[{"data":1,"prerenderedAt":812},["ShallowReactive",2],{"/en-us/blog/comprehensive-guide-to-gitlab-dast":3,"navigation-en-us":33,"banner-en-us":443,"footer-en-us":453,"blog-post-authors-en-us-Fernando Diaz":695,"blog-related-posts-en-us-comprehensive-guide-to-gitlab-dast":709,"blog-promotions-en-us":750,"next-steps-en-us":802},{"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":30,"tagSlugs":31,"__hash__":32},"blogPosts/en-us/blog/comprehensive-guide-to-gitlab-dast.yml","Comprehensive Guide To Gitlab Dast",[7],"fernando-diaz",null,"security",{"slug":11,"featured":12,"template":13},"comprehensive-guide-to-gitlab-dast",true,"BlogPost",{"heroImage":15,"body":16,"authors":17,"updatedDate":19,"date":20,"title":21,"tags":22,"description":25,"category":9},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1759320418/xjmqcozxzt4frx0hori3.png","Modern businesses entirely depend on web-based platforms for customer interactions, financial\ntransactions, data processing, and core business operations. As digital transformation\naccelerates and remote or hybrid work becomes the norm, the attack surface for web applications has\nexpanded dramatically, making them prime targets for cybercriminals. Therefore, securing web applications has become more critical than ever.\n\nWhile static code analysis catches vulnerabilities in source code, it cannot identify\nruntime security issues that emerge when applications interact with real-world\nenvironments, third-party services, and complex user workflows. This is where Dynamic\nApplication Security Testing ([DAST](https://docs.gitlab.com/user/application_security/dast/)) becomes invaluable. GitLab's integrated DAST solution provides teams with automated security testing capabilities directly within their CI/CD pipelines, on a schedule, or on-demand, enabling continuous security validation\nwithout disrupting development workflows.\n\n## Why DAST?\n\nDAST should be implemented because it provides critical runtime security validation by testing applications\nin their actual operating environment, identifying vulnerabilities that static analysis cannot detect.\nAdditionally, GitLab DAST can be seamlessly integrated into shift-left security workflows, and\ncan enhance compliance assurance along with risk management.\n\n### Runtime vulnerability detection\n\nDAST excels at identifying security vulnerabilities that only manifest when applications are running.\nUnlike static analysis tools that examine code at rest, DAST scanners interact with live applications\nas an external attacker would, uncovering issues such as:\n\n- **Authentication and session management flaws** that could allow unauthorized access\n- **Input validation vulnerabilities,** including SQL injection, cross-site scripting (XSS), and command injection\n- **Configuration weaknesses** in web servers, databases, and application frameworks\n- **Business logic flaws** that emerge from complex user interactions\n- **API security issues,** including improper authentication, authorization, and data exposure\n\nDAST complements other security testing approaches to provide comprehensive application security coverage. When combined with Static Application Security Testing ([SAST](https://docs.gitlab.com/user/application_security/sast/)), Software Composition Analysis ([SCA](https://docs.gitlab.com/user/application_security/dependency_scanning/)), manual\npenetration testing, and [many other scanner types](https://about.gitlab.com/solutions/application-security-testing/), DAST fills critical gaps in security validation:\n\n- **Black-box testing perspective** that mimics real-world attack scenarios\n- **Environment-specific testing** that validates security in actual deployment configurations\n- **Third-party component testing,** including APIs, libraries, and external services\n- **Configuration validation** across the entire application stack\n\n### Seamless shift-left security integration\n\nGitLab DAST seamlessly integrates into existing CI/CD pipelines, enabling teams to identify security\nissues early in the development lifecycle. This shift-left approach provides several key benefits:\n\n- **Cost reduction** — Fixing vulnerabilities during development is significantly less expensive than addressing them in production. Studies show that remediation costs can be 10 to 100 times higher in production environments.\n- **Faster time-to-market** — Automated security testing eliminates bottlenecks caused by manual security reviews, allowing teams to maintain rapid deployment schedules while ensuring security standards.\n- **Developer empowerment** — By providing immediate feedback on security issues, DAST helps developers build security awareness and improve their coding practices over time.\n\n### Compliance and risk management\n\nMany regulatory frameworks and industry standards require regular security testing of web applications.\nDAST helps organizations meet compliance requirements for standards such as:\n\n- **PCI DSS** for applications handling payment card data\n- **SOC 2** security controls for service organizations\n- **ISO 27001** information security management requirements\n\nThe automated nature of GitLab DAST ensures consistent, repeatable security testing that auditors can\nrely on, while detailed reporting provides the documentation needed for compliance validation.\n\n## Implementing DAST\n\nBefore implementing GitLab DAST, ensure your environment meets the following requirements:\n\n- **GitLab version and Ultimate subscription** — DAST is available in [GitLab Ultimate](https://about.gitlab.com/pricing/ultimate/) and requires GitLab 13.4 or later for full functionality; however, the [latest version](/releases/whats-new/) is recommended.\n- **Application accessibility** — Your application must be accessible via HTTP/HTTPS with a publicly reachable URL or accessible within your GitLab Runner's network.\n- **Authentication setup** — If your application requires authentication, prepare test credentials or configure authentication bypass mechanisms for security testing.\n\n### Basic implementation\n\nThe simplest way to add DAST to your pipeline is by including the DAST template in your [`.gitlab-ci.yml`](https://docs.gitlab.com/ci/#step-1-create-a-gitlab-ciyml-file) file\nand providing a website to scan:\n\n```yaml\n\ninclude:\n  - template: DAST.gitlab-ci.yml\n\nvariables:\n  DAST_WEBSITE: \"https://your-application.example.com\"\n\n```\n\nThis basic configuration will:\n- Run a DAST scan against your specified website\n- Generate a security report in GitLab's security dashboard\n- Fail the pipeline if high-severity vulnerabilities are detected\n- Store scan results as pipeline artifacts\n\nHowever, it is suggested to gain the full benefit of [CI/CD](https://about.gitlab.com/topics/ci-cd/), you can first deploy the application\nand set DAST to run only after an application has been deployed. The application URL can be\ndynamically created and the DAST job can be configured fully with [GitLab Job syntax](https://docs.gitlab.com/ci/yaml/).\n\n```yaml\n\nstages:\n  - build\n  - deploy\n  - dast\n\ninclude:\n  - template: Security/DAST.gitlab-ci.yml\n\n# Builds and pushes application to GitLab's built-in container registry\nbuild:\n  stage: build\n  variables:\n    IMAGE: $CI_REGISTRY_IMAGE/$CI_COMMIT_REF_SLUG:$CI_COMMIT_SHA\n  before_script:\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n  script:\n    - docker build -t $IMAGE .\n    - docker push $IMAGE\n\n# Deploys application to your suggested target, setsup the dast site dynamically, requires build to complete\ndeploy:\n  stage: deploy\n  script:\n    - echo \"DAST_WEBSITE=http://your-application.example.com\" >> deploy.env\n    - echo \"Perform deployment here\"\n  environment:\n    name: $DEPLOY_NAME\n    url: http://your-application.example.com\n  artifacts:\n    reports:\n      dotenv: deploy.env\n  dependencies:\n    - build\n\n# Configures DAST to run a an active scan on non-main branches, and a passive scan on the main branches and requires a deployment to complete before it is run\ndast:\n  stage: dast\n  rules:\n    - if: $CI_COMMIT_REF_NAME == $CI_DEFAULT_BRANCH\n      variables:\n        DAST_FULL_SCAN: \"false\"\n    - if: $CI_COMMIT_REF_NAME != $CI_DEFAULT_BRANCH\n      variables:\n        DAST_FULL_SCAN: \"true\"\n  dependencies:\n    - deploy\n\n```\n\nYou can learn from an example by seeing the [Tanuki Shop](https://gitlab.com/gitlab-da/tutorials/security-and-governance/tanuki-shop) demo application, which generates the\nfollowing pipeline:\n\n![Standard DAST Pipeline](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118303/rr3cyxjwyecxbmrdxon6.png)\n\n### Understanding passive vs. active scans\n\nIn the example above we enabled active scanning for non-default branches:\n\n```yaml\n\n- if: $CI_COMMIT_REF_NAME != $CI_DEFAULT_BRANCH\n  variables:\n    DAST_FULL_SCAN: \"true\"\n\n```\n\nGitLab DAST employs two distinct scanning methodologies (passive and active), each serving\ndifferent security testing needs.\n\n**Passive scans** analyze application responses without sending potentially harmful requests. This approach:\n\n- Examines HTTP headers, cookies, and response content for security misconfigurations\n- Identifies information disclosure vulnerabilities like exposed server versions or stack traces\n- Detects missing security headers (CSP, HSTS, X-Frame-options)\n- Analyzes SSL/TLS configuration and certificate issues\n\n**Active scans** send crafted requests designed to trigger vulnerabilities. This approach:\n\n- Tests for injection vulnerabilities (SQL injection, XSS, command injection)\n- Attempts to exploit authentication and authorization flaws\n- Validates input sanitization and output encoding\n- Tests for business logic vulnerabilities\n\n**Note:** The DAST scanner is set to passive by default.\n\nDAST has several configuration options that can be applied via environment variables.\nFor a list of all the possible configuration options for DAST, see the [DAST documentation](https://docs.gitlab.com/user/application_security/dast/browser/configuration/customize_settings/).\n\n### Authentication configuration\n\nDAST requires authentication configuration in CI/CD jobs to achieve complete security coverage. Authentication enables DAST to simulate real attacks and test user-specific features only accessible after login. The DAST job typically authenticates by submitting login forms in a browser, then verifies success before continuing to crawl the application with saved credentials. Failed authentication stops the job.\n\nSupported authentication methods:\n- Single-step login form\n- Multi-step login form\n- Authentication to URLs outside the target scope\n\nHere is an example for a single-step login form in a [Tanuki Shop MR](https://gitlab.com/gitlab-da/tutorials/security-and-governance/tanuki-shop/-/merge_requests/20) which adds\nadmin authentication to non-default branches.\n\n```yaml\n\ndast:\n  stage: dast\n  before_script:\n    - echo \"DAST_TARGET_URL set to '$DAST_TARGET_URL'\" # Dynamically loaded from deploy job\n    - echo \"DAST_AUTH_URL set to '$DAST_TARGET_URL'\" # Dynamically loaded from deploy jobs\n  rules:\n    - if: $CI_COMMIT_REF_NAME == $CI_DEFAULT_BRANCH\n      variables:\n        DAST_FULL_SCAN: \"false\"\n    - if: $CI_COMMIT_REF_NAME != $CI_DEFAULT_BRANCH\n      variables:\n        DAST_FULL_SCAN: \"true\" # run both passive and active checks\n        DAST_AUTH_USERNAME: \"admin@tanuki.local\" # The username to authenticate to in the website\n        DAST_AUTH_PASSWORD: \"admin123\" # The password to authenticate to in the website\n        DAST_AUTH_USERNAME_FIELD: \"css:input[id=email]\" # A selector describing the element used to enter the username on the login form\n        DAST_AUTH_PASSWORD_FIELD: \"css:input[id=password]\" # A selector describing the element used to enter the password on the login form\n        DAST_AUTH_SUBMIT_FIELD: \"css:button[id=loginButton]\" # A selector describing the element clicked on to submit the login form\n        DAST_SCOPE_EXCLUDE_ELEMENTS: \"css:[id=navbarLogoutButton]\" # Comma-separated list of selectors that are ignored when scanning\n        DAST_AUTH_REPORT: \"true\" # generate a report detailing steps taken during the authentication process\n        DAST_REQUEST_COOKIES: \"welcomebanner_status:dismiss,cookieconsent_status:dismiss\" # A cookie name and value to be added to every request\n        DAST_CRAWL_GRAPH: \"true\" # generate an SVG graph of navigation paths visited during crawl phase of the scan\n  dependencies:\n    - deploy-kubernetes\n\n```\n\nYou can see if the authentication was successful by viewing the job logs:\n\n![Auth logs](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118293/zdxgwb6jmseyzwcjscrz.png)\n\nOnce this job completes it provides an authentication report which includes screenshots of the login page:\n\n![Auth report](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118292/idm62deg3ezeehcubmc1.png)\n\nYou can also see more examples on DAST with authentication in our [DAST demos](https://gitlab.com/gitlab-org/security-products/demos/dast/) group.\nTo learn more about how to perform DAST with authentication with your specific requirements, see the [DAST authentication documentation](https://docs.gitlab.com/user/application_security/dast/browser/configuration/authentication/).\n\nWatch this video demonstration of GitLab DAST authentication configuration:\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n\u003Ciframe src=\"https://www.youtube.com/embed/q_oAgEYILc8?si=b_kll6G7MxssQE8j\" allowfullscreen=\"true\" title=\"GitLab DAST Tutorial Video\">\u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n## Viewing results in MR\n\nGitLab's DAST seamlessly integrates security scanning into your development workflow\nby displaying results directly within merge requests:\n\n![DAST MR 1](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118293/rrx4n3pgxi9vmzlas8vp.png)\n![DAST MR 2](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118294/rh9vwv6ohoaenpvicujm.png)\n![DAST MR 3](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118294/ficelmulsc0r7bijf24m.png)\n\nThese results include comprehensive vulnerability data within MRs to help developers identify and address\nsecurity issues before code is merged. Here's what DAST typically reports:\n\n### Vulnerability details\n* Vulnerability name and type (e.g., SQL injection, XSS, CSRF)\n* Severity level (Critical, High, Medium, Low, Info)\n* CVSS score when applicable\n* Common Weakness Enumeration (CWE) identifier\n* Confidence level of the finding\n\n### Location information\n* URL/endpoint where the vulnerability was detected\n* HTTP method used (GET, POST, etc.)\n* Request/response details showing the vulnerable interaction\n* Parameter names that are vulnerable\n* Evidence demonstrating the vulnerability\n\n#### Technical context\n* Description of the vulnerability and potential impact\n* Proof of concept showing how the vulnerability can be exploited\n* Request/response pairs that triggered the finding\n* Scanner details (which DAST tool detected it)\n\n### Remediation guidance\n* Solution recommendations for fixing the vulnerability\n* References to security standards (OWASP, etc.)\n* Links to documentation for remediation steps\n\n## Viewing results in GitLab Vulnerability Report\n\nFor managing vulnerabilities located in the default (or production) branch, the GitLab Vulnerability Report provides a centralized dashboard for monitoring all security findings (in the default branch) across your entire project or organization. This comprehensive view aggregates all security scan results, offering filtering and sorting capabilities to help security teams prioritize remediation efforts.\n\n![Vulnerability Report](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118304/o8jjgngtxqplcgux9h5p.png)\n\nWhen selecting a vulnerability, you are taken to its vulnerability page:\n\n![Vulnerability Page 1](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118303/rolcgxhe0lh2s54zz2kc.png)\n![Vulnerability Page 2](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118303/dubic3yacd5n11ine1vi.png)\n![Vulnerability Page 3](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118303/iojrm3zasqxljuybbqcs.png)\n\nJust like in merge requests, the vulnerability page provides comprehensive vulnerability data, as seen above. From here you can triage vulnerabilities by assigning them with a status:\n\n* Needs triage (Default)\n* Confirmed\n* Dismissed (Acceptable risk, False positive, Mitigating control, Used in tests, Not applicable)\n* Resolved\n\nWhen a vulnerability status is changed, the audit log includes a note of who changed it, when it was changed, and the reason it was changed. This comprehensive system allows security teams to efficiently prioritize, track, and manage vulnerabilities throughout their lifecycle with clear accountability and detailed risk context.\n\n## On-demand and scheduled DAST\n\nGitLab provides flexible scanning options beyond standard CI/CD pipeline integration through\non-demand and scheduled DAST scans. On-demand scans allow security teams and developers to\ninitiate DAST testing manually whenever needed, without waiting for code commits or pipeline triggers.\nThis capability is particularly valuable for ad-hoc security assessments, incident response scenarios,\nor when testing specific application features that may not be covered in regular pipeline scans.\n\n![On-demand 1](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118296/hs3fhn42ceycmd94oaua.png)\n![On-demand 2](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118298/wiptmr948xey6rrodosg.png)\n\nOn-demand scans can be configured with custom parameters, target URLs, and scanning profiles, making\nthem ideal for focused security testing of particular application components or newly-deployed features.\nScheduled DAST scans provide automated, time-based security testing that operates independently of\nthe development workflow. These scans can be configured to run daily, weekly, or at custom intervals,\nensuring continuous security monitoring of production applications.\n\n![Scheduling DAST](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118300/dbxgkeahij4fklkpcpck.png)\n\nTo learn how to implement on-demand or scheduled scans within your project, see the\n[DAST on-demand scan documentation](https://docs.gitlab.com/user/application_security/dast/on-demand_scan/)\n\n## DAST in compliance workflows\n\nGitLab's security policies framework allows organizations to enforce consistent security\nstandards across all projects, while maintaining flexibility for different teams and environments.\nSecurity policies enable centralized governance of DAST scanning requirements, ensuring that\ncritical applications receive appropriate security testing without requiring individual project\nconfiguration. By defining security policies at the group or instance level, security teams can\nmandate DAST scans for specific project types, deployment environments, or risk classifications.\n\n**Scan/Pipeline Execution Policies** can be configured to automatically trigger DAST scans based on\nspecific conditions such as merge requests to protected branches, scheduled intervals, or deployment events.\nFor example, a policy might require full active DAST scans for all applications before production deployment,\nwhile allowing passive scans only for development branches. These policies can include custom variables,\nauthentication configurations, and exclusion rules that are automatically applied to all covered projects,\nreducing the burden on development teams and ensuring security compliance.\n\n![Scan Execution Policy](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118299/twe0967sayasvassimf3.png)\n\n**Merge Request Approval Policies** provide an additional layer of security governance by enforcing human\nreview for code changes that may impact security. These policies can be configured to require security team\napproval when DAST scans detect new vulnerabilities, when security findings exceed defined thresholds, or\nwhen changes affect security-critical components. For example, a policy might automatically require approval\nfrom a designated security engineer when DAST findings include high-severity vulnerabilities, while allowing\nlower-risk findings to proceed with standard code review processes.\n\n![MR Approval Policy](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118295/w0odyhf3gnkxis3f61ma.png)\n\nTo learn more about GitLab security policies, see the [policy documentation](https://docs.gitlab.com/user/application_security/policies/).\nAdditionally, for compliance, GitLab provides [Security Inventory](https://docs.gitlab.com/user/application_security/security_inventory/)\nand [Compliance center](https://docs.gitlab.com/user/compliance/compliance_center/), which can allow you to oversee\nif DAST is running in your environment and where it is required.\n\n![Security Inventory](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758118300/hro6gykf7igpnnczmpyg.png)\n\nTo learn more about these features, visit our [software compliance solutions page](https://about.gitlab.com/solutions/software-compliance/).\n\n## Summary\n\nGitLab DAST represents a powerful solution for integrating dynamic security testing into modern development workflows. By implementing DAST in your CI/CD pipeline, your team gains the ability to automatically detect runtime vulnerabilities, maintain compliance with security standards, and build more secure applications without sacrificing development velocity.\n\nThe key to successful DAST implementation lies in starting with basic configuration and gradually expanding to more sophisticated scanning profiles as your security maturity grows. Begin with simple website scanning, then progressively add authentication, custom exclusions, and advanced reporting to match your specific security requirements.\n\nRemember that DAST is most effective when combined with other security testing approaches. Use it alongside static analysis, dependency scanning, and manual security reviews to create a comprehensive security testing strategy. The automated nature of GitLab DAST ensures that security testing becomes a consistent, repeatable part of your development process rather than an afterthought.\n\n> To learn more about GitLab security, check out our [security testing solutions page](https://about.gitlab.com/solutions/application-security-testing/). To get started with GitLab DAST, [sign up for a free trial of GitLab Ultimate today](https://about.gitlab.com/free-trial/devsecops/).",[18],"Fernando Diaz","2025-10-01","2025-09-17","A comprehensive guide to GitLab DAST",[9,23,24],"tutorial","testing","DevSecOps teams can learn how to implement and configure dynamic application security testing, perform passive/active scans, and set security policies.","yml",{},"/en-us/blog/comprehensive-guide-to-gitlab-dast",{"title":21,"description":25},"en-us/blog/comprehensive-guide-to-gitlab-dast",[9,23,24],"pL0TQwzpy3mHmMpp6jiqR1Ul_OYm31v0lSDShsfdjeY",{"data":34},{"logo":35,"freeTrial":40,"sales":45,"login":50,"items":55,"search":363,"minimal":394,"duo":413,"switchNav":422,"pricingDeployment":433},{"config":36},{"href":37,"dataGaName":38,"dataGaLocation":39},"/","gitlab logo","header",{"text":41,"config":42},"Get free 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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.",[715],"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.",[719,9,528],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":12,"template":13,"slug":722},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":724,"config":735},{"title":725,"description":726,"authors":727,"heroImage":729,"date":730,"category":9,"tags":731,"body":734},"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.",[728],"Grant Hickman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1774375772/kpaaaiqhokevxxeoxvu0.png","2026-03-25",[9,23,560,732,733],"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":736,"featured":12,"template":13},"auto-dismiss-vulnerability-management-policy",{"content":738,"config":747},{"title":739,"description":740,"authors":741,"heroImage":743,"date":744,"body":745,"category":9,"tags":746},"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.",[742],"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_).",[733,9,732],{"featured":748,"template":13,"slug":749},false,"gitlab-18-10-brings-ai-native-triage-and-remediation",{"promotions":751},[752,766,777,788],{"id":753,"categories":754,"header":756,"text":757,"button":758,"image":763},"ai-modernization",[755],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":759,"config":760},"Get your AI maturity score",{"href":761,"dataGaName":762,"dataGaLocation":237},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":764},{"src":765},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":767,"categories":768,"header":769,"text":757,"button":770,"image":774},"devops-modernization",[733,563],"Are you just managing tools or shipping innovation?",{"text":771,"config":772},"Get your DevOps maturity score",{"href":773,"dataGaName":762,"dataGaLocation":237},"/assessments/devops-modernization-assessment/",{"config":775},{"src":776},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":778,"categories":779,"header":780,"text":757,"button":781,"image":785},"security-modernization",[9],"Are you trading speed for security?",{"text":782,"config":783},"Get your security maturity score",{"href":784,"dataGaName":762,"dataGaLocation":237},"/assessments/security-modernization-assessment/",{"config":786},{"src":787},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":789,"paths":790,"header":793,"text":794,"button":795,"image":800},"github-azure-migration",[791,792],"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":796,"config":797},"See how GitLab compares to GitHub",{"href":798,"dataGaName":799,"dataGaLocation":237},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":801},{"src":776},{"header":803,"blurb":804,"button":805,"secondaryButton":810},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":806,"config":807},"Get your free trial",{"href":808,"dataGaName":44,"dataGaLocation":809},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":499,"config":811},{"href":48,"dataGaName":49,"dataGaLocation":809},1777302594888]