[{"data":1,"prerenderedAt":813},["ShallowReactive",2],{"/en-us/blog/gitlab-security-twenty-twenty-one":3,"navigation-en-us":34,"banner-en-us":444,"footer-en-us":454,"blog-post-authors-en-us-Johnathan Hunt":696,"blog-related-posts-en-us-gitlab-security-twenty-twenty-one":710,"blog-promotions-en-us":751,"next-steps-en-us":803},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":23,"isFeatured":12,"meta":24,"navigation":25,"path":26,"publishedDate":20,"seo":27,"stem":31,"tagSlugs":32,"__hash__":33},"blogPosts/en-us/blog/gitlab-security-twenty-twenty-one.yml","Gitlab Security Twenty Twenty One",[7],"johnathan-hunt",null,"security",{"slug":11,"featured":12,"template":13},"gitlab-security-twenty-twenty-one",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"GitLab Security in 2021: protect, enhance, certify and strengthen","Join our Security team as we review how we worked to keep GitLab, and our community, secure this past year.",[18],"Johnathan Hunt","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749670795/Blog/Hero%20Images/security-year-in-review-2021.png","2021-12-17","\n\n2021 has turned out to be another … interesting year, especially for those of us in the security industry.  Like so many software companies in the business, much of our recent focus has shifted to collective, cross-organizational research efforts to identify, mitigate and help resolve the threat posed by the Log4j vulnerability (See [our response](/blog/updates-and-actions-to-address-logj-in-gitlab/), as well as our post where we detail [how to use GitLab to detect Log4j vulnerabilities](/blog/use-gitlab-to-detect-vulnerabilities/)).\n\nThankfully though, 2021 was also focused on growing the Security department and adding additional teams and roles, bolstering enterprise SaaS security, reducing our threat landscape with improvements to supply chain security and APT threat protection, and fulfilling our mission of working to enable GitLab to succeed in the most secure way possible (see our [vision and mission statements](https://handbook.gitlab.com/handbook/security/#-security-vision-and-mission)). We achieved impressive results through expansion of our security third-party certification and self-attestation portfolio, contribution of GitLab and customer impacting product security features and improved security across all teams and domains in our security program. Our security teams also focused on improving processes and programs that enable customers on their trust journey, educate and engage team members to contribute toward improving our security posture, and encourage and enable collaboration from our community to strengthen GitLab. These efforts have been successful due to the contributions of our talented and dedicated Security team members, as well as the groups and individuals we partner with each day; including our wider community. THANK YOU for making GitLab stronger!\n\n## Improving assurance for the GitLab community\n\nOur [Security Assurance sub-department](https://handbook.gitlab.com/handbook/security/security-assurance/) spent the last year working across our organization to pursue and complete certifications, test and strengthen governance, assess and manage risk, and provide overall support and enablement to GitLab teams and our customers through a number of initiatives.\n\n### Certification portfolio expansion\n\nOur Security Assurance team built on a [successful 2020](/blog/how-we-made-gitlab-more-secure-in-twenty-twenty/) by focusing on our ambitious pursuit of [compliance certifications](https://handbook.gitlab.com/handbook/security/security-assurance/security-compliance/certifications/) with the issuance of GitLab’s first [SOC 2 Type 2/SOC 3 reports](https://us.aicpa.org/interestareas/frc/assuranceadvisoryservices/serviceorganization-smanagement) for the Security [Trust Service Criteria (TSC)](https://us.aicpa.org/interestareas/frc/assuranceadvisoryservices/trustdataintegritytaskforce) dated December 2020. Then, to support customers who need reports by the end of the calendar year, we adjusted our 2021 SOC reporting period to end on October 31st. For our most recent SOC reports we also added the [Confidentiality TSC](/blog/how-gitlab-successfully-expanded-our-soc-2-type-ii-trust-services-report-criteria/) to further highlight the maturity of our operating environment.\n\nIn addition, we delivered our very first [ISO/IEC 27001:2013 certification](https://www.globenewswire.com/en/news-release/2021/12/15/2352975/0/en/A-Compliance-Win-GitLab-Inc-Successfully-Achieves-ISO-IEC-27001-2013-Certification.html) in 2021. Certification against this highly-regarded baseline security standard recognizes our proven commitment to the highest level of information security management.\n\nLastly, in alignment with our continued commitment to transparency we publish all of our security certifications and attestation as part of GitLab’s [Customer Assurance Package](https://trust.gitlab.com/) (learn more below).\n\n### True, continuous control monitoring\n\nOur [Security Compliance team](https://handbook.gitlab.com/handbook/security/security-assurance/security-compliance/) upgraded our [GitLab Control Framework (GCF)](https://handbook.gitlab.com/handbook/security/security-assurance/security-compliance/sec-controls.html) in 2021 by adopting the Secure Control Framework (SCF) and moving into a [new GRC tool: ZenGRC](https://handbook.gitlab.com/handbook/business-technology/tech-stack/#zengrc). This upgraded control framework has increased testing efficiency and allowed GitLab to achieve our external compliance and regulatory obligations with minimized impact to our teams. This, along with our system/profile-based approach to testing, enabled us to achieve [successful external audits](https://handbook.gitlab.com/handbook/security/security-assurance/security-compliance/certifications/) and the implementation of strong [IT general controls (ITGCs) for SOX](https://handbook.gitlab.com/handbook/business-technology/enterprise-applications/it-compliance/) with a small [team of highly-skilled compliance engineers](/company/team/?department=security-assurance).\n\nWe believe our approach to control monitoring has a natural bias towards automation which allows our program to scale, along with GitLab. We’ve continued automating our compliance and regulatory workflows and, where possible, testing evidence as we work towards true continuous control monitoring with proactive alerting of control risks.\n\n### Next generation customer assurance services\n\nOur [Field Security team](https://handbook.gitlab.com/handbook/security/security-assurance/field-security/) deployed [GitLab’s Trust Center](/security/) and next generation Customer Assurance Package to further support our customers on their GitLab trust journey. As part of this effort we expanded our Customer Assurance Package to include the [Standard Information Gathering (SIG) Lite](https://sharedassessments.org/sig/) pre-completed questionnaire, completed an [ISO 20243 Self-Assessment](https://certification.opengroup.org/register/ottps-certification) for both our SaaS and Self-Managed service offerings, and became a [CSA STAR Trusted Cloud Service Provider](https://cloudsecurityalliance.org/star/registry/gitlab/). To support this program internally we dogfooded GitLab’s [Service Desk module](https://docs.gitlab.com/user/project/service_desk/) to deliver a more efficient way of monitoring, completing and responding to customer assurance requests.\n\nFor this group, 2022 will bring a heavy focus on tooling and automation in support of continued control monitoring, certification expansion and regulated market specialization.\n\n**Note:** Shout out to [@mmaneval20](/company/team/#mmaneval20), [@jburrows001](/company/team/#jburrows001), [@tdilbeck](/company/team/#tdilbeck) and [@julia.lake](/company/team/#Julia.Lake) who provided content for this section!\n\n\n## Shoring up our defenses\n\nOur team of [“breakers, builders, and defenders”](https://handbook.gitlab.com/handbook/security/security-operations/#mission) in our [Security Operations sub-department](https://handbook.gitlab.com/handbook/security/#protect-the-company---the-security-operations-sub-department) were *quite* busy this year identifying, preventing, detecting and responding to risks and security events targeting GitLab, our users and the business.\n\n### Identify, analyze and minimize the threat\n\nTo enhance visibility and increase protection of our ever-growing laptop fleet, our [Security Incident Response Team(SIRT)](https://handbook.gitlab.com/handbook/security/security-operations/sirt/) completed early testing of multiple endpoint detection and response platforms this year. After our IT Ops team successfully deployed our solution, our SIRT team took over support for the tool and owns the endpoint incident response lifecycle. Alerts from the platform have helped to identify possible issues and allow us to respond quickly to keep GitLab secure. Future plans currently include proactive threat hunting and creating advanced detection mechanisms based on available data points.\n\n### Security automation to address that ever-increasing threat landscape\n\nTo ensure our team’s ongoing incident response efforts are effective against the expanded attack surface and threat landscape that comes with our continued growth and expansion, we’re onboarding incident response automation. This solution has enabled us to automate the handling of reported phishing emails, user attestation on GCP documents access, and the assignment of appropriate response priority level via an incident severity calculator. These enhancements allow our engineers to focus on incident response and devising solutions to more complex issues and incidents.\n\n### Strengthening GitLab’s security in the shadows\n\nAs for our [Red Team](https://handbook.gitlab.com/handbook/security/security-operations/red-team/), they continued toeing the line of that ever-present balancing act between their stealth, exploratory testing and their commitment to GitLab’s value of transparency; all while helping GitLab implement effective cyber defenses. They held an external-facing [AMA this year in which they answered many questions from our community](/blog/you-asked-and-our-red-team-answered/) and [shared tips on how developers can secure themselves against RCE drive-by attacks](/blog/why-are-developers-vulnerable-to-driveby-attacks/); including details on a real-life disclosure on the GitLab GDK and shared our expertise surrounding offensive and defensive perspectives of attacks hiding malicious code in #OSS contributions at BlackHat Europe with [\"Picking Lockfiles: Attacking & Defending Your Supply Chain\"](https://www.blackhat.com/eu-21/briefings/schedule/#picking-lockfiles--attacking--defending-your-supply-chain-24844). And, much more … which we can’t talk about 😉 😎 .\n\n**Note:** Shout out to [@hasharma](/company/team/#hasharma), [@mjozenazemian](/company/team/#mjozenazemian), [@smanzuik](/company/team/#smanzuik), [@vmairet ](/company/team/#vmairet) and [@blutz1 ](/company/team/#blutz1) who provided content for this section!\n\n\n## Strengthening and securing GitLab the product\n\nOur [Security Engineering sub-department](https://handbook.gitlab.com/handbook/security/security-engineering/) endeavors to ensure all aspects of GitLab that are exposed to customers or that host customer data are held to the highest security standards, and to be proactive and responsive to ensure the security of anything GitLab offers. Throughout the year, this group collaborates with teams across the organization, and beyond with the GitLab community, to support our business and their bid to ensure that all GitLab products securely manage customer data.\n\n### Enhance the product with new tooling: Spamcheck and Package Hunter\n\nLast year we blogged about [how we work to detect and mitigate spam on GitLab.com](/blog/how-we-work-to-detect-and-mitigate-spam/). This year our [Security Automation team](https://handbook.gitlab.com/handbook/security/security-engineering/automation/) worked closely with the GitLab Trust and Safety team to [introduce Spamcheck](/blog/introducing-spamcheck-data-driven-anti-abuse/), our new anti-spam engine that has been enabled for all projects on GitLab.com and we're targeting inclusion of Spamcheck in the [14.6 release for our GitLab self-managed customers](https://gitlab.com/gitlab-org/omnibus-gitlab/-/issues/6259). By allowing us to better detect and prevent spam, we believe Spamcheck has significantly improved GitLab’s resilience to it. We recently blogged about the [technical decisions behind Spamcheck](/blog/deep-dive-tech-stack-behind-spamcheck/), as well as some of the early performance data points. You can also check out the [code behind Spamcheck](https://gitlab.com/gitlab-org/spamcheck).\n\nIn July 2021, the GitLab [Security Research team](https://handbook.gitlab.com/handbook/security/security-engineering/security-research/) [released Package Hunter](/blog/announcing-package-hunter/), a tool that helps identify malicious dependencies via runtime monitoring. Powered by [Falco](https://falco.org/), Package Hunter installs a program’s dependencies in a sandbox environment and analyzes system calls for malicious code and other unexpected behavior. Testing of NodeJS and Ruby Gems is currently supported. The project is [open source](https://gitlab.com/gitlab-org/security-products/package-hunter) and we are continually working to improve upon it. Community contributions and feedback are very much welcome!\n\n### Risk reduction and vulnerability management\n\nScaling our [Application Security](https://handbook.gitlab.com/handbook/security/security-engineering/application-security/) efforts has been a big priority for our teams. Again, the key to [doing so successfully is thru automation](https://handbook.gitlab.com/handbook/security/security-engineering/application-security/inventory/), particularly when it comes to keeping track of a growing list of codebases that are constantly changing, adding new components, and relying on different dependencies. For this reason we’re very excited about the progress that has been made on the [GitLab Inventory Builder](https://gitlab.com/gitlab-com/gl-security/engineering-and-research/gib/), a very handy tool capable of generating and maintaining a complete list of projects and their dependencies hosted on GitLab.com or self-hosted instances. This is also our first iteration of using policy-as-code to monitor and control various aspects of our projects. Not only can we track where security scans are not well configured, but we believe we can also spot project configuration issues precisely. With the automatic creation of violation issues in GitLab, we can organize, track, and scale the work of our Security Engineers more efficiently. Take a look at this [live action demo](https://gitlab-com.gitlab.io/gl-security/engineering-and-research/inventory-example/) and view the [example code supporting it](https://gitlab.com/gitlab-com/gl-security/engineering-and-research/inventory-example) for more information!\n\nDuring 2021 we bootstrapped our [Infrastructure Security team](https://handbook.gitlab.com/handbook/security/security-engineering/infrastructure-security/) and program. This new team works as a stable counterpart to the Infrastructure team and collaborates across Infrastructure and Security to help identify and mitigate security issues, vulnerabilities, and misconfigurations by applying their in-depth knowledge of operating systems, infrastructure, and cloud providers. With this new team and program we’ve bolstered our security observability, added an operating system instrumentation platform, enhanced monitoring, and created an analytics framework for our hosts; all of which help give us insight into all aspects of our production systems. We’ve also deployed an intuitive security graph tool across our cloud platforms that inventories all of our assets and shows the connections between them, but also enables querying based on various metadata. We believe these efforts have already resulted in significant security risk reduction, enhanced vulnerability management, increased observability, and granular monitoring capabilities.\n\nTo help team members understand the security implications of the systems and features they design and work on, this year our team formalized and integrated a [threat modeling process](https://handbook.gitlab.com/handbook/security/threat_modeling/) here at GitLab. Building upon the [evidence driven threat modeling approach that we started working towards adopting last year](https://handbook.gitlab.com/handbook/security/threat_modeling/#the-framework), we’ve iterated on the threat modeling processes and tooling in order to increase adoption, usage, and understanding across GitLab teams. We’ve also added [issue templates](https://docs.gitlab.com/user/project/description_templates/#create-an-issue-template) to our internal threat modeling repository and improved upon our [threat modeling runbook](https://handbook.gitlab.com/handbook/security/security-engineering/application-security/runbooks/threat-modeling.html). We talk about some of the basics of threat modeling and [how we’ve developed a framework that will work for GitLab in our blog](/blog/creating-a-threat-model-that-works-for-gitlab/).\n\n### Strengthening our product through global expertise and contributions\nThis past year we received 752 reports from 404 talented bug bounty reporters from all across the globe who helped us to strengthen our product through the identification of security vulnerabilities.\n\nIn February, we moved to a managed bug bounty program with [HackerOne](https://hackerone.com/gitlab). This enables us to scale our report triage process, filter out the noise, and ultimately present the most important reports to our development teams faster. In November, we kicked off, [“Our 3rd annual bug bounty contest: the swagtastic sequel to the sequel“](/blog/3rd-annual-bug-bounty-contest/), announced a near double in [bounty rewards and detailed our move to standardize bounty payments by using CVSS along with a [nifty CVSS calculator](https://gitlab-com.gitlab.io/gl-security/appsec/cvss-calculator/#). This program, and the amazing bug bounty hunters who contribute to it, continue to raise GitLab’s security bar and reduce risk for our customers. You can read more about what happened in our bug bounty program this past year in [“2021: Smashing bugs and dropping names”](https://about.gitlab.com/blog/smashing-bugs-and-dropping-names-in-2021/).\n\n**Note:** Shout out to [@ankelly](/company/team/#ankelly), [@jritchey](/company/team/#jritchey), [@plafoucriere](/company/team/#plafoucriere), [@heather ](/company/team/#heather) and [@laurence.bierner](/company/team/#laurence.bierner) who provided content for this section!\n\n\n## Everyone can contribute…to Security\n\nWhen we say that Security is a team effort, we mean it.  These three sub departments, and the 12 teams that sit within them work collaboratively (and sometimes tirelessly) with dozens of teams across GitLab, and community members, to keep GitLab secure and protect our company, the community and our customers.   Thank you to everyone who contributes here and best wishes for a safe, healthy and happy 2022! 🥂\n\n",[9],"yml",{},true,"/en-us/blog/gitlab-security-twenty-twenty-one",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":28,"ogSiteName":29,"ogType":30,"canonicalUrls":28},"https://about.gitlab.com/blog/gitlab-security-twenty-twenty-one","https://about.gitlab.com","article","en-us/blog/gitlab-security-twenty-twenty-one",[9],"OjK-mh8FQKlnqi2QpSGk_PQm8al7lKUFzo6cj8U0maM",{"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 trial",{"href":44,"dataGaName":45,"dataGaLocation":40},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free trial",{"text":47,"config":48},"Talk to 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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":25,"template":13,"slug":723},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":725,"config":737},{"title":726,"description":727,"authors":728,"heroImage":730,"date":731,"category":9,"tags":732,"body":736},"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",[9,733,561,734,735],"tutorial","features","product","Security scanners are essential, but not every finding requires action. Test code, vendored dependencies, generated files, and known false positives create noise that buries the vulnerabilities that actually matter. Security teams waste hours manually dismissing the same irrelevant findings across projects and pipelines. They experience slower triage, alert fatigue, and developer friction that undermines adoption of security scanning itself.\n\nGitLab's auto-dismiss vulnerability policies let you codify your triage decisions once and apply them automatically on every default-branch pipeline. Define criteria based on file path, directory, or vulnerability identifier (CVE, CWE), choose a dismissal reason, and let GitLab handle the rest.\n\n## Why auto-dismiss?\nAuto-dismiss vulnerability policies enable security teams to:\n- **Eliminate triage noise**: Automatically dismiss findings in test code, vendored dependencies, and generated files.\n- **Enforce decisions at scale**: Apply policies centrally to dismiss known false positives across your entire organization.\n- **Maintain audit transparency**: Every auto-dismissed finding includes a documented reason and links back to the policy that triggered it.\n- **Preserve the record**: Unlike scanner exclusions, dismissed vulnerabilities remain in your report, so you can revisit decisions if conditions change.\n\n## How auto-dismiss policies work\n\n1. **Define your policy** in a vulnerability management policy YAML file. Specify match criteria (file path, directory, or identifier) and a dismissal reason.\n\n2. **Merge and activate.** Create the policy via **Secure > Policies > New  policy > Vulnerability management policy**. Merge the MR to enable it.\n3. **Run your pipeline.** On every default-branch pipeline, matching vulnerabilities are automatically set to \"Dismissed\" with the specified reason. Up to 1,000 vulnerabilities are processed per run.\n4. **Measure the impact.** Filter your vulnerability report by status \"Dismissed\" to see exactly what was cleaned up and validate that the right findings are being handled.\n\n## Use cases with ready-to-use configurations\n\nEach example below includes a policy configuration you can copy, customize, and apply immediately.\n\n### 1. Dismiss test code vulnerabilities\n\nSAST and dependency scanners flag hardcoded credentials, insecure fixtures, and dev-only dependencies in test directories. These are not production risks.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss test code vulnerabilities\"\n    description: \"Auto-dismiss findings in test directories\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"test/**/*\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"tests/**/*\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"spec/**/*\"\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"__tests__/*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: used_in_tests\n\n```\n\n### 2. Dismiss vendored and third-party code\n\nVulnerabilities in `vendor/`, `third_party/`, or checked-in `node_modules` are managed upstream and not actionable for your team.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss vendored dependency findings\"\n    description: \"Findings in vendored code are managed upstream\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"vendor/*\"\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"third_party/*\"\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"vendored/*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: not_applicable\n\n```\n\n### 3. Dismiss known false positive CVEs\n\nCertain CVEs are repeatedly flagged but don't apply to your usage context. Teams dismiss these manually every time they appear. Replace the example CVEs below with your own.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss known false positive CVEs\"\n    description: \"CVEs confirmed as false positives for our environment\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2023-44487\"\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2024-29041\"\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2023-26136\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: false_positive\n\n```\n\n### 4. Dismiss generated and auto-created code\n\nProtobuf, gRPC, OpenAPI generators, and ORM scaffolding tools produce files with flagged patterns that cannot be patched by your team.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss generated code findings\"\n    description: \"Generated files are not authored by us\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"generated/*\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"**/*.pb.go\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"**/*.generated.*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: not_applicable\n\n```\n\n### 5. Dismiss infrastructure-mitigated vulnerabilities\n\nVulnerability classes like XSS (CWE-79) or SQL injection (CWE-89) that are already addressed by WAF rules or runtime protection. Only use this when mitigating controls are verified and consistently enforced.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss CWEs mitigated by WAF\"\n    description: \"XSS and SQLi mitigated by WAF rules\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CWE-79\"\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CWE-89\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: mitigating_control\n\n```\n\n### 6. Dismiss CVE families across your organization\n\nA wave of related CVEs for a widely-used library your team has assessed? Apply at the group level to dismiss them across dozens of projects. The wildcard pattern (e.g., `CVE-2021-44*`) matches all CVEs with that prefix.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Accept risk for log4j CVE family\"\n    description: \"Log4j CVEs mitigated by version pinning and WAF\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2021-44*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: acceptable_risk\n\n```\n\n## Quick reference\n\n| Parameter | Details |\n|-----------|---------|\n| **Criteria types** | `file_path` (glob patterns, e.g., `test/**/*`), `directory` (e.g., `vendor/*`), `identifier` (CVE/CWE with wildcards, e.g., `CVE-2023-*`) |\n| **Dismissal reasons** | `acceptable_risk`, `false_positive`, `mitigating_control`, `used_in_tests`, `not_applicable` |\n| **Criteria logic** | Multiple criteria within a rule = AND (must match all). Multiple rules within a policy = OR (match any). |\n| **Limits** | 3 criteria per rule, 5 rules per policy, 5 policies per security policy project. Vulnerabilty management policy actions process 1000 vulnerabilities per pipeline run in the target project, until all matching vulnerabilities are processed. |\n| **Affected statuses** | Needs triage, Confirmed |\n| **Scope** | Project-level or group-level (group-level applies across all projects) |\n\n## Getting started\nHere's how to get started with auto-dismiss policies:\n\n1. **Identify the noise.** Open your vulnerability report and sort by \"Needs triage.\" Look for patterns: test files, vendored code, the same CVE across projects.\n\n2. **Pick a scenario.** Start with whichever use case above accounts for the most findings.\n\n3. **Record your baseline.** Note the number of \"Needs triage\" vulnerabilities before creating a policy.\n\n4. **Create and enable.** Navigate to **Secure > Policies > New policy > Vulnerability management policy**. Paste the configuration from the use case above, then merge the MR.\n\n5. **Validate results.** After the next default-branch pipeline, filter by status \"Dismissed\" to confirm the right findings were handled.\n\nFor full configuration details, see the [vulnerability management policy documentation](https://docs.gitlab.com/user/application_security/policies/vulnerability_management_policy/#auto-dismiss-policies).\n\n> Ready to take control of vulnerability noise? [Start a free GitLab Ultimate trial](https://about.gitlab.com/free-trial/) and configure your first auto-dismiss policy today.\n",{"slug":738,"featured":25,"template":13},"auto-dismiss-vulnerability-management-policy",{"content":740,"config":749},{"title":741,"description":742,"authors":743,"heroImage":745,"date":746,"body":747,"category":9,"tags":748},"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.",[744],"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_).",[735,9,734],{"featured":12,"template":13,"slug":750},"gitlab-18-10-brings-ai-native-triage-and-remediation",{"promotions":752},[753,767,778,789],{"id":754,"categories":755,"header":757,"text":758,"button":759,"image":764},"ai-modernization",[756],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":760,"config":761},"Get your AI maturity score",{"href":762,"dataGaName":763,"dataGaLocation":238},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":765},{"src":766},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":768,"categories":769,"header":770,"text":758,"button":771,"image":775},"devops-modernization",[735,564],"Are you just managing tools or shipping innovation?",{"text":772,"config":773},"Get your DevOps maturity score",{"href":774,"dataGaName":763,"dataGaLocation":238},"/assessments/devops-modernization-assessment/",{"config":776},{"src":777},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":779,"categories":780,"header":781,"text":758,"button":782,"image":786},"security-modernization",[9],"Are you trading speed for security?",{"text":783,"config":784},"Get your security maturity score",{"href":785,"dataGaName":763,"dataGaLocation":238},"/assessments/security-modernization-assessment/",{"config":787},{"src":788},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":790,"paths":791,"header":794,"text":795,"button":796,"image":801},"github-azure-migration",[792,793],"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":797,"config":798},"See how GitLab compares to GitHub",{"href":799,"dataGaName":800,"dataGaLocation":238},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":802},{"src":777},{"header":804,"blurb":805,"button":806,"secondaryButton":811},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":807,"config":808},"Get your free trial",{"href":809,"dataGaName":45,"dataGaLocation":810},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":500,"config":812},{"href":49,"dataGaName":50,"dataGaLocation":810},1777313720835]