and expiration management, do the following:\n* Actively rotate your tokens via the UI or API. If you use the latter, be mindful of the [automatic token reuse detection](https://docs.gitlab.com/ee/api/personal_access_tokens.html#automatic-reuse-detection) security mechanism.  * Set an instance-wide [maximum lifetime limit](https://docs.gitlab.com/ee/administration/settings/account_and_limit_settings.html#limit-the-lifetime-of-access-tokens) for access tokens.\n### Token rotation API\n\nUntil GitLab 17.7, customers had to programmatically rotate access tokens with the API. Its counterpart is now available on the UI. Check out the video in the table below or follow the [documentation](https://docs.gitlab.com/ee/user/project/settings/project_access_tokens.html#use-the-ui) for guidance.\n\n### Token rotation snippets\n\nThe following table includes videos detailing the rotation of GitLab tokens.\n| Tokens | Prerequisites | GitLab UI | GitLab API |\n| :---: | :---: | ----- | ----- |\n| Personal access token | Scope: api\u000b | [Documentation](https://docs.gitlab.com/ee/user/profile/personal_access_tokens.html#create-a-personal-access-token) and [video](https://youtu.be/v5Nj3Jy4vaI?t=76)  | [Documentation](https://docs.gitlab.com/ee/api/personal_access_tokens.html#rotate-a-personal-access-token) and [video](https://youtu.be/v5Nj3Jy4vaI?t=92)  |\n| Group access token | Scope: api and Role(s): owner | [Documentation](https://docs.gitlab.com/ee/user/group/settings/group_access_tokens.html#create-a-group-access-token-using-ui) and [video](https://youtu.be/v5Nj3Jy4vaI?t=203)  | [Documentation](https://docs.gitlab.com/ee/api/group_access_tokens.html) and [video](https://youtu.be/v5Nj3Jy4vaI?t=214)  |\n| Project access token | Scope: api and Role(s): owner, maintainer | [Documentation](https://docs.gitlab.com/ee/user/project/settings/project_access_tokens.html#create-a-project-access-token) and [video](https://youtu.be/v5Nj3Jy4vaI?t=335)  | [Documentation](https://docs.gitlab.com/ee/api/project_access_tokens.html) and [video](https://youtu.be/v5Nj3Jy4vaI?t=349)  |\n\n## Token management best practices\n\n### Principle of least privilege\n\nMitigate risk by restricting assigned permissions to tokens required for their respective tasks. This allows you to proactively predict and troubleshoot points of failure in your systems. You can do this by:\n* Selecting the right token for the right job. See the flowchart.  * Assign only the required scopes when creating a token. For example, use read-only scopes for tokens with auditor-like jobs. See [roles](https://docs.gitlab.com/ee/user/permissions.html#roles).  * Avoid granting administrative privileges unless specifically required.  * Enforce instance-wide default token [lifetimes](https://docs.gitlab.com/ee/administration/settings/account_and_limit_settings.html#set-a-lifetime-1).  * Regularly review and audit token permissions to ensure they align with current operational needs.  * Revoke tokens once the task is complete.\n\n### Service accounts\n\n[Service accounts](https://docs.gitlab.com/ee/user/profile/service_accounts.html) ensure tokens are tied to non-human entities, separating them from individual user accounts and reducing dependency on specific users. Instead of using personal accounts to generate tokens for automation, create service accounts with limited scopes. Benefits include:\n\n* Usage of service account tokens in CI/CD pipelines to avoid disruptions caused by user account changes  * Programmatically automate rotation processes, as personal accounts remain unaffected  * Clearer monitoring and auditing trail of actions taken by service accounts  * Service accounts with [no expiration](https://docs.gitlab.com/ee/user/profile/personal_access_tokens.html#create-a-service-account-personal-access-token-with-no-expiry-date) date  * Does not consume [a license seat](https://docs.gitlab.com/user/profile/service_accounts/#create-a-service-account)\n\nGitLab plans to release a new [Service Accounts UI](https://gitlab.com/groups/gitlab-org/-/epics/9965) as a counterpart to its [API-based creation](https://docs.gitlab.com/ee/api/user_service_accounts.html#create-a-service-account-user), designed to simplify the management of service accounts and their associated tokens. Check out the demo below on the programmatic usage of service accounts.\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://youtu.be/oZvjg0SCsqY?si=hSXKtq87lDeY9aRq\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n### Vulnerability tools\n\nLeverage GitLab’s built-in security tools to identify and mitigate vulnerabilities associated with token usage. For maximum coverage, it is recommended to use them all in tandem.\n\n* [Secret Detection](https://docs.gitlab.com/ee/user/application_security/secret_detection/): Scans your repository for hardcoded secrets like API tokens, passwords, and other sensitive information. View the [list of detected secrets](https://docs.gitlab.com/ee/user/application_security/secret_detection/detected_secrets.html).  * [Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/): Analyzes your source code for security vulnerabilities and [provides reports with UI findings in merge requests](https://docs.gitlab.com/ee/user/application_security/sast/#features), among other features.  * [Dependency Scanning](https://docs.gitlab.com/ee/user/application_security/dependency_scanning/): Ensures that third-party libraries used in your project do not expose token-related vulnerabilities.\n### Audit logs and monitoring\n\nMaintain token health by regularly reviewing audit logs and token usage, instance- and/or group-wide.\n\n* [Audit events](https://docs.gitlab.com/ee/user/compliance/audit_events.html): Enable audit event logging in GitLab to track token-related activities such as creation, usage, deletion and unusual API calls (unpermitted parameters in logs, and consistent triggers of the rate limiter). * [IP allowlisting](https://docs.gitlab.com/ee/administration/reporting/ip_addr_restrictions.html#configure-ip-address-restrictions): Helps prevent malicious users from hiding their activities behind multiple IP addresses.  * [Alerts](https://docs.gitlab.com/ee/operations/incident_management/alerts.html): Set up alerts for unusual activities (trigger paging for on-call rotations or be used to create incidents).  * [Credentials inventory](https://docs.gitlab.com/ee/administration/credentials_inventory.html): Complete control of all available access tokens with the ability to revoke as needed.  * [Notifications](https://docs.gitlab.com/ee/user/profile/notifications.html): Proactively handle any token (group, project, and personal) expiration notification emails you receive. Based on customer demand, this feature was recently extended to include 30-day and 60-day notifications from the seven-day default.   * [Webhooks](https://docs.gitlab.com/ee/user/project/integrations/webhooks.html#create-a-webhook): Access token webhooks can be configured on groups and projects to send seven-day token expiry events. This feature was also recently extended to include 30-day and 60-day notifications behind the **:extended_expiry_webhook_execution_setting** feature flag (disabled by default).\n\n## What's next\n\nWith GitLab’s large token catalog, there are ongoing [plans](https://gitlab.com/gitlab-org/gitlab/-/issues/502630) for consolidation with a focus on the lifetime, fine-grained scopes, consistent management, and usage. 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Learn how pipeline enforcement, automated triage, and AI remediation close the gap.",[721],"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.",[725,9,24],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":12,"template":13,"slug":728},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":730,"config":739},{"title":731,"description":732,"authors":733,"heroImage":735,"date":736,"category":9,"tags":737,"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.",[734],"Grant Hickman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1774375772/kpaaaiqhokevxxeoxvu0.png","2026-03-25",[9,23,565,25,26],"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_).",[26,9,25],{"featured":31,"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":243},"/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",[26,568],"Are you just managing tools or shipping innovation?",{"text":774,"config":775},"Get your DevOps maturity score",{"href":776,"dataGaName":765,"dataGaLocation":243},"/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":243},"/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":243},"/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":50,"dataGaLocation":812},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":505,"config":814},{"href":54,"dataGaName":55,"dataGaLocation":812},1777302643543]