[{"data":1,"prerenderedAt":817},["ShallowReactive",2],{"/en-us/blog/introducing-token-hunter":3,"navigation-en-us":38,"banner-en-us":448,"footer-en-us":458,"blog-post-authors-en-us-Greg Johnson":700,"blog-related-posts-en-us-introducing-token-hunter":714,"blog-promotions-en-us":755,"next-steps-en-us":807},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":12,"meta":26,"navigation":27,"path":28,"publishedDate":20,"seo":29,"stem":33,"tagSlugs":34,"__hash__":37},"blogPosts/en-us/blog/introducing-token-hunter.yml","Introducing Token Hunter",[7],"greg-johnson",null,"security",{"slug":11,"featured":12,"template":13},"introducing-token-hunter",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Introducing Token-Hunter","Our red team has created a new tool to find sensitive data in the vast, wide-open.",[18],"Greg Johnson","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749679669/Blog/Hero%20Images/lightscape-Bsw6l6e01Rw-unsplash.jpg","2019-12-20","\n\nWe operate business at GitLab in a [“public by default”](https://handbook.gitlab.com/handbook/values/#public-by-default) mindset so other people can benefit from our transparent business practices. Defaulting to public sharing also means we store massive amounts of data in a public format by design. Much of what we do as a company takes the form of a GitLab issue and is open for the world to see, including those individuals with nefarious goals. Naturally, for a [Red Team](https://handbook.gitlab.com/handbook/security/security-operations/red-team/), we’re curious about what all of this public information could do to aid someone intent on attacking GitLab. We started our investigation by identifying those secrets that are unintentionally shared across the assets we make public like issues, issue discussions, merge requests, merge request discussions, and snippets. There was no tooling available that accomplished what we set out to do, so we developed it ourselves and just released it: [Token-Hunter](https://gitlab.com/gitlab-com/gl-security/gl-redteam/token-hunter).\n\n### Background\n\nAPI tokens are a keystone in the development world. They facilitate important functionality not only in the software developers build, but also in the deployment, maintenance, integration, and security of both closed and open source projects. Many companies providing services on the internet offer API tokens in multiple flavors that allow interaction with their systems, as does GitLab. Ideally, these tokens offer configurable access control to otherwise closed systems allowing you to impersonate a user’s session and access raw data. Developers, DevOps professionals, infrastructure professionals and the like often depend on API tokens to do their job successfully.\n\nIt’s a common and understandable mistake to make a commit to a Git repository containing one of these tokens when building software in a shared environment. Moving quickly, trying to support your fellow developer, and generally working quickly to get things done efficiently can lead to mistakes made under pressure, which can happen to us all. Popular tools that search for these commits like [gitrob](https://github.com/michenriksen/gitrob), [TruffleHog](https://github.com/dxa4481/truffleHog), [gitleaks](https://github.com/zricethezav/gitleaks), and even GitLab’s own [SAST project](https://docs.gitlab.com/ee/user/application_security/sast/) can find leaked tokens given proper configuration. Our Red Team had early success leveraging these known techniques, tactics, and procedures (TTPs).\n\nThe tools referenced above are fantastic at finding secrets unintentionally left in source code. However, it's also a common mistake to submit sensitive data like API tokens, usernames, and passwords to public locales like [GitLab snippets](https://docs.gitlab.com/ee/user/snippets.html), [issues](https://docs.gitlab.com/ee/user/project/issues/), [issue discussions](https://docs.gitlab.com/ee/api/discussions.html), [merge requests](https://docs.gitlab.com/ee/user/project/merge_requests/), and [merge request discussions](https://docs.gitlab.com/ee/api/discussions.html). Sharing this type of information by accident can happen easily when attempting to share relevant information to facilitate a public support request as we often do at GitLab for many different products. Though most people know not to post sensitive information in a public place directly, mistakes do happen, sometimes shortcuts are taken, logs get shared, configuration files get dropped, and information inadvertently gets leaked and leveraged.  More often than not these areas of exposure are often forgotten, but not by attackers.\n\n### Exploring the wide-open\n\nToken-Hunter is intended to complement tools like gitrob, gitleaks, TruffleHog, and others. It can be used if you’re hosting your groups and projects on GitLab.com, or on a self-managed GitLab instance of your own. We created Token-Hunter to support the following features:\n\n- **Search GitLab issues and the related discussions for sensitive data.** GitLab issues and comments are a primary method of sharing information and resolving support issues. They typically contain shared log data, configuration files, copy/pasted [source code](/solutions/source-code-management/) examples, and discussions by both GitLab employees and customers, and are therefore likely to contain sensitive data.\n- **Search GitLab snippets for sensitive data.** These are small, URL-addressable chunks of code or text intended to be shared between GitLab users or served directly in source code. They are most often used to share small bits of configuration data, JavaScript source code, example code in any language, or log data. Therefore, they can likely contain sensitive information like usernames and passwords, API tokens, etc.\n- **Search GitLab merge requests and discussions for sensitive data.** Merge requests and comments are, more often than not, how public open source projects recieve changes from the community.  At GitLab, merge requests facilitate everything from [handbook updates](https://handbook.gitlab.com/handbook/company/culture/all-remote/handbook-first-documentation/) to [GitLab runner](https://gitlab.com/gitlab-org/gitlab-runner) code changes for both internal employees and external contributors.  Descriptions and discussions on these assets can include log data, system access instructions, and the like.\n- **List all of the projects associated with a group.** This is helpful to quantify the problem and understand where the search will start. Optionally, you can include members’ projects in the search to expand the organizational scope similar to gitrob. Starting at different points in the project after you understand your target more completely can yield very different results.\n- **Proxy all traffic from the tool.** Token-Hunter accepts arguments for an HTTP proxy server and self-signed certificate to decrypt TLS traffic. GitLab’s Red Team used this feature to record traffic pattern examples to the Security Operations team in support of defensive strategy development. This feature is also handy for debugging by examining the traffic the tool generates. [Burp Suite](https://portswigger.net/burp/communitydownload) and [OWASP Zap](https://www.owasp.org/index.php/OWASP_Zed_Attack_Proxy_Project) are two popular tool choices for proxying traffic locally and can be configured with a self-signed certificate to decrypt TLS traffic.\n\nFor full details on using the tool and the functionality of each of its available arguments, visit [the Token-Hunter project page](https://gitlab.com/gitlab-com/gl-security/gl-redteam/token-hunter/tree/master) on GitLab.\n\n### Taming the wild... mostly\n\nHitting an API to gather large amounts of raw data is daunting. Internet connections flake out, servers need maintenance, rate limits get hit, WiFi drops, performance degrades, timeouts happen, and you end up with a headache attempting to simply get the data you’d like to analyze. To counter some of these issues as pragmatically as possible, two simple algorithms were applied: request retries and dynamic page-size reduction. Request retries simply retries a failed request after a few seconds. The tool will retry a failed request twice, each after a four-second delay with a four-second backoff. In other words, the first retry will occur four seconds after the initial failed request. The second retry will occur eight seconds after the first failed retry attempt. If each of these retry attempts fails, the tool then attempts to reduce the paging size in order to complete the request. Reducing the page size reduces the number of records the request needs to return lessening the likelihood of a timeout. *Though simple, these two algorithms allowed the tool to reliably pull data for nearly 1.3 million individual GitLab assets with only three recorded request errors resulting in over 1600 pattern matches.*\n\n### More to explore\n\nThe ability to search discussions and other popular channels where sensitive data is likely to be shared is the key benefit of the Token-Hunter tool over other related tooling. The Red Team plans to continue iterating to support our operations, including adding support for more assets such as [merge requests](https://docs.gitlab.com/ee/user/project/merge_requests/), commit discussions, and [epics](https://docs.gitlab.com/ee/user/group/epics/). We learned during our operation that the specifics of the regular expressions we used in relation to the context in which we were searching (posted log data format, configuration file format, code structure, etc.) largely determined our level of success. It can be necessary to tune these expressions depending on your environment and context. To start, we made a few adjustments to [TruffleHog’s regular expressions](https://github.com/dxa4481/truffleHogRegexes) to add coverage for GitLab-specific token formats. However, there’s still much room for improvement depending on your environment and objective.\n\nLooking for a specific password for a user name? Trying to find all mentions of a specific server DNS name or IP? Expecting a specific log format that has the potential to contain an API token? Tune [the regular expressions](https://gitlab.com/gitlab-com/gl-security/gl-redteam/token-hunter/blob/master/regexes.json), and you just may find what you’re looking for.\n\n### We want your ideas and contributions\n\nThere is still plenty to be done and we welcome community contributions and ideas. If the tool is helpful to you in defense of your infrastructure and you’d like to contribute, [there are instructions in the README.md](https://gitlab.com/gitlab-com/gl-security/gl-redteam/token-hunter#contributing) on how to get started. If you’re not sure what to do, pick an issue out of [our issue list](https://gitlab.com/gitlab-com/gl-security/gl-redteam/token-hunter/issues) or add to the existing discussions.  I'd like to extend a special thank you to GitLab user [Ohad Dahan](https://gitlab.com/ohaddahan) for his many contributions to this and other GitLab projects.  These types of contributions are paramount to the continued success of open source projects.\n\nSome of the ideas we’re currently pursuing are:\n\n- **Better output formatting:** We’d like to standardize output to an industry accepted, standard format that allows support for findings verification. A simple CSV file might be the first step.\n- **Real-time reporting of findings:** Currently, the tool gathers data first, then reports on the findings, leaving you in way too much suspense for way too long. Reporting findings as they are found allows verification to begin earlier during a long-running execution.\n- **Data persistence:** Querying the API is the costliest part of inspecting GitLab assets for sensitive data. Persisting that data from an execution would:\n  - Reduce the need to query the API again after tuning your regular expressions. During our operation, we often needed to make changes to the regular expressions based on what we were seeing in the matches. This was virtually impossible given the amount of data necessary to pull.\n  - Allow for long-running executions to be paused and resumed. Executions against larger groups can take several hours and would sometimes require a restart during our operation.\n  - Maintain a permanent record of findings should they be edited following a found match. During our exercise, there were a few occasions where matches were found that looked to be legitimate, but could not be verified as the asset was modified post-discovery.\n\nWe have learned a lot from this initial attempt at gathering OSINT from rather unique and unorthodox locations, but this exercise was just a start. We hope you find the tooling useful and if you have questions or ideas to share please reach out through [email](mailto:redteam@gitlab.com), through our [issue board](https://gitlab.com/gitlab-com/gl-security/gl-redteam/token-hunter/-/boards), or [on Twitter](https://twitter.com/code_emitter). Happy hacking!\n\nPhoto by [Lightscape](https://unsplash.com/@lightscape?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/photos/Bsw6l6e01Rw).\n",[9,23,24],"security research","open source","yml",{},true,"/en-us/blog/introducing-token-hunter",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":30,"ogSiteName":31,"ogType":32,"canonicalUrls":30},"https://about.gitlab.com/blog/introducing-token-hunter","https://about.gitlab.com","article","en-us/blog/introducing-token-hunter",[9,35,36],"security-research","open-source","ewIlrI2rq9PBk8XbEyZ8zruWVvhICPts7etS6ZsCNIQ",{"data":39},{"logo":40,"freeTrial":45,"sales":50,"login":55,"items":60,"search":368,"minimal":399,"duo":418,"switchNav":427,"pricingDeployment":438},{"config":41},{"href":42,"dataGaName":43,"dataGaLocation":44},"/","gitlab logo","header",{"text":46,"config":47},"Get free 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statement",{"items":690},[691,694,697],{"text":692,"config":693},"Terms",{"href":518,"dataGaName":519,"dataGaLocation":466},{"text":695,"config":696},"Cookies",{"dataGaName":528,"dataGaLocation":466,"id":529,"isOneTrustButton":27},{"text":698,"config":699},"Privacy",{"href":523,"dataGaName":524,"dataGaLocation":466},[701],{"id":702,"title":18,"body":8,"config":703,"content":705,"description":8,"extension":25,"meta":709,"navigation":27,"path":710,"seo":711,"stem":712,"__hash__":713},"blogAuthors/en-us/blog/authors/greg-johnson.yml",{"template":704},"BlogAuthor",{"name":18,"config":706},{"headshot":707,"ctfId":708},"","codeEmitter",{},"/en-us/blog/authors/greg-johnson",{},"en-us/blog/authors/greg-johnson","g8bhRm4wOrH_mAQB68w6-2Pj1sNE3MFxgF3l1ijQifE",[715,728,743],{"content":716,"config":726},{"title":717,"description":718,"authors":719,"date":721,"body":722,"category":9,"tags":723,"heroImage":725},"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.",[720],"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.",[724,9,533],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":27,"template":13,"slug":727},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":729,"config":741},{"title":730,"description":731,"authors":732,"heroImage":734,"date":735,"category":9,"tags":736,"body":740},"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.",[733],"Grant Hickman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1774375772/kpaaaiqhokevxxeoxvu0.png","2026-03-25",[9,737,565,738,739],"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":742,"featured":27,"template":13},"auto-dismiss-vulnerability-management-policy",{"content":744,"config":753},{"title":745,"description":746,"authors":747,"heroImage":749,"date":750,"body":751,"category":9,"tags":752},"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.",[748],"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_).",[739,9,738],{"featured":12,"template":13,"slug":754},"gitlab-18-10-brings-ai-native-triage-and-remediation",{"promotions":756},[757,771,782,793],{"id":758,"categories":759,"header":761,"text":762,"button":763,"image":768},"ai-modernization",[760],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":764,"config":765},"Get your AI maturity score",{"href":766,"dataGaName":767,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":772,"categories":773,"header":774,"text":762,"button":775,"image":779},"devops-modernization",[739,568],"Are you just managing tools or shipping innovation?",{"text":776,"config":777},"Get your DevOps maturity score",{"href":778,"dataGaName":767,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":783,"categories":784,"header":785,"text":762,"button":786,"image":790},"security-modernization",[9],"Are you trading speed for security?",{"text":787,"config":788},"Get your security maturity score",{"href":789,"dataGaName":767,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":791},{"src":792},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":794,"paths":795,"header":798,"text":799,"button":800,"image":805},"github-azure-migration",[796,797],"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":801,"config":802},"See how GitLab compares to GitHub",{"href":803,"dataGaName":804,"dataGaLocation":242},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":806},{"src":781},{"header":808,"blurb":809,"button":810,"secondaryButton":815},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":811,"config":812},"Get your free trial",{"href":813,"dataGaName":49,"dataGaLocation":814},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":504,"config":816},{"href":53,"dataGaName":54,"dataGaLocation":814},1777302614531]