[{"data":1,"prerenderedAt":815},["ShallowReactive",2],{"/en-us/blog/secure-by-design-principles-meet-devsecops-innovation-in-gitlab-17":3,"navigation-en-us":40,"banner-en-us":449,"footer-en-us":459,"blog-post-authors-en-us-Elisabeth Burrows":698,"blog-related-posts-en-us-secure-by-design-principles-meet-devsecops-innovation-in-gitlab-17":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":26,"isFeatured":12,"meta":27,"navigation":12,"path":28,"publishedDate":20,"seo":29,"stem":34,"tagSlugs":35,"__hash__":39},"blogPosts/en-us/blog/secure-by-design-principles-meet-devsecops-innovation-in-gitlab-17.yml","Secure By Design Principles Meet Devsecops Innovation In Gitlab 17",[7],"elisabeth-burrows",null,"security",{"slug":11,"featured":12,"template":13},"secure-by-design-principles-meet-devsecops-innovation-in-gitlab-17",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Secure by Design principles meet DevSecOps innovation in GitLab 17","GitLab reinforced a commitment to Secure by Design principles across key aspects of the software development lifecycle in latest release, further protecting the software supply chain.",[18],"Elisabeth Burrows","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749676004/Blog/Hero%20Images/blog-image-template-1800x945__6_.png","2024-06-05","Secure by Design just [turned one](https://about.gitlab.com/blog/happy-birthday-secure-by-design/)! Introduced by the Cybersecurity and Infrastructure Security Agency (CISA) a little over a year ago, Secure by Design principles serve as a directive for technology providers to embed security at the heart of their products from the outset of development. This approach is the clearest answer to address cyber attacks, dramatically reducing the number of exploitable flaws before they are introduced to the market for broad use or consumption. Cyberattacks can be more prevalent when businesses and vendors “bolt on” security as an afterthought, amplifying the need for Secure by Design solutions. With the launch of [GitLab 17](https://docs.gitlab.com/releases/17/gitlab-17-0-released/), we have strengthened our commitment to Secure by Design principles across five key aspects of the software development lifecycle. Although no supply chain is 100% immune to cyber threats, it is imperative to embrace a proactive security strategy to protect against persistent threats from malicious actors.\n\n> Discover the future of AI-driven software development with our GitLab 17 virtual launch event. [Watch today!](https://about.gitlab.com/eighteen/)\n\n## How GitLab 17 aligns with Secure by Design principles\n\n### 1. Enhance secure coding practices\n\nFostering secure software development practices is a key element to CISA’s Secure by Design framework. CISA recommends alignment to the [Secure Software Development Framework (SSDF)](https://csrc.nist.gov/pubs/sp/800/218/final) from the National Institute of Standards and Technology (NIST). GitLab’s robust [application security scanners](https://about.gitlab.com/blog/getting-started-with-gitlab-application-security/) demonstrate strong default alignment to this framework. In GitLab 17, we added streamlined Static Application Security Testing ([SAST](https://docs.gitlab.com/ee/user/application_security/sast/)) analyzer coverage for more languages, offering a simpler, more customizable scan experience. The recent [acquisition of Oxeye](https://about.gitlab.com/press/releases/2024-03-20-gitlab-acquires-oxeye-to-advance-application-security-and-governance-capabilities/) enhances SAST accuracy, reducing false positives and offering actionable insights to tackle application-layer risks proactively. Other related improvements in GitLab 17 include [API Security Testing analyzer](https://docs.gitlab.com/ee/user/application_security/api_security_testing/) updates, [advanced vulnerability tracking for Secret Detection](https://docs.gitlab.com/ee/user/application_security/secret_detection/pipeline/#advanced-vulnerability-tracking), and [Dependency Scanning support for Android](https://docs.gitlab.com/ee/user/application_security/dependency_scanning/index.html#enabling-dependency-scanning-by-using-cicd-components). GitLab also continues to improve its [dynamic software bill of materials (SBOM) management](https://about.gitlab.com/blog/the-ultimate-guide-to-sboms/) capabilities.\n\n### 2. Manage vulnerabilities at scale\n\nMalicious actors capitalize on cost-effective tactics, leveraging basic vulnerabilities to cause widespread disruption. GitLab’s [Vulnerability Report](https://docs.gitlab.com/ee/user/application_security/vulnerability_report/) enables you to quantify risk across your portfolio in a single view, identifying key vulnerability details throughout your supply chain. Improvements to [Vulnerability Report filtering](https://docs.gitlab.com/ee/user/application_security/vulnerability_report/#vulnerability-report-filters) in GitLab 17 increased usability of the report at scale. Actionable security findings are vital for developers to address critical weaknesses. GitLab provides [vulnerability insights](https://docs.gitlab.com/ee/user/application_security/vulnerabilities/), [security training for vulnerabilities](https://docs.gitlab.com/ee/user/application_security/vulnerabilities/#view-security-training-for-a-vulnerability), and [vulnerability explanation](https://docs.gitlab.com/ee/user/application_security/vulnerabilities/#vulnerability-explanation).\n### 3. Transition to memory-safe languages with AI\n\nIn a recent [virtual panel with the Atlantic Council](https://www.atlanticcouncil.org/event/the-secure-by-design-imperative/), CISA Senior Technical Advisor Jack Cable stated, “Technology manufacturers must focus on eliminating entire classes of vulnerability, rather than playing “whack-a-mole” with their defects.” In CISA’s [Secure by Design whitepaper](https://www.cisa.gov/sites/default/files/2023-10/SecureByDesign_1025_508c.pdf), they recommend that manufacturers take steps to eliminate one of the largest classes of vulnerabilities by migrating existing products and building new products using memory-safe languages. A memory-safe language is a language where memory allocation and garbage collection are abstracted away from the developer and handled by the programming language itself. Such languages include Python, Java, and Go, to name a few. Vulnerabilities related to memory safety are the most common and dangerous. Technology manufacturers can effectively address vulnerabilities by integrating memory-safe language development practices. [GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/), our suite of AI-powered features, provides AI-accelerated assistance for memory-safe code conversions:\n\n- Accelerate application development: [GitLab Duo Code Explanation](https://docs.gitlab.com/ee/user/ai_features.html#code-explanation-in-the-ide) succinctly articulates code functionality in everyday language, helping developers understand code quickly and add value faster. [GitLab Duo Code Suggestions](https://docs.gitlab.com/ee/user/ai_features.html#code-suggestions) assists developers in writing secure code efficiently and speeding up cycle times by handling repetitive coding tasks effectively.\n- Convert to memory-safe code: [GitLab Duo Chat](https://docs.gitlab.com/ee/user/gitlab_duo_chat_examples.html#refactor-code-in-the-ide) can help expedite memory-safe language refactoring by suggesting changes based on coding patterns, libraries, functions, algorithms, programming languages, performance, or vulnerabilities.\n- Secure AI-generated code: [GitLab Duo Vulnerability Explanation](https://docs.gitlab.com/ee/user/ai_features.html#vulnerability-explanation) provides clear insights into identified security issues, while [GitLab Duo Vulnerability Resolution](https://docs.gitlab.com/ee/user/ai_features.html#vulnerability-resolution) can automatically generate a merge request to mitigate a vulnerability.\nIn GitLab 17, we also have added the means to validate and track AI impact to your development progress through [AI Impact Analytics](https://about.gitlab.com/blog/developing-gitlab-duo-ai-impact-analytics-dashboard-measures-the-roi-of-ai/).\n\n### 4. Align to the principle of least privilege\n\nAligning product deployment guides with zero trust architecture, such as the [CISA Zero Trust Maturity Model](https://www.cisa.gov/sites/default/files/2023-04/zero_trust_maturity_model_v2_508.pdf), is a key recommendation in Secure by Design. In zero trust security, the [principle of least privilege (PoLP)](https://about.gitlab.com/blog/the-ultimate-guide-to-least-privilege-access-with-gitlab/) is a key element within the overarching framework. The PoLP is a concept in which a user's access rights should be limited to the bare minimum needed for them to complete the tasks required within their respective roles. By keeping a tight rein on user access rights, granting only the necessary permissions for their tasks, organizations uphold the core tenet of [zero trust](https://about.gitlab.com/blog/why-devops-and-zero-trust-go-together/). Maintaining a clear separation of duties is the first step in upholding this principle. GitLab's [policy management](https://docs.gitlab.com/ee/administration/compliance.html#policy-management) features empower security and compliance teams to oversee operations while defining responsibilities among security, compliance, legal, and engineering units. By implementing GitLab's [security policies](https://docs.gitlab.com/ee/user/application_security/policies/), development teams gain process flexibility, ensuring the delivery of stable, reliable, and high-quality code. With the ability to establish rules and policies tailored to the organization's unique needs, teams can utilize granular user roles, permissions, and customizable compliance settings for specific projects, groups, and individuals. GitLab 17 introduces enhanced governance controls via [permissions customizations](https://about.gitlab.com/releases/2024/05/16/gitlab-17-0-released/#new-permissions-for-custom-roles), reducing unnecessary privilege escalation.\n\n## How we are committed to Secure by Design principles\n\nOne of the principles of Secure by Design business practices is the notion of leading from the top. It's imperative for organizations to secure executive buy-in that places Secure by Design at the forefront of business priorities, nurturing an environment where security takes precedence. GitLab recently joined the ranks of technology leaders who signed CISA’s [Secure by Design Pledge](https://www.cisa.gov/securebydesign/pledge/statements-of-support#JoshLemosCISOGitLab), showcasing our commitment to uphold CISA’s Secure by Design goals. This public commitment, paired with strategic investments, a culture of transparency, and product designs that prioritize security, fosters a robust security ethos that directly benefits end users. With the launch of GitLab 17, GitLab propels security and compliance solutions forward, harnessing AI advancements to empower clients to embrace a Secure by Design methodology with confidence.\n\n> Get familiar with GitLab's secure-by-design platform today with a [free trial of GitLab Ultimate](https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com/blog&glm_content=default-saas-trial). \n",[23,9,24,25],"public sector","DevSecOps 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statement",{"items":688},[689,692,695],{"text":690,"config":691},"Terms",{"href":519,"dataGaName":520,"dataGaLocation":467},{"text":693,"config":694},"Cookies",{"dataGaName":529,"dataGaLocation":467,"id":530,"isOneTrustButton":12},{"text":696,"config":697},"Privacy",{"href":524,"dataGaName":525,"dataGaLocation":467},[699],{"id":700,"title":18,"body":8,"config":701,"content":703,"description":8,"extension":26,"meta":707,"navigation":12,"path":708,"seo":709,"stem":710,"__hash__":711},"blogAuthors/en-us/blog/authors/elisabeth-burrows.yml",{"template":702},"BlogAuthor",{"name":18,"config":704},{"headshot":705,"ctfId":706},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659535/Blog/Author%20Headshots/liz_burrows_headshot.png","6Nj2Lio5W7HdeNYoysVgCf",{},"/en-us/blog/authors/elisabeth-burrows",{},"en-us/blog/authors/elisabeth-burrows","Aa3Md9daKifGCswl2xZz_ybV8wPpCajPpr3-IX9wRRg",[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.",[722,9,24],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":12,"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",[9,735,25,736,737],"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":12,"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&amp;autopause=0&amp;player_id=0&amp;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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