[{"data":1,"prerenderedAt":810},["ShallowReactive",2],{"/en-us/blog/vulnerability-triage-made-simple-with-gitlab-security-analyst-agent":3,"navigation-en-us":34,"banner-en-us":444,"footer-en-us":454,"blog-post-authors-en-us-Fernando Diaz":696,"blog-related-posts-en-us-vulnerability-triage-made-simple-with-gitlab-security-analyst-agent":710,"blog-promotions-en-us":749,"next-steps-en-us":800},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":28,"path":29,"publishedDate":20,"seo":30,"stem":31,"tagSlugs":32,"__hash__":33},"blogPosts/en-us/blog/vulnerability-triage-made-simple-with-gitlab-security-analyst-agent.yml","Vulnerability Triage Made Simple With Gitlab Security Analyst Agent",[7],"fernando-diaz",null,"security",{"slug":11,"featured":12,"template":13},"vulnerability-triage-made-simple-with-gitlab-security-analyst-agent",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"category":9,"tags":21,"body":25},"AI-powered vulnerability triaging with GitLab Duo Security Agent","Learn how this GitLab Duo Agent Platform capability uses AI to prioritize vulnerabilities, reduce alert fatigue, and help teams focus on critical security risks.",[18],"Fernando Diaz","https://res.cloudinary.com/about-gitlab-com/image/upload/v1756122536/akivvcnafog9c4dhhzkp.png","2026-01-06",[22,23,24,9],"ai-ml","product","tutorial","Security vulnerabilities are discovered constantly in modern applications. Development teams often face hundreds or thousands\nof findings from security scanners, making it challenging to identify which vulnerabilities pose the greatest risk and should\nbe prioritized. This is where effective vulnerability triaging becomes essential.\n\nIn this article, we'll explore how GitLab's [integrated security scanning capabilities](https://docs.gitlab.com/user/application_security/) combined with the [GitLab Duo Security Analyst Agent](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/security_analyst_agent/)\ncan transform vulnerability management from a time-consuming manual process into an intelligent, efficient workflow.\n\n> :bulb: Join GitLab Transcend on February 10 to learn how agentic AI transforms software delivery. Hear from customers and discover how to jumpstart your own modernization journey. [Register now.](https://about.gitlab.com/events/transcend/virtual/)\n\n## What is vulnerability triaging?\n\nVulnerability triaging is the process of analyzing, prioritizing, and deciding how to address security findings discovered in\nyour applications. Not all vulnerabilities are created equal — some represent critical risks requiring immediate attention, while\nothers may be false positives or pose minimal threat in your specific context.\n\nTraditional triaging involves:\n\n- **Reviewing scan results** from multiple security tools\n- **Assessing severity** based on CVSS scores and exploitability\n- **Understanding context** such as whether vulnerable code is actually reachable\n- **Prioritizing remediation** based on business impact and risk\n- **Tracking resolution** through to deployment\n\nThis process becomes overwhelming when dealing with large codebases and frequent scans. GitLab addresses these challenges through\nintegrated security scanning and AI-powered analysis.\n\n## How to add integrated security scanners in GitLab\n\nGitLab provides built-in security scanners that integrate seamlessly into your CI/CD pipelines. These scanners run automatically\nduring pipeline execution and populate GitLab's [Vulnerability Report](https://docs.gitlab.com/user/application_security/vulnerability_report/_) with findings from the default branch.\n\n### Available security scanners\n\nGitLab offers the following security scanning capabilities:\n\n- **[Static Application Security Testing (SAST)](https://docs.gitlab.com/user/application_security/sast/)**: Analyzes source code for vulnerabilities\n- **[Dependency Scanning](https://docs.gitlab.com/user/application_security/dependency_scanning/)**: Identifies vulnerabilities in project dependencies\n- **[Container Scanning](https://docs.gitlab.com/user/application_security/container_scanning/)**: Scans Docker images for known vulnerabilities\n- **[Dynamic Application Security Testing (DAST)](https://docs.gitlab.com/user/application_security/dast/browser/)**: Tests running applications for vulnerabilities\n- **[Secret Detection](https://docs.gitlab.com/user/application_security/secret_detection/)**: Finds accidentally committed secrets and credentials\n- **[Infrastructure-as-Code (IaC) Scanning](https://docs.gitlab.com/user/application_security/iac_scanning/)**: Analyzes infrastructure as code for misconfigurations\n- **[API Security Testing](https://docs.gitlab.com/user/application_security/api_security_testing/)**: Test web APIs to help discover bugs and potential security issues\n- **[Web API Fuzzing](https://docs.gitlab.com/user/application_security/api_fuzzing/)**: Passes unexpected values to API operation parameters to cause unexpected behavior and errors in the backend\n\n### Example: Adding SAST and Dependency Scanning\n\nTo enable security scanning, add the scanners to your `.gitlab-ci.yml` file.\n\nIn this example, we are including SAST and Dependency Scanning templates which automatically run those scanners on the test stage.\nEach scanner can be overwritten using variables (which differ for each scanner). For example, the `SAST_EXCLUDED_PATHS` variable\ntells SAST to skip the directories/files provided. Security jobs can be further overwritten using the [GitLab Job Syntax](https://docs.gitlab.com/ci/yaml/).\n\n```yaml\ninclude:\n  - template: Security/SAST.gitlab-ci.yml\n  - template: Security/Dependency-Scanning.gitlab-ci.yml\n\nstages:\n  - test\n\nvariables:\n  SAST_EXCLUDED_PATHS: \"spec/, test/, tests/, tmp/\"\n```\n\n### Example: Adding Container Scanning\n\nGitLab provides a built-in [container registry](https://docs.gitlab.com/user/packages/container_registry/)\nwhere you can store container images for each GitLab project. To scan those containers for vulnerabilities,\nyou can enable container scanning.\n\nThis example shows how a container is built and pushed in the `build-container` job running in the `build` stage\nand how it is then scanned in the same pipeline in the `test` stage:\n\n```yaml\ninclude:\n  - template: Security/Container-Scanning.gitlab-ci.yml\n\nstages:\n  - build\n  - test\n\nbuild-container:\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\ncontainer_scanning:\n  variables:\n    CS_IMAGE: $CI_REGISTRY_IMAGE/$CI_COMMIT_REF_SLUG:$CI_COMMIT_SHA\n```\n\nOnce configured, these scanners execute automatically in your pipeline and report findings to\nthe [Vulnerability Report](https://docs.gitlab.com/user/application_security/vulnerability_report/).\n\n**Note:** Although not covered in this blog, in merge requests, scanners show the diff of vulnerabilities from a feature\nbranch to the target branch. Additionally, granular [security policies](https://docs.gitlab.com/user/application_security/policies/) can be created to prevent vulnerable code\nfrom being merged (without approval) if vulnerabilities are detected, as well as force scanners to run, regardless of how the\n`.gitlab-ci.yml` is defined.\n\n## Triaging using the Vulnerability Report and Pages\n\nAfter scanners run, GitLab aggregates all findings in centralized views that make triaging more manageable.\n\n### Accessing the Vulnerability Report\n\nNavigate to **Security & Compliance > Vulnerability Report** in your project or group. This page displays all\ndiscovered vulnerabilities with key information:\n\n- Severity levels (Critical, High, Medium, Low, Info)\n- Status (Detected, Confirmed, Dismissed, Resolved)\n- Scanner type that detected the vulnerability\n- Affected files and lines of code\n- Detection date and pipeline information\n\n![Vulnerability Report](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072457/jsz5qcti9pse1myyzktd.png)\n\n### Filtering and organizing vulnerabilities\n\nThe Vulnerability Report provides powerful filtering options:\n\n- Filter by severity, status, scanner, identifier, and reachability\n- Group by severity, status, scanner, OWASP Top 10\n- Search for specific CVEs or vulnerability names\n- Sort by detection date or severity\n- View trends over time with the security dashboard\n\n### Manual workflow triage\n\nTraditional triaging in GitLab involves:\n\n1. **Reviewing each vulnerability** by clicking into the detail page\n2. **Assessing the description** and understand the potential impact\n3. **Examining the affected code** through integrated links\n4. **Checking for existing fixes** or patches in dependencies\n5. **Setting status** (Confirm, Dismiss with reason, or create an issue)\n6. **Assigning ownership** for remediation\n\nThis is an example of vulnerability data provided to allow for triage including the code flow:\n\n![Vulnerability Page 1](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072471/imy4qfc89ajoc42auqs3.png)\n\n\n![Vulnerability Page 2](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072473/g7dfge2acunebf9oa99g.png)\n\n\n![Vulnerability Code Flow](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072468/wr2i9ry5rgzwhimmo793.png)\n\n\nWhen on the vulnerability data page, you can select **Edit vulnerability** to change its\nstatus as well as provide a reason. Then you can create an issue and assign ownership for remediation.\n\n\n![Vulnerability Page - Status Change](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072466/t0m8ewo82wbgo12d3vip.png)\n\n\nWhile this workflow is comprehensive, it requires security expertise and can be time-consuming when dealing with hundreds\nof findings. This is where GitLab Duo Security Analyst Agent, part of [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/), becomes invaluable.\n\n## About Security Analyst Agent and how to set it up\n\nGitLab Duo Security Analyst Agent is an AI-powered tool that automates vulnerability analysis and triaging.\nThe agent understands your application context, evaluates risk intelligently, and provides actionable recommendations.\n\n### What Security Analyst Agent does\n\nThe agent analyzes vulnerabilities by:\n\n- **Evaluating exploitability** in your specific codebase context\n- **Assessing reachability** to determine if vulnerable code paths are actually used\n- **Prioritizing based on risk** rather than just CVSS scores\n- **Explaining vulnerabilities** in clear, actionable language\n- **Recommending remediation steps** specific to your application\n- **Reducing false positives** through contextual analysis\n\n### Prerequisites\n\nTo use Security Analyst Agent, you need:\n\n- GitLab Ultimate subscription with GitLab Duo Agent Platform enabled\n- Security scanners configured in your project\n- At least one vulnerability in your Vulnerability Report\n\n### Enabling Security Analyst Agent\n\nSecurity Analyst Agent is a [foundational agent](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/).\nUnlike the general-purpose GitLab Duo agent, foundational agents understand the unique workflows, frameworks, and best practices\nof their specialized domains. Foundational agents can be accessed directly from your project without any additional configuration.\n\nYou can find Security Analyst Agent in the [AI Catalog](https://docs.gitlab.com/user/duo_agent_platform/ai_catalog/):\n\n![AI Catalog](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072458/nv1qwisln1hxbgzeva7a.png)\n\n\nTo dive in and see the details of the agent, such as its system prompt and tools:\n\n1. Navigate to **gitlab.com/explore/**.\n2. Select **AI Catalog** from the side tab.\n3. Select **Security Analyst Agent** from the list.\n\n\n![Security Analyst Agent Details 1](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072470/wjbwpgy6ipbblderxfdb.png)\n\n\n\n![Security Analyst Agent Details 2](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072469/dzhbqxt2cmwwvxeaqxfe.png)\n\n\n\nThe agent is integrated directly into your existing workflow without requiring additional configuration beyond the defined\nprerequistes.\n\n## Using Security Analyst Agent to find most critical vulnerabilities\n\nNow let's explore how to leverage Security Analyst Agent to quickly identify and prioritize the vulnerabilities\nthat matter most.\n\n### Starting an analysis\n\nTo start an analysis, navigate to your GitLab project (ensure it meets the prerequistes). Then\nyou can open GitLab Duo Chat and select the **Security Agent**.\n\n![Security Analyst Agent selection](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072464/rrdk9aidkck2oeddtjm0.png)\n\n\n\nFrom the chat, select the model to use with the agent and make sure to enable Agentic mode.\n\n![Security Analyst Agent - Model Selection](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072458/hvccofv3nadkpkzfevx0.png)\n\n\n\nA chat will open where you can engage with Security Analyst Agent by using the agent's conversational\ninterface. This agent can perform:\n\n- **Vulnerability triage**: Analyze and prioritize security findings across different scan types.\n- **Risk assessment**: Evaluate the severity, exploitability, and business impact of vulnerabilities.\n- **False positive identification**: Distinguish genuine threats from benign findings.\n- **Compliance management**: Understand regulatory requirements and remediation timelines.\n- **Security reporting**: Generate summaries of security posture and remediation progress.\n- **Remediation planning**: Create actionable plans to address security vulnerabilities.\n- **Security workflow automation**: Streamline repetitive security assessment tasks.\n\nAdditionally, these are the tools which Security Analyst Agent has at its disposal:\n\n![Security Analyst Agent - tools](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072470/bgg2icxb0hp5g0zmerj3.png)\n\n\n\nFor example, I can ask \"**What are the most critical vulnerabilities and which vulnerabilities should I address first?**\"\nto make it easy to determine what is important. The agent will respond as follows:\n\n![Security Analyst Agent 1](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072476/hic8szspoobbmntxw5js.png)\n\n\n\n![Security Analyst Agent 2](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072463/iytr116dfkno3akr2xgf.png)\n\n\n\n![Security Analyst Agent 3](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072464/gzmggi6xu1bzobqdyxhg.png)\n\n\n\n![Security Analyst Agent 4](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072457/gv7ncdauqw8eszaxdcpf.png)\n\n\n\n![Security Analyst Agent 5](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072457/ifj4xp8kfv9ranfwav3h.png)\n\n\n\n![Security Analyst Agent 6](https://res.cloudinary.com/about-gitlab-com/image/upload/v1766072457/arr8jfqn52zy1q72jqh5.png)\n\n### Example queries for effective triaging\n\nHere are powerful queries to use with the Security Analyst Agent:\n\n**Identify critical issues:**\n\n```text\n\"Show me vulnerabilities that are actively exploitable in our production code\"\n```\n\n**Focus on reachable vulnerabilities:**\n\n```text\n\"Which high-severity vulnerabilities are in code paths that are actually executed?\"\n```\n\n**Understand dependencies:**\n\n```text\n\"What are the most critical dependency vulnerabilities and are patches available?\"\n```\n\n\n**Get remediation guidance:**\n\n```text\n\"Explain how to fix the SQL injection vulnerability in user authentication\"\n```\n\nYou can also directly assign developers to vulnerabilities.\n\n### Understanding agent recommendations\n\nWhen Security Analyst Agent analyzes vulnerabilities, it provides:\n\n**Risk assessment**: The agent explains why a vulnerability is critical beyond just the CVSS score, considering your\napplication's specific architecture and usage patterns.\n\n**Exploitability analysis**: It determines whether vulnerable code is actually reachable and exploitable in your\nenvironment, helping filter out theoretical risks.\n\n**Remediation steps**: The agent provides specific, actionable guidance on how to fix vulnerabilities, including code\nexamples when appropriate.\n\n**Priority ranking**: Instead of overwhelming you with hundreds of findings, the agent helps identify the top issues\nthat should be addressed first.\n\n### Real-world workflow example\n\nHere's how a typical triaging session might look:\n\n1. **Start with the big picture**: \"Analyze the security posture of this project and highlight the top 5 most critical vulnerabilities.\"\n\n2. **Dive into specifics**: For each critical vulnerability identified, ask \"Is this vulnerability actually exploitable in our application?\"\n\n3. **Plan remediation**: \"What's the recommended fix for this SQL injection issue, and are there any side effects to consider?\"\n\n4. **Track progress**: After addressing critical issues, ask \"What vulnerabilities should I prioritize next?\"\n\n### Benefits of agent-assisted triaging\n\nUsing Security Analyst Agent transforms vulnerability management:\n\n- **Time savings**: Reduce hours of manual analysis to minutes of guided review\n- **Better prioritization**: Focus on vulnerabilities that actually pose risk to your specific application\n- **Knowledge transfer**: Learn security best practices through agent explanations\n- **Consistent standards**: Apply consistent triaging logic across all projects\n- **Reduced alert fatigue**: Filter noise and false positives effectively\n\n## Get started today\n\nVulnerability triaging doesn't have to be an overwhelming manual process. By combining GitLab's integrated security scanners\nwith GitLab Duo Security Analyst Agent, development teams can quickly identify and prioritize the vulnerabilities that\ntruly matter.\n\nThe agent's ability to understand context, assess real risk, and provide actionable guidance transforms security scanning\nfrom a compliance checkbox into a practical, efficient part of your development workflow. Instead of drowning in hundreds\nof vulnerability reports, you can focus your energy on addressing the issues that actually threaten your application's security.\n\nStart by enabling security scanners in your GitLab pipelines, then leverage Security Analyst Agent to make intelligent,\ninformed decisions about vulnerability remediation. Your future self — and your security team — will thank you.\n\n> **Ready to get started?** Check out the [GitLab Duo Agent Platform documentation](https://docs.gitlab.com/user/duo_agent_platform/) and\n[security scanning documentation](https://docs.gitlab.com/ee/user/application_security/) to begin transforming your\nvulnerability management workflow today.\n","yml",{},true,"/en-us/blog/vulnerability-triage-made-simple-with-gitlab-security-analyst-agent",{"title":15,"description":16},"en-us/blog/vulnerability-triage-made-simple-with-gitlab-security-analyst-agent",[22,23,24,9],"ONBGsz159IgGhsBC0BCs2Al3CepbAfIzWD_YC-6cBtY",{"data":35},{"logo":36,"freeTrial":41,"sales":46,"login":51,"items":56,"search":364,"minimal":395,"duo":414,"switchNav":423,"pricingDeployment":434},{"config":37},{"href":38,"dataGaName":39,"dataGaLocation":40},"/","gitlab logo","header",{"text":42,"config":43},"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.",[716],"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.",[720,9,529],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":28,"template":13,"slug":723},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":725,"config":735},{"title":726,"description":727,"authors":728,"heroImage":730,"date":731,"category":9,"tags":732,"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.",[729],"Grant Hickman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1774375772/kpaaaiqhokevxxeoxvu0.png","2026-03-25",[9,24,561,733,23],"features","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":28,"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_).",[23,9,733],{"featured":12,"template":13,"slug":748},"gitlab-18-10-brings-ai-native-triage-and-remediation",{"promotions":750},[751,764,775,786],{"id":752,"categories":753,"header":754,"text":755,"button":756,"image":761},"ai-modernization",[22],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":757,"config":758},"Get your AI maturity score",{"href":759,"dataGaName":760,"dataGaLocation":238},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":762},{"src":763},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":765,"categories":766,"header":767,"text":755,"button":768,"image":772},"devops-modernization",[23,564],"Are you just managing tools or shipping innovation?",{"text":769,"config":770},"Get your DevOps maturity score",{"href":771,"dataGaName":760,"dataGaLocation":238},"/assessments/devops-modernization-assessment/",{"config":773},{"src":774},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":776,"categories":777,"header":778,"text":755,"button":779,"image":783},"security-modernization",[9],"Are you trading speed for security?",{"text":780,"config":781},"Get your security maturity score",{"href":782,"dataGaName":760,"dataGaLocation":238},"/assessments/security-modernization-assessment/",{"config":784},{"src":785},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":787,"paths":788,"header":791,"text":792,"button":793,"image":798},"github-azure-migration",[789,790],"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":794,"config":795},"See how GitLab compares to GitHub",{"href":796,"dataGaName":797,"dataGaLocation":238},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":799},{"src":774},{"header":801,"blurb":802,"button":803,"secondaryButton":808},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":804,"config":805},"Get your free trial",{"href":806,"dataGaName":45,"dataGaLocation":807},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":500,"config":809},{"href":49,"dataGaName":50,"dataGaLocation":807},1777302625503]