[{"data":1,"prerenderedAt":814},["ShallowReactive",2],{"/en-us/blog/gitlab-security-hardening-documentation":3,"navigation-en-us":38,"banner-en-us":448,"footer-en-us":458,"blog-post-authors-en-us-Mark Loveless":697,"blog-related-posts-en-us-gitlab-security-hardening-documentation":711,"blog-promotions-en-us":752,"next-steps-en-us":804},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":12,"meta":26,"navigation":27,"path":28,"publishedDate":20,"seo":29,"stem":33,"tagSlugs":34,"__hash__":37},"blogPosts/en-us/blog/gitlab-security-hardening-documentation.yml","Gitlab Security Hardening Documentation",[7],"mark-loveless",null,"security",{"slug":11,"featured":12,"template":13},"gitlab-security-hardening-documentation",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"The backstory on GitLab's security hardening documentation","GitLab has detailed documentation about how to harden your instance, now as a part of GitLab itself. Here's how it came to be.",[18],"Mark Loveless","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749665667/Blog/Hero%20Images/built-in-security.jpg","2023-08-01","\nRecently, my fellow Security team member [Ayoub Fandi](https://gitlab.com/ayofan) released an excellent blog post entitled “[How to harden your self-managed GitLab instance](https://about.gitlab.com/blog/how-to-harden-your-self-managed-gitlab-instance/),” which featured seven steps for helping to lock down your environment. Ayoub’s blog post was based, in part, on early drafts of research into hardening I had been working on. I had no idea how long it would take me to reach a point where I could finally say this, but those original drafts are now a [part of GitLab documentation](https://docs.gitlab.com/ee/security/hardening.html).\n\n## Hardening your GitLab instance\nThe goal of the hardening documentation is for DevSecOps teams to be able to apply security controls and make sure your data and your GitLab instance are protected.\n\nHere's what I will cover:\n- the origins of the hardening documentation\n- field research, including a few attack scenarios\n- bringing an instance online\n- insight into when to use hardening, specifically how to do a bit of threat modeling and get the basics sorted before diving deep into full hardening\n\n## Origins of the hardening documentation\nThe idea for creating extensive hardening documentation started with a blog post I wrote three years ago called “[GitLab instance: Security best practices](https://about.gitlab.com/blog/gitlab-instance-security-best-practices/).\" This became a rather popular reference for GitLab customers asking about securing their self-managed instance (and even a SaaS deployment).\n\nAs I participated in other security efforts around Gitlab, such as [FedRAMP certification](https://handbook.gitlab.com/handbook/security/security-assurance/security-compliance/fedramp-compliance/), [compliance requirements](https://handbook.gitlab.com/handbook/security/security-assurance/security-compliance/certifications/), and general [security concerns](https://handbook.gitlab.com/handbook/security/), I realized we needed more support materials focused on the \"Sec\" in DevSecOps.\n\nThe hardening project was born from this -  based largely off of an instance I had loaded up in 2020 and was using at home. During all of this time, from 2020 until now, I took notes, performed tests, had hacker friends and work colleagues poke and prod at this home system, and, then took even more notes. No doubt, the information I learned would be a huge benefit to GitLab users so I opted to create recommendations that could be updated frequently and accessible directly from a GitLab instance via the Help menu.\n\n## Field research\nI've been a security professional for well over a couple of decades and have had my own domain online since 1997, using five static IP addresses in my house. This deployment includes web services, a Mastodon instance, and a mail server. These systems have been used by a few close hacker friends during this time as a testbed, a place to converse and exchange ideas, and a great educational environment. A few years ago, I thought it would be fun for this group to have its own private GitLab instance called [Blackhole](https://blackhole.nmrc.org/). We could work on coding projects together, collaborate, and, since I worked at GitLab, I could use it for testing of a standalone instance for certain work-inspired issues.\n\nHaving live servers up with dedicated and static IP addresses means that, yes, these servers are under pretty much constant attack. Because of this clustering of security friends on these systems, we’ve even been targeted by more sophisticated attackers, up to and including nation states. A perfect test environment for real-world attack scenarios.\n\n### Attack scenarios\nLike many in the security field, over the years I’ve seen a number of attacks firsthand, so I made a list of things I needed for hardening against attacks. After doing some [threat modeling](https://handbook.gitlab.com/handbook/security/product-security/application-security/threat-modeling/), I noted the following three areas of concern:\n1. **Opportunistic intruders (okay, who am I kidding, [script kiddies](https://www.techtarget.com/searchsecurity/definition/script-kiddy-or-script-kiddie)).** These types of attacks are typically composed of easy-to-use scripts against known vulnerable applications. This has happened to me. Within five minutes of installation of an odd web-based application, while still reading the documentation and wondering if the apparently vulnerable software was actually working, it was compromised. So I had to assume immediate attacks would happen if I loaded this up on a live system exposed to the open internet.\n2. **Layered or chained attacks.** This is when an attacker takes advantage of a particular sub-component that is exposed to the internet, and while the sub-component might not allow for full system access, it could allow for access to another sub-component with access to data. I was determined to disable or secure as much as possible, leaving as few exposed ports or running services as possible.\n3. **Advanced persistent threat ([APT](https://en.wikipedia.org/wiki/Advanced_persistent_threat)) attackers.** They have repeatedly gone after my former employers, and as they learned who their employees were, home systems would become targets, and [mine were no exception](https://www.markloveless.net/blog/2021/6/8/advancing-persistently-against-apt). To this day, I receive an APT attack attempt every few months. Knowing that one tactic of APT attackers is supply chain attacks, having a DevSecOps platform shared by hackers could be seen as a delicious target, so security had to be top of mind.\n\n## Bringing an instance online\nAs I installed Blackhole, I first configured the firewall at the operating system level to close off all of the ports from public access, only allowing access from my internal network. As a rule, my perimeter router allows all traffic in for those five static IP addresses, with each system following strict firewall rules using the operating system’s firewall capabilities. Most of these five systems cannot even talk to each other, or only have the bare minimum connectivity configured to enable functionality. As I was monitoring traffic to my public systems via my perimeter router, in less than 10 minutes, I started seeing port scans against Blackhole’s IP address, well before it had even finished installation of the Linux operating system. I was glad the firewall was up and running from the start.\n\nAs GitLab was installed and Blackhole came up in its state as a GitLab instance, I started going through the various settings and making sure that things were locked down as tight as possible. Anything I wasn’t using was disabled. This applied to the underlying operating system as well as the GitLab software itself. When I felt good enough about it, I adjusted the firewall settings to open things up ever so slightly, and the system has been under near constant attack since.\n\n## When to use the hardening documentation\nGitLab is a comprehensive DevSecOps platform that can handle all kinds of security scenarios. GitLab the company uses the product to not only develop the platform, but we also run the company off of it. The feature-rich platform can be configured in many different ways. Keeping that in mind, note that one setup might be set up to be more secure than another simply because of the environment it needs to be included in. There are drastically different configuration choices for an environment that is publicly accessible vs. one that is only accessible from employee workstations, or  a large enterprise with employees located on multiple continents vs. a small business' single server deployment.\n\nHardening, therefore, is dependent on your unique environment, and requires you to understand the threats you need to mitigate against, and account for any regulatory and compliance requirements to which you must adhere. However, there are a few common steps that can lead you through the process.\n\n### Start with the basics\nThe first recommendation is to start with a few basics. Make sure you have some ground rules established in your organization such as password standards, software upgrade schedules, and compliance requirements. This will make it easier as you move through the process. Understand the threats your organization has faced in the past, and the potential threats you could face in the future. I wrote a blog post on [threat modeling](https://handbook.gitlab.com/handbook/security/product-security/application-security/threat-modeling/) and [we use it internally](https://handbook.gitlab.com/handbook/security/product-security/application-security/threat-modeling/howto.html) as well.\n\n### Full hardening\nI’d recommend reading [Ayoub’s blog post](https://about.gitlab.com/blog/how-to-harden-your-self-managed-gitlab-instance/) and follow the seven steps he puts forward. In many cases, after you’ve finished Ayoub’s blog post you will have enough to meet your security needs right there. If you need more, delve into the [hardening recommendations documentation](https://docs.gitlab.com/ee/security/hardening.html). Adapt it as needed to meet your organization’s security demands, and explore the possibilities to increase the security of your environment. Note that these recommendations are not limited to just GitLab settings, but also includes a few recommendations for the underlying operating system itself.\n\n### Share your feedback\nIf you have ideas for more security tips and tricks or questions regarding the hardening documentation, please open an issue on GitLab. We’d love to hear from you and welcome feedback and contributions! And if you want to learn more about how we do security at GitLab, review the [security section](https://handbook.gitlab.com/handbook/security/) of the handbook.\n",[9,23,24],"cloud native","DevSecOps","yml",{},true,"/en-us/blog/gitlab-security-hardening-documentation",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":30,"ogSiteName":31,"ogType":32,"canonicalUrls":30},"https://about.gitlab.com/blog/gitlab-security-hardening-documentation","https://about.gitlab.com","article","en-us/blog/gitlab-security-hardening-documentation",[9,35,36],"cloud-native","devsecops","QQ01A071SLHGF3biZ2PJ2uytfCE1G6FdgmwKRzpB2Ew",{"data":39},{"logo":40,"freeTrial":45,"sales":50,"login":55,"items":60,"search":368,"minimal":399,"duo":418,"switchNav":427,"pricingDeployment":438},{"config":41},{"href":42,"dataGaName":43,"dataGaLocation":44},"/","gitlab logo","header",{"text":46,"config":47},"Get free 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statement",{"items":687},[688,691,694],{"text":689,"config":690},"Terms",{"href":518,"dataGaName":519,"dataGaLocation":466},{"text":692,"config":693},"Cookies",{"dataGaName":528,"dataGaLocation":466,"id":529,"isOneTrustButton":27},{"text":695,"config":696},"Privacy",{"href":523,"dataGaName":524,"dataGaLocation":466},[698],{"id":699,"title":18,"body":8,"config":700,"content":702,"description":8,"extension":25,"meta":706,"navigation":27,"path":707,"seo":708,"stem":709,"__hash__":710},"blogAuthors/en-us/blog/authors/mark-loveless.yml",{"template":701},"BlogAuthor",{"name":18,"config":703},{"headshot":704,"ctfId":705},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664093/Blog/Author%20Headshots/mloveless-headshot.png","mloveless",{},"/en-us/blog/authors/mark-loveless",{},"en-us/blog/authors/mark-loveless","gYgzUdwKCnypExwE5MZ2RPPrl97ftdqEfuAoem8PmXM",[712,725,740],{"content":713,"config":723},{"title":714,"description":715,"authors":716,"date":718,"body":719,"category":9,"tags":720,"heroImage":722},"Prepare 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.",[717],"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.",[721,9,533],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":27,"template":13,"slug":724},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":726,"config":738},{"title":727,"description":728,"authors":729,"heroImage":731,"date":732,"category":9,"tags":733,"body":737},"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.",[730],"Grant Hickman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1774375772/kpaaaiqhokevxxeoxvu0.png","2026-03-25",[9,734,24,735,736],"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":739,"featured":27,"template":13},"auto-dismiss-vulnerability-management-policy",{"content":741,"config":750},{"title":742,"description":743,"authors":744,"heroImage":746,"date":747,"body":748,"category":9,"tags":749},"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.",[745],"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. 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