[{"data":1,"prerenderedAt":815},["ShallowReactive",2],{"/en-us/blog/how-to-detecting-secrets-in-video":3,"navigation-en-us":37,"banner-en-us":447,"footer-en-us":457,"blog-post-authors-en-us-Dennis Appelt":699,"blog-related-posts-en-us-how-to-detecting-secrets-in-video":713,"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":25,"isFeatured":12,"meta":26,"navigation":12,"path":27,"publishedDate":20,"seo":28,"stem":33,"tagSlugs":34,"__hash__":36},"blogPosts/en-us/blog/how-to-detecting-secrets-in-video.yml","How To Detecting Secrets In Video",[7],"dennis-appelt",null,"security",{"slug":11,"featured":12,"template":13},"how-to-detecting-secrets-in-video",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How-to: Detecting secrets in video content ","GitLab’s Security team identifies and mitigates security risks in video content by searching for API keys or other sensitive tokens. Here's how we do it (with an assist from AI) and how you can, too.",[18],"Dennis Appelt","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099421/Blog/Hero%20Images/Blog/Hero%20Images/security-checklist_security-checklist.png_1750099421443.png","2024-02-29","Today, we are open-sourcing our [solution for detecting secrets in video content]( https://gitlab.com/gitlab-com/gl-security/security-research/video-scanner/youtube-video-scanner). We use it internally to search videos published on our [GitLab Unfiltered YouTube channel](https://www.youtube.com/@GitLabUnfiltered) for [secrets](https://docs.gitlab.com/ee/security/token_overview.html) such as API keys and other sensitive tokens.\n\nWhile there are existing tools for secret detection, we did not find a tool that quite fit the bill for our use case, so we decided to implement a custom scanner. In this blog post, we'll walk through our general approach, some of the challenges we encountered, and our solution. We'll also discuss how GitLab’s new AI assistant, [GitLab Duo Chat](https://about.gitlab.com/gitlab-duo-agent-platform/), helped with the implementation of the scanner.\n\n## Scanning videos, one frame at a time\n\nOur general approach to secret detection in videos is quite simple: Split the video into frames, run optical character recognition (OCR) over each frame, and match the resulting text against known [secret patterns](https://docs.gitlab.com/ee/security/token_overview.html). If a secret is found, a [security incident](https://handbook.gitlab.com/handbook/security/security-operations/sirt/engaging-security-on-call/#engage-the-security-engineer-on-call) is kicked off to investigate the leak and revoke exposed secrets.\n\nTo implement this approach, we first experimented using [FFmpeg](https://ffmpeg.org//) for splitting the video into frames and feeding the frames to [Tesseract](https://github.com/tesseract-ocr/tesseract), an open-source engine for OCR. This worked quite well and gave us confidence that the general approach was feasible. However, we decided to switch to [Google Cloud Platform's Video Intelligence API](https://cloud.google.com/video-intelligence/docs/) for the frame splitting and OCR for the simple reason of not having to scale and maintain our own implementation.\n\nFFmpeg and Tesseract are good options if third-party APIs cannot be used or if more control over the process is required. For example, if the secrets are only exposed for a brief moment in the video, using FFmpeg allows you to increase the frame sampling rate to analyze more frames per second and increases the chances of catching the frame that exposes the secret. The Video Intelligence API does not provide a comparable level of control. \n\nThe choice between the Video Intelligence API and FFmpeg + Tesseract also depends on the data set that has to be analyzed. The Video Intelligence API works well on our data set, which makes the additional complexity of a custom implementation based on FFmpeg + Tesseract hard to justify. After settling for the Video Intelligence API, it was a natural choice to host the rest of the scanner on GCP as well. The below diagram gives an overview of the design:\n\n![video content scanners - image 1](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099431/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750099431138.png)\n\nThe scanner is implemented as a collection of cloud functions running on GCP. The cloud function `WebSub API` implements the WebSub [spec](https://www.w3.org/TR/websub/), which is used by YouTube to deliver notifications. Notifications of new videos are published to a PubSub topic, which the cloud function `Video Fetcher` is subscribed to. If a message is received, the video is downloaded and submitted for OCR to the Video Intelligence API. The resulting text extract is checked for secrets by the `Secret Matcher` and alerts are created in case a secret is found.\n\n## Accounting for inaccuracies in OCR\n\nThe described approach sounds simple enough, but as with most things, the devil is in the details. When comparing the video scanner to other secret scanning methods, a notable difference is how the video scanner determines if a given string literal is a secret. Secret detection tools usually determine if the given text contains a secret by matching the text against a list of regular expressions, each defining the format of a secret. If there is a match, a secret is detected.\n\n![video content scanners - image 2](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099431/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750099431139.png)\n\n\u003Ccenter>\u003Ci>A video frame showing a GitLab access token\u003C/i>\u003C/center>\n\n\u003Cp>\u003C/p>\n\nWhen it comes to video scanning, this approach has limited effectiveness due to the OCR step. In some instances, the recognized text does not quite match the text displayed in the video. For example, the above video frame shows the access token `glpat-HseyLLLE92Ubso2vyVeD` and OCR extracted the text `glpat-HseyLLLE92Ubso2vyVe\\`. The last character of the secret is `D`, but OCR extracted a backslash ( `\\`). This error causes the extracted text to no longer match the format of GitLab personal access tokens; therefore, simply matching the text against a regular expression conforming to the token format would have not detected the leaked access token.\n\nTo account for the inaccuracies that are introduced by the OCR step, the video scanner uses approximate regular expression matching where a string is not required to match a regular expression exactly, but small deviations in the strings are allowed. These deviations are expressed as string edit distance and define how many characters in the string need to be inserted, deleted, or substituted to make the string match a given regular expression. For example, the string edit distance for the previous example is 1 because the erroneously detected backslash has to be substituted with an alphanumeric character or a minus sign to make the string match the GitLab personal access token format.\n\nFinding the right value for the maximum edit distance for a string to still be considered to match a regular expression depends on the data set and requires some experimentation. If the value is too low, secrets might be missed and if the value is too high, strings that are not secrets will be matched. To find the right balance for our use case, we collected a data set of videos leaking secrets and ran them through our scanner pipeline. We measured precision (how many matches are not secrets) and recall (how many matches are relevant/true secrets) for different maximum edit distances. We collected this data for the two most relevant types of secrets: GitLab API access tokens and runner registration tokens. The below diagrams show the results.\n\nFor both types of secrets, the optimal maximum cost is 2, at which point a high precision and recall are achieved (97% precision and 91% recall for access tokens; 100% precision and 86% recall for runner registration tokens). Decreasing the maximum distance results in worse recall, while increasing the maximum cost leads to either no improvement (runner registration token) or adversely affects precision (access tokens).\n\n![video content scanner - image 3](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099431/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750099431141.png)\n\n\u003Cp>\u003C/p>\n\n![video content scanner - image 4](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099431/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750099431143.png)\n\n## Implementation made easy using GitLab Duo Chat\n\nAfter some initial experimentation, it was clear to us that approximate regular expression matching can increase the detection accuracy of our video scanner. However, the implementation turned out not to be trivial. While the [algorithms for approximate regular expression matching](https://www.tgries.de/agrep/) have existed since the '90s, there is no widespread library support. [TRE](https://laurikari.net/tre/) is a solid choice but is written in C and thus requires bindings to be called from the scanner’s runtime environment (Node.js).\n\nTo reduce the development effort, we turned to [GitLab Duo Chat](https://about.gitlab.com/gitlab-duo-agent-platform/). After discussing our options with Duo Chat for calling C from Node.js, we settled on [implementing a C++ add-on](https://nodejs.org/docs/latest-v20.x/api/addons.html). We quickly iterated towards a working implementation, which would have undoubtedly taken considerably more time without the help of GitLab Duo Chat. Below is an excerpt from the chat session.\n\n> Me: Are you familiar with the C library TRE? Can you help me write a Node.js addon that is a wrapper for TRE?\n\n> Duo: Here is the template for wrapping the TRE library in a Node.js addon using N-API:\n>\n>![video content scanner - image 5](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099431/Blog/Content%20Images/Blog/Content%20Images/Screenshot_2024-02-28_at_9.51.14_AM_aHR0cHM6_1750099431144.png)\n>\n> The key steps are:\n> - Include TRE header\n> - Get tre regex_t from parameters\n> - Use tre_regcomp() to compile pattern\n> - Execute regexec()\n> - Handle TRE match results\n> - Return results back to JavaScript\n>\n> Let me know if this helps explain how to integrate TRE regex usage in a Node.js add-on!\n\n> Me: Ok, now let's add code for the tasks that you outlined in the template. I want to call this C++ from JavaScript like this:\n>\n>![video content scanners - image 6](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099431/Blog/Content%20Images/Blog/Content%20Images/Screenshot_2024-02-28_at_9.53.05_AM_aHR0cHM6_1750099431145.png)\n>\n> In this example, the first parameter is the regular expression that should be used to create the parameter regex_t *preg, the second parameter is the string that should be matched against the regular expression, and the third parameter is an integer that represents TRE’s max_cost.\n\n>Duo: Here is example C++ code implementing TRE regex usage to match the provided JavaScript code snippet.\n>\n>![video content scanners - image 7](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099431/Blog/Content%20Images/Blog/Content%20Images/Screenshot_2024-02-28_at_9.54.08_AM_aHR0cHM6_1750099431146.png)\n>\n> Let me know if you need any clarification on this implementation!\n\n> Me: Thank you for the code. Can you change the call tre_regexec to tre_regaexec?\n\n> Duo: Here is the code updated to use tre_regaexec instead of tre_regexec:\n>\n>![code updated to use tre_regaexec instead of tre_regexec](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099431/Blog/Content%20Images/Blog/Content%20Images/Screenshot_2024-02-28_at_9.56.29_AM_aHR0cHM6_1750099431146.png)\n>\n> The key changes are:\n> - Using `tre_regaexec` instead of `tre_regexec`\n> - Initializing a `regamatch_t` instead of `regmatch_t`\n> - Passing a regaparams_t struct to configure match parameters\n> \n> Let me know if you need any clarification or have additional questions!\n\nWhile the code generation capabilities are very impressive and save a lot of time, the generated code needs review and sometimes correction. For example, in our session with Duo Chat it is prompted to call [tre_regaexec](https://laurikari.net/tre/documentation/regaexec/), but the generated code is calling the similarly named, but distinct function [tre_regexec](https://laurikari.net/tre/documentation/regexec/). Typically it is enough to make Duo Chat aware of its mistake and it will correct the code in question. In this respect, working with Duo Chat feels like pair programming rather than fully outsourcing a programming task.\n\nThe full add-on code is available [here](https://gitlab.com/gitlab-com/gl-security/security-research/video-scanner/tre-node-bindings/).\n\n## Try our open-source implementation\n\nWe are [making the implementation of the scanner open source](https://gitlab.com/gitlab-com/gl-security/security-research/video-scanner/youtube-video-scanner) under the MIT license. We hope this solution can help you with detecting secrets in your own video content. 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statement",{"items":689},[690,693,696],{"text":691,"config":692},"Terms",{"href":517,"dataGaName":518,"dataGaLocation":465},{"text":694,"config":695},"Cookies",{"dataGaName":527,"dataGaLocation":465,"id":528,"isOneTrustButton":12},{"text":697,"config":698},"Privacy",{"href":522,"dataGaName":523,"dataGaLocation":465},[700],{"id":701,"title":18,"body":8,"config":702,"content":704,"description":8,"extension":25,"meta":708,"navigation":12,"path":709,"seo":710,"stem":711,"__hash__":712},"blogAuthors/en-us/blog/authors/dennis-appelt.yml",{"template":703},"BlogAuthor",{"name":18,"config":705},{"headshot":706,"ctfId":707},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749672032/Blog/Author%20Headshots/dappelt-headshot.jpg","dappelt",{},"/en-us/blog/authors/dennis-appelt",{},"en-us/blog/authors/dennis-appelt","s0eKcsSwHvpcru4Dk7IkXJyRfuBEWrMhfVAJ_Fan-L4",[714,727,741],{"content":715,"config":725},{"title":716,"description":717,"authors":718,"date":720,"body":721,"category":9,"tags":722,"heroImage":724},"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.",[719],"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.",[723,9,532],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":12,"template":13,"slug":726},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":728,"config":739},{"title":729,"description":730,"authors":731,"heroImage":733,"date":734,"category":9,"tags":735,"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.",[732],"Grant Hickman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1774375772/kpaaaiqhokevxxeoxvu0.png","2026-03-25",[9,23,564,736,737],"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&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_).",[737,9,736],{"featured":29,"template":13,"slug":752},"gitlab-18-10-brings-ai-native-triage-and-remediation",{"promotions":754},[755,769,780,791],{"id":756,"categories":757,"header":759,"text":760,"button":761,"image":766},"ai-modernization",[758],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":762,"config":763},"Get your AI maturity score",{"href":764,"dataGaName":765,"dataGaLocation":241},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":767},{"src":768},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":770,"categories":771,"header":772,"text":760,"button":773,"image":777},"devops-modernization",[737,567],"Are you just managing tools or shipping innovation?",{"text":774,"config":775},"Get your DevOps maturity score",{"href":776,"dataGaName":765,"dataGaLocation":241},"/assessments/devops-modernization-assessment/",{"config":778},{"src":779},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":781,"categories":782,"header":783,"text":760,"button":784,"image":788},"security-modernization",[9],"Are you trading speed for security?",{"text":785,"config":786},"Get your security maturity score",{"href":787,"dataGaName":765,"dataGaLocation":241},"/assessments/security-modernization-assessment/",{"config":789},{"src":790},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":792,"paths":793,"header":796,"text":797,"button":798,"image":803},"github-azure-migration",[794,795],"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":799,"config":800},"See how GitLab compares to GitHub",{"href":801,"dataGaName":802,"dataGaLocation":241},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":804},{"src":779},{"header":806,"blurb":807,"button":808,"secondaryButton":813},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":809,"config":810},"Get your free trial",{"href":811,"dataGaName":48,"dataGaLocation":812},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":503,"config":814},{"href":52,"dataGaName":53,"dataGaLocation":812},1777302630877]