[{"data":1,"prerenderedAt":813},["ShallowReactive",2],{"/en-us/blog/threat-modeling-kubernetes-agent":3,"navigation-en-us":34,"banner-en-us":444,"footer-en-us":454,"blog-post-authors-en-us-Vitor Meireles De Sousa":696,"blog-related-posts-en-us-threat-modeling-kubernetes-agent":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/threat-modeling-kubernetes-agent.yml","Threat Modeling Kubernetes Agent",[7],"vitor-meireles-de-sousa",null,"security",{"slug":11,"featured":12,"template":13},"threat-modeling-kubernetes-agent",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Threat modeling the Kubernetes Agent: from MVC to continuous improvement","Learn how we put our threat model into action iteratively and expanded the\nprocess into a full-fledged standalone activity.",[18],"Vitor Meireles De Sousa","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682156/Blog/Hero%20Images/pexels-jesus-miron-garcia-3043592.jpg","2021-10-11","Threat modeling is more common in people’s everyday lives than they might think.  Each of us performs some level of threat modeling every time we’re in a situation where we’re evaluating threats. An everyday example I really like: crossing the street. Before we cross the street we look both ways, we evaluate the speed of each oncoming vehicle and verify the driver has seen us. Finally, if the lights are green, we cross the street. This is threat modeling!\n\n_If you’re interested in learning more about what threat modeling is and how we’re developing our threat model here at GitLab, you can read about [“How we’re creating a threat model framework that works for\nGitLab“](/blog/creating-a-threat-model-that-works-for-gitlab/) by my teammate [Mark Loveless](/company/team/#mloveless)._\n\n## Threat modeling IRL\n\nIn this blog post I’ll talk about how we put our threat modeling process into action iteratively with an in-depth look into how we developed the process from a security assessment with a side of threat modeling to a full-fledged standalone activity.\n\n### Our threat modeling MVC\n\nWe rolled out the initial iteration of the GitLab threat model in November of 2020. One of the first projects we assessed through that new process was\nGitLab’s [Kubernetes Agent](https://docs.gitlab.com/ee/user/clusters/agent/)\nbeta. At that time, my colleague [Joern\nSchneeweisz](/company/team/#joernchen) performed an initial threat model, which was actually more of a security assessment in which threat modeling activities were incorporated. In this [data flow diagram](https://handbook.gitlab.com/handbook/security/product-security/application-security/threat-modeling/howto/#tools-and) from our initial threat model, you can see how the [architecture for the Kubernetes\nAgent looked in May 2020](https://gitlab.com/gitlab-org/cluster-integration/gitlab-agent/-/blob/f374356751aec8cb65b9dea4de3ba618805c2414/docs/architecture.md):\n\n```mermaid\n\ngraph TB\n  agentk -- gRPC bidirectional streaming --> kgb\n\n  subgraph \"GitLab\"\n  kgb[kgb]\n  GitLabRoR[GitLab RoR] -- gRPC --> kgb\n  kgb -- gRPC --> Gitaly[Gitaly]\n  kgb -- REST API --> GitLabRoR\n  end\n\n  subgraph \"Kubernetes cluster\"\n  agentk[agentk]\n  end\n```\n\nThis first review, despite our threat model being early stage, still allowed us to identify issues and findings (like the Agent exposing public projects with private repositories [(fixed with this merge request)](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/48314) or the Agent name being vulnerable to path traversal attacks [(fixed with this merge request)](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/37564)) that benefited our security and the broader engineering teams. Plus, the cross-organizational feedback we received during this iteration was key to improving our threat model integration and templates.\n\n### Increasing capabilities expand the threats\n\nFour months had passed since the initial assessment; it was time to revisit our previous findings and to review the expanded capabilities of the\nKubernetes Agent feature. The [architecture had also evolved](https://gitlab.com/gitlab-org/cluster-integration/gitlab-agent/-/blob/154f538c997b4064294a54695846092c348bada8/doc/architecture.md#high-level-architecture), and at a high level we can see the product is changing names (`kgb` changed to `kas`, for Kubernetes Agent Server (KAS)).\n\n```mermaid\n\ngraph TB\n  agentk -- gRPC bidirectional streaming --> nginx\n\n  subgraph \"GitLab\"\n  nginx -- proxy pass port 5005 --> kas\n  kas[kas]\n  GitLabRoR[GitLab RoR] -- gRPC --> kas\n  kas -- gRPC --> Gitaly[Gitaly]\n  kas -- REST API --> GitLabRoR\n  end\n\n  subgraph \"Kubernetes cluster\"\n  agentk[agentk]\n  end\n```\n\nBy this time the threat modeling template we were using had evolved, and our [Application Security team](https://handbook.gitlab.com/handbook/security/security-engineering/application-security/) had used it in several other reviews.\n\nGitLab’s practice of [dogfooding](https://handbook.gitlab.com/handbook/values/#dogfooding) means we use issues to track our reviews. At the time of this second review, our threat modeling template now had a more structured approach where reviewers had sections that would 1) guide them throughout the review 2) require them to provide specific information.\n\nBecause our process was more robust now we moved the threat modeling activity of our security assessments into a dedicated repository, giving us a single source of truth. [The template](https://gitlab.com/gitlab-com/gl-security/security-research/threat-modeling-template/-/blob/3486ca53baf13d4aaba28dd340df153b2b83ea05/threat_model.md)\nhad evolved to also include:\n\n* An “Application decomposition” section where the reviewer must enter\ndetails such as use case, external entry points, trust levels, data flow diagram, previous security issues and known references and best practices.\n\n* A dedicated threat analysis section.\n\n* The use of issue comments to detail each section of the threat modeling\nallowing us to easily reference sections individually via direct link.\n\n* The conversion and merge of the completed issue to an MD file, saved into\nthe dedicated repository for future use.\n\n### What is better than iteration? More iteration\n\nSince our second review in October 2020, the Kubernetes Agent feature had evolved significantly, so we performed another assessment in February 2021.\nThe difference this time was that we now had a [formal threat modeling process in place](https://handbook.gitlab.com/handbook/security/threat_modeling/).\n\n**To better understand the Kubernetes Agent feature, we added more details to our architectural diagram:**\n\n_Legend:_\n\n* Dotted arrows/flow: out of scope of the Architecture or TM\n\n* grpc: Google Remote Procedure Call\n\n* grpcs: grpc over SSL/TLS\n\n* ws: WebSocket\n\n* wss: ws over SSL/TLS\n\n![Detailed architectural diagram of the Kubernetes\nAgent](https://about.gitlab.com/images/blogimages/threat-modeling-KA/ka-architectural-diagram.png){:\n.shadow.medium.center}\n\nDetailed architectural diagram of the Kubernetes Agent.\n\n\n\n**The data flow diagram we were using also evolved:**\n\n_Legend:_\n\n* Dotted arrows/flow: out of scope of the architecture or threat model\n\n* grpc: Google remote procedure call\n\n* grpcs: grpc over SSL/TLS\n\n* ws: WebSocket\n\n* wss: ws over SSL/TLS\n\n![file name](https://about.gitlab.com/images/blogimages/threat-modeling-KA/ka-data-flow.png){:\n.shadow.medium.center}\n\nAn evolved data flow diagram for the Kubernetes Agent.\n\n\n\nAnd, naturally, as the features evolved, the threats evolved. Through our latest threat modeling we discovered that:\n\n* Listeners are used by the Agent and the KAS for observability and health\nchecking. These listeners are unrestricted but they do listen on localhost by default.\n\n* On GitLab’s side, developers would be able to deploy an application to\nanother cluster on another corporate network. This is limited by Kubernetes' own authorisations defined for each cluster.\n\n* While brainstorming for threats, we thought about whether a user would be\nable to access unauthorised projects through indirect access on Gitaly.\nThankfully, this is well mitigated since a few conditions are necessary:\n      * A user must have access to the Agent's pod\n      * A user must be able to modify and reply to requests from the Agent pod\n          * However, each Agent also needs to submit a secret token, otherwise the [request is denied](https://gitlab.com/gitlab-org/cluster-integration/gitlab-agent/-/blob/5b7ca0b9fbc8daba28ca552dc26aab45e482cf0c/internal/module/agent_configuration/server/server.go#L55) (using [GitLab's client](https://gitlab.com/gitlab-org/cluster-integration/gitlab-agent/-/blob/5b7ca0b9fbc8daba28ca552dc26aab45e482cf0c/internal/gitlab/client.go)) GetAgentInfo implementation. That implementation [generates and passes a JWT token](https://gitlab.com/gitlab-org/cluster-integration/gitlab-agent/-/blob/5b7ca0b9fbc8daba28ca552dc26aab45e482cf0c/internal/gitlab/client.go#L108-123), along with the [Agent token](https://gitlab.com/gitlab-org/cluster-integration/gitlab-agent/-/blob/5b7ca0b9fbc8daba28ca552dc26aab45e482cf0c/internal/gitlab/do_options.go#L103-109). The Agent must also be configured to consult only authorised repositories, which makes it impossible for a user to access other repositories from the Agent.\n* On local installations, it’s possible to use unencrypted communications\nbetween the Kubernetes Agent and the KAS. However, on gitlab.com we have enforced secure communications.\n\n## Continuous improvement and iteration\n\nThreat modeling, just like anything in security, isn’t something you do once and are done with it. As we’ve seen with the reviews we’ve performed on the\nKubernetes Agent, threat modeling involves iteration and constant adoption of ever-changing features and attack surfaces of a product.\n\nOur threat model is now mature enough that it's an established process, performed as a separate review and merged into a dedicated repository at completion. In the future, we hope to be able to publicly share those threat models to help customers, the community and to continue strengthening our [hackerone program](https://hackerone.com/gitlab).\n\nFor the next iterations we’re looking for broad adoption of threat modeling across GitLab's engineering teams and beyond. We plan to use our threat model as a way to improve  [asynchronous communication](https://handbook.gitlab.com/handbook/company/culture/all-remote/asynchronous/) between the different [security stable counterparts](https://handbook.gitlab.com/handbook/security/security-engineering/application-security/stable-counterparts.html)\nthat operate across the organization. These stable counterparts ensure security practices are integrated early on in the development process and also allow us to ensure better coverage across vacations and time zones.\n\n## About Kubernetes\n\nUntil then, if you’re interested in more threat modeling details specific to\nKubernetes itself, I highly recommend the [Kubernetes Security Audit Working\nGroup Kubernetes threat model](https://github.com/kubernetes/community/tree/d538271e3f5eed22429ded165aeb2557c6277967/wg-security-audit).\nOne of my fellow GitLab teammates, [Marco Lancini](/company/team/#mlancini)\nhas published a great post, [“The Current State of Kubernetes Threat\nModelling”](https://www.marcolancini.it/2020/blog-kubernetes-threat-modelling/)\non his personal blog which contains useful information on different methods used to perform a threat model for Kubernetes.\n\n## How to threat model\n\nAnd, if you’re interested in how we’re rolling out threat modeling across\nGitLab, to teams beyond Security and Engineering, we’ve been tweaking our [how to threat model](https://handbook.gitlab.com/handbook/security/product-security/application-security/threat-modeling/howto/) guide to help as many team members as possible understand what threat modeling is and how and where to get started. Perhaps you’ll find some helpful tips and tricks there.\n\nAnd, keep an eye on this space, we’re planning to revisit threat modeling in blog posts where we’ll dive deeper into the PASTA methodology we’re using and take a closer look at what threat modeling looks like in practice here at GitLab.\n\nHave something to share? Comment below or find me on twitter at [@muffinbox33](https://twitter.com/Muffinbox33).\n\n_Also, I would like to take a moment to thank the Configure team and [Mikhail](/company/team/#ash2k) for their awesome collaboration during the various threat models we have performed._\n\nHappy threat modeling!\n\nCover image by [Jesús Mirón\nGarcía](https://www.pexels.com/@jesus-miron-garcia-1583477?utm_content=attributionCopyText&utm_medium=referral&utm_source=pexels)\non [Pexels](https://www.pexels.com/photo/timelapse-photography-of-vehicles-on-road-3043592/?utm_content=attributionCopyText&utm_medium=referral&utm_source=pexels)\n\n\n\n## Read more on Kubernetes:\n\n- [How to install and use the GitLab Kubernetes\nOperator](/blog/gko-on-ocp/)\n\n- [How to deploy the GitLab Agent for Kubernetes with limited\npermissions](/blog/setting-up-the-k-agent/)\n\n- [A new era of Kubernetes integrations on\nGitLab.com](/blog/gitlab-kubernetes-agent-on-gitlab-com/)\n\n- [Understand Kubernetes terminology from namespaces to\npods](/blog/kubernetes-terminology/)\n\n- [What we learned after a year of GitLab.com on\nKubernetes](/blog/year-of-kubernetes/)",[9],"yml",{},true,"/en-us/blog/threat-modeling-kubernetes-agent",{"ogTitle":15,"ogImage":19,"ogDescription":16,"ogSiteName":28,"noIndex":12,"ogType":29,"ogUrl":30,"title":15,"canonicalUrls":30,"description":16},"https://about.gitlab.com","article","https://about.gitlab.com/blog/threat-modeling-kubernetes-agent","en-us/blog/threat-modeling-kubernetes-agent",[9],"s5i6sL8Fg937o4Qokl6Vq8eASQsbojCf-ajMtnpjW_c",{"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 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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},1777302643680]