[{"data":1,"prerenderedAt":815},["ShallowReactive",2],{"/en-us/blog/agile-iteration-unique-onboarding-experience":3,"navigation-en-us":36,"banner-en-us":446,"footer-en-us":456,"blog-post-authors-en-us-Michael Fahey":698,"blog-related-posts-en-us-agile-iteration-unique-onboarding-experience":712,"blog-promotions-en-us":753,"next-steps-en-us":805},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":24,"isFeatured":12,"meta":25,"navigation":26,"path":27,"publishedDate":20,"seo":28,"stem":32,"tagSlugs":33,"__hash__":35},"blogPosts/en-us/blog/agile-iteration-unique-onboarding-experience.yml","Agile Iteration Unique Onboarding Experience",[7],"michael-fahey",null,"security",{"slug":11,"featured":12,"template":13},"agile-iteration-unique-onboarding-experience",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Agile iteration: My unique onboarding experience at GitLab","How I learned to iterate quickly during my first week at GitLab.",[18],"Michael Fahey","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749662877/Blog/Hero%20Images/security-cover-new.png","2019-04-26","\n\nMy name is Michael Fahey. I have been working in the security and IT industries for over 15 years. Recently, I joined GitLab’s Security Team as the manager of the [Red Team](https://handbook.gitlab.com/handbook/security/#red-team). The GitLab Red Team is responsible for assessing the overall security posture of GitLab as a company as well as testing the security and defensive capabilities of our products and services.\n\nWe demonstrate that by telling the stories of our exploits, to help provide context and flavor to the risks we identify. We are white-hat hackers emulating adversaries, and bad guys, so we can rapidly iterate on our security practices resulting in a stronger security posture and better security products.\n\nThe Red Team is a new team. Initially, when I talked to my manager, I was expecting to plan and conduct Red Team exercises after I onboarded. An opportunity presented itself for me to join the [CEO Shadow Program](https://handbook.gitlab.com/handbook/ceo/shadow/), so instead, in my second week, I was in San Francisco working with the CEO of GitLab, [Sid Sijbrandij](/company/team/#sytses)! One thing to know about Sid is that he is passionate about security, so while I was a part of the CEO Shadow Program, he recommended we perform a social engineering exercise against GitLab. I was starting to understand how serious GitLab is about our values, and I wanted to get the firsthand experience with one of our values, [iteration](https://handbook.gitlab.com/handbook/values/#iteration).\n\nThe tempo at which everything was going was not something I was used to. When faced with a new situation like this, I try to emphasize care by slowly gathering information on the target, then building a believable story to persuade the target to perform what I want them to. Social engineering exercises are more about building trust and sympathy than anything else. Sid, however, insisted that I just execute and iterate on the exercise, despite my reservations. Sid was trying to teach me something important which I did not yet grasp.\n\n## What are our Red Team exercise goals?\n\nThe goal of the exercise was to observe how a new employee would react to the demands of the CEO. From the perspective of an adversary, the goal was to compromise GitLab.com by impersonating the CEO, and then demand that an employee with privileged access install an authorization key, controlled by the Red Team, to production servers.\n\nThe expected value of this basic exercise was to identify areas of improvement and level set on our current security stance. It's a starting point to allow us to iterate and build upon. Ideally, we hoped our chosen target would report the incident to the Security Operations Team. At that point, the event would be triaged, and the account deactivated quickly to mitigate any further impact.\n\n### Here is how we scoped this basic exercise:\n\n- Limit the activity to Slack.\n- Emulate an immature, aggressive adversary.\n- Target and identify the people who administer our production systems.\n- Assume compromise of the CEO's Slack identity. For the objectives of this exercise, we don't care how Sid's identity got compromised. In fact, the impersonated Slack account was provisioned before this exercise.\n- The Security Operations Team were not aware of the engagement and were not notified prior to this exercise.\n\n### How did the attempted compromise go?\n\nSo, as the adversary, we started out with the pre-provisioned CEO slack account and logged in. Next, we needed to learn more about GitLab and find the weakest link in the chain to exploit. Luckily, GitLab makes all the information we need publicly available\nwithin the [handbook](https://handbook.gitlab.com/handbook/) and [team](/company/team/) pages.\n\n### Here is what we learned:\n\n- The Infrastructure Team administers all of GitLab’s production systems.\n- The Infrastructure Team remotely accesses, controls and manages GitLab.com.\n- I identified a new GitLab team-member who had just joined the Infrastructure Team. His Slack profile really stood out for us:\n\n![New GitLab team-member Slack profile](https://about.gitlab.com/images/blogimages/red-team-exercise/slack-profile.png){: .shadow.medium.center}\n\nWe found the status of “Onboarding – be gentle” too good not to take up. So, we sent out an urgent request impersonating the CEO of GitLab. “Sid” had an urgent request to add his SSH key to the production systems and Target0 was the only one that could help. Check out what the Red Team sent him below.\n\n![Message from \"Sid\"](https://about.gitlab.com/images/blogimages/red-team-exercise/sid-message.png){: .shadow.medium.center}\n\nFor context alone, there is one crucial fact to understand. An Advanced Persistent Threat (APT) would not burn such a high-value asset as Sid’s Slack profile on something so aggressive. It has too high of a chance for failure. That isn’t to say this doesn’t happen. It is generally a more immature adversary who just wants to do a smash and grab of whatever they can get.\n\nWith the message sent, Target0 never responded. We didn’t have any insight as to what was happening, and we didn’t want to push too hard, so we took a different tactic. We contacted his manager, Target1 to see if we could pressure Target0 through another trusted means.\n\n![Message from \"Sid\" to manager](https://about.gitlab.com/images/blogimages/red-team-exercise/sid-message-manager.png){: .shadow.medium.center}\n\nLooks like we are onto something here! Target1 is going to look into it for us, but we hear nothing back. At this point in the exercise, we were still not sure what was happening in the background and waited over an hour. Our access was still intact, so we weren’t sure if we were caught or they were working on implementing the request.\n\n## What actually happened?\n\nTurns out Target0 challenged the request and reached out for help from our Security Operations team. We failed to compromise GitLab.com, but there could be more to learn in how Security Operations responded to the event. One can see that Target0 created the following ticket below. At that point, our Security Operations team was on it!\n\n![Ticket to SecOps](https://about.gitlab.com/images/blogimages/red-team-exercise/sec-ops-ticket.png){: .shadow.medium.center}\n\nThe Security Operations Team immediately triaged the issue. They got in touch with Sid’s executive assistant. She asked the Security Operations team to hold off on any action then went unresponsive for a half hour, because she knew about this exercise, and was advised to take the actions that she took. This stalled the response process. During that time, the Red Team still had control over Sid’s Slack account, which had not been deactivated.\n\n## What were the results of the exercise?\n\nFrom a Red Team perspective, we _wanted_ to fail in our exercises, but fail or succeed, it is critical that everyone involved learns from the experience.\n\n### Here are some key observations:\n\n- Target0 and Target1's instincts and decisions were validated. They did the right thing to challenge and report the request from “Sid.” They are now more empowered to challenge dubious claims in the future. Heroes of the story!\n\n- The Security Operations Team quickly responded to and triaged the incident. However, through a combination of the following, a final response was delayed:\n    - Sid’s executive assistant requested to delay action until she heard back from Sid.\n    - There was a lack of evidence indicating unauthorized access (via investigation of Slack’s audit logs).\n    - Positive confirmation from the executive assistant that Sid was in an interview (thus no physical breach). A Security Operations team member later jested:\n\n![SecOps team member joke](https://about.gitlab.com/images/blogimages/red-team-exercise/slack-comment.png){: .shadow.medium.center}\n\n- Communication is critical when running Red Team exercises, and a failure in communication can lead to failures in efficiency. For example:\n    - When the Red Team exercise is starting, send a notification to leadership that the activity is beginning, so that leaders can better respond to the natural panic of these engagements.\n    - Perform a Zoom review meeting with the Sr. Director of Security, VP of Engineering, and the CEO to make sure everyone is on the same page.\n\n## How did this social experiment play out?\n\nGitLab is a growing startup with lots of new employees onboarding and an evolving security organization. GitLab demonstrated their ability to be agile and security-aware, but we’ve now started a conversation on why people shouldn’t blindly follow orders due to the person's position and authority, like the CEO. That is precisely why controls like Separation of Duties [(NIST 800-53 Security Control: CA-5)](https://nvd.nist.gov/800-53/Rev4/control/AC-5) and the incident response process are so critical.\n\nThis exercise allowed both the Red Team and Security Operations Team the opportunity to learn and grow together. Red Team is the robbers and Security Operations the cops, but what can happen if the robbers and cops start working together? If one of my favorite shows, \"White Collar,\" is any indicator, we can achieve far more together than we could alone.\n\n## What did I learn from all of this?\n\nFrom my perspective, I expected Target0 and Target1 to report the issue and Security Operations to respond to the incident. The Red Team’s goal should be about empowering people to champion cybersecurity challenges and solutions. We may do that through adversarial means to highlight problems, but it should always be for the benefit of the employees, customer, and company. I feel like some of us in the industry forget that from time to time.\n\nOutside of the exercise, I learned the importance of [iteration](https://handbook.gitlab.com/handbook/values/#iteration)\nand a strategic concept GitLab employs called [Breadth over Depth](https://handbook.gitlab.com/handbook/company/strategy/#breadth-over-depth).\nThe idea is to iterate as fast as possible to learn and grow as fast as possible. Quickly learn and grow as opposed to planning something over days and weeks.\n\nIf you quickly iterate then fail or succeed, you can learn far more than if you carefully planned\nevery step then execute on that plan. There is no guarantee that any plan or\nidea will succeed, no matter how much planning and thought you put into it. There is truth in\nthe saying, “No plan survives first contact with the enemy.”\n\nWe can’t wait for that perfect moment or take the time to develop the perfect plan because we will become stagnant and learn little otherwise. What should you do then? Rapidly iterate. Over time, you will grow far faster, be more capable, and have greater insight into your solution.\n",[23,9],"inside GitLab","yml",{},true,"/en-us/blog/agile-iteration-unique-onboarding-experience",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":29,"ogSiteName":30,"ogType":31,"canonicalUrls":29},"https://about.gitlab.com/blog/agile-iteration-unique-onboarding-experience","https://about.gitlab.com","article","en-us/blog/agile-iteration-unique-onboarding-experience",[34,9],"inside-gitlab","Qix8CQ3sjqDLNZ3DnvkdzpsfSLtLS55KV-Ilw33J_1k",{"data":37},{"logo":38,"freeTrial":43,"sales":48,"login":53,"items":58,"search":366,"minimal":397,"duo":416,"switchNav":425,"pricingDeployment":436},{"config":39},{"href":40,"dataGaName":41,"dataGaLocation":42},"/","gitlab logo","header",{"text":44,"config":45},"Get free 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statement",{"items":688},[689,692,695],{"text":690,"config":691},"Terms",{"href":516,"dataGaName":517,"dataGaLocation":464},{"text":693,"config":694},"Cookies",{"dataGaName":526,"dataGaLocation":464,"id":527,"isOneTrustButton":26},{"text":696,"config":697},"Privacy",{"href":521,"dataGaName":522,"dataGaLocation":464},[699],{"id":700,"title":18,"body":8,"config":701,"content":703,"description":8,"extension":24,"meta":707,"navigation":26,"path":708,"seo":709,"stem":710,"__hash__":711},"blogAuthors/en-us/blog/authors/michael-fahey.yml",{"template":702},"BlogAuthor",{"name":18,"config":704},{"headshot":705,"ctfId":706},"","mfahey",{},"/en-us/blog/authors/michael-fahey",{},"en-us/blog/authors/michael-fahey","HpQomxCAzuH7i3qmWq0_XOOZ4W4T3ArfzviqTdBJPvA",[713,726,741],{"content":714,"config":724},{"title":715,"description":716,"authors":717,"date":719,"body":720,"category":9,"tags":721,"heroImage":723},"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.",[718],"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.",[722,9,531],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":26,"template":13,"slug":725},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":727,"config":739},{"title":728,"description":729,"authors":730,"heroImage":732,"date":733,"category":9,"tags":734,"body":738},"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.",[731],"Grant Hickman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1774375772/kpaaaiqhokevxxeoxvu0.png","2026-03-25",[9,735,563,736,737],"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":740,"featured":26,"template":13},"auto-dismiss-vulnerability-management-policy",{"content":742,"config":751},{"title":743,"description":744,"authors":745,"heroImage":747,"date":748,"body":749,"category":9,"tags":750},"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.",[746],"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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