[{"data":1,"prerenderedAt":813},["ShallowReactive",2],{"/en-us/blog/memory-safe-vs-unsafe":3,"navigation-en-us":37,"banner-en-us":447,"footer-en-us":457,"blog-post-authors-en-us-Fernando Diaz":697,"blog-related-posts-en-us-memory-safe-vs-unsafe":711,"blog-promotions-en-us":751,"next-steps-en-us":803},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":12,"meta":26,"navigation":27,"path":28,"publishedDate":20,"seo":29,"stem":33,"tagSlugs":34,"__hash__":36},"blogPosts/en-us/blog/memory-safe-vs-unsafe.yml","Memory Safe Vs Unsafe",[7],"fernando-diaz",null,"security",{"slug":11,"featured":12,"template":13},"memory-safe-vs-unsafe",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How to secure memory-safe vs. manually managed languages","Learn how GitLab reduces source code risk using scanning, vulnerability management, and other key features.",[18],"Fernando Diaz","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749672878/Blog/Hero%20Images/securityscreen.jpg","2023-03-14","The National Security Agency (NSA) has published an executive summary showcasing the risk of using [manually managed languages](https://en.wikipedia.org/wiki/Manual_memory_management) over [memory-safe languages](https://en.wikipedia.org/wiki/Garbage_collection_%28computer_science%29) in application development. Manual memory management may introduce major bugs and security risks into your application if the memory is managed incorrectly.\n\nSecurity bugs introduced by manually managed languages can be catastrophic to the function of the application, as well as the information contained in the application. These bugs may cause performance slowdowns, application crashes, remote code execution, information leakage, and system failures.\n\nBugs that may be introduced include the following:\n\n* [Memory leak](https://en.wikipedia.org/wiki/Memory_leak): Memory no longer being used is not released, which reduces the amount of available memory.\n* [Buffer overflow](https://en.wikipedia.org/wiki/Buffer_overflow): Overwriting of memory locations adjacent to a buffer's boundary.\n* [Segmentation fault](https://en.wikipedia.org/wiki/Segmentation_fault): An application tries to access a restricted piece of memory.\n* [Wild pointers](https://en.wikipedia.org/wiki/Dangling_pointer): Pointer points to memory which has been deallocated.\n* [Undefined behavior](https://en.wikipedia.org/wiki/Undefined_behavior): An application with unpredictable behavior.\n\nTo provide some insight on the prevalence of risk introduced by manually managed languages, Microsoft revealed that within the span of 12 years, [70% of their vulnerabilities](https://github.com/Microsoft/MSRC-Security-Research/blob/master/presentations/2019_02_BlueHatIL/2019_01%20-%20BlueHatIL%20-%20Trends%2C%20challenge%2C%20and%20shifts%20in%20software%20vulnerability%20mitigation.pdf) were due to mismanagement of memory.\n\nGoogle reported that there was a [similar percentage of vulnerabilities](https://security.googleblog.com/2021/09/an-update-on-memory-safety-in-chrome.html) introduced by memory safety violations within the Chrome browser.\n\nAll these vulnerabilities can be exploited by malicious actors who may compromise a device, potentially leading to a compromise of a larger network infrastructure. With this large risk presented by mismanaged memory, the NSA advises organizations to consider using memory-safe languages wherever possible and providing mechanisms to harden applications built with manually managed languages.\n\n## Memory-safe languages vs. manually managed languages\n\nA 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. These languages include **Python**, **Java**, and **Go**, to name a few.\n\nIn contrast, manually managed languages provide a developer with full control over system memory (with some exceptions). The most popular manually managed languages are **C** and **C++**.\n\nEach language type has a purpose and use case. There are times when a memory-safe language is recommended, but there are also times when it may not suit the application requirements.\n\nBelow is a list of some pros and cons of each language type:\n\n| Language type    | Pros                                                                       | Cons                                                             |\n| ---------------- | -------------------------------------------------------------------------- | ---------------------------------------------------------------- |\n| Memory safe      | Memory management abstracted from developer, reduced risk of memory errors | Reduced efficiency/performance, unpredictable garbage collection |\n| Manually managed | Enhanced efficiency/performance, no garbage collection overhead            | Prone to memory-related failures                                 |\n\nManually managed languages provide the developer with more power but also introduce a greater amount of risk, so they should only be used where required.\n\n## Memory 'unsafe' language security scanning\n\nAlthough many organizations are promoting the use of memory-safe languages vs. manually managed ones, it is unrealistic to remove manually managed languages from a developer's toolbox. Therefore, developers must get ahead of all the bugs and vulnerabilities that may be introduced. This can be done by scanning application source code.\n\nGitLab supports various scanners for memory-unsafe languages. Below you can see the scanners used for C and C++:\n\n| Language | Scanners                                                                                               |\n| -------- | ------------------------------------------------------------------------------------------------------ |\n| C        | [Semgrep with GitLab-managed rules](https://gitlab.com/gitlab-org/security-products/analyzers/semgrep) |\n| C++      | [Flawfinder](https://gitlab.com/gitlab-org/security-products/analyzers/flawfinder)                     |\n\nNow let's take a look at how [GitLab's static application security testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/) allows us to find and resolve vulnerabilities.\n\nBelow is an application which doesn't crash but may generate unexpected behavior:\n\n```c\n#include \u003Cstdio.h>\n\nint main()\n{  \n  char msg[5] = \"Hello\";\n\n  /* Add exclamation, to a position that doesn't exist */\n  msg[8] = '!';\n\n  /* print each letter 1 by 1 */\n  /* Notice we are going further than the length of the array */\n  int i;\n  for (i = 0; i \u003C 10; ++i)\n  {\n    printf(\"%i: %c \\n\", i, msg[i]);\n  }\n\n  return 0;\n}\n```\n\nWhen running the GitLab SAST scanner, the vulnerability is detected and a solution is provided:\n\n![GitLab SAST scanner results](https://about.gitlab.com/images/blogimages/memory-safe-vs-manually-managed/CWE_120.png)\n\nIt shows you need to **perform bounds checking, use functions that limit length**, or **ensure that the size is larger than the maximum possible length.** You can also see the [CWE](https://cwe.mitre.org/data/definitions/120.html) for more information on how the system may be impacted.\n\nNote that vulnerabilities are actionable. These actions include the ability to dismiss a vulnerability, add additional information for the security team to review, or create a confidential issue for review.\n\nThese scanners allow [DevSecOps](/topics/devsecops/) teams to resolve security issues before code makes it into production and safeguard their applications from memory issues. Note that not all memory issues are easily detected due to the nature of manual memory management.\n\nTherefore, it is also important to add unit tests, fuzzing, and run checks using GitLab CI to further ensure the reliability and security of your application.\n\nThe following applications contain examples of creating a GitLab pipeline for C applications:\n\n* [General Build, SAST, and Run](https://gitlab.com/tech-marketing/devsecops/initech/other/cul8r)\n* [Coverage-based fuzzing](https://gitlab.com/gitlab-org/security-products/demos/coverage-fuzzing/c-cpp-fuzzing-example)\n\n## Memory-safe language security scanning\n\nAs more developers move to memory-safe languages, it is important that the tools used to prevent vulnerabilities support these languages as well. GitLab provides a rich feature set for securing application source code, especially for memory-safe languages.\n\nBelow is a table of some of the popular languages GitLab supports. To see the full list, visit the [GitLab SAST Language/Framework Support](https://docs.gitlab.com/ee/user/application_security/sast/#supported-languages-and-frameworks) page.\n\n| Language   | Scanners                                                                                                                                                                                                                                                                                              |\n| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Python     | [Semgrep with GitLab-managed rules](https://gitlab.com/gitlab-org/security-products/analyzers/semgrep), [Bandit](https://gitlab.com/gitlab-org/security-products/analyzers/bandit)                                                                                                                    |\n| Go         | [Semgrep with GitLab-managed rules](https://gitlab.com/gitlab-org/security-products/analyzers/semgrep), [GoSec](https://gitlab.com/gitlab-org/security-products/analyzers/gosec)                                                                                                                      |\n| Java       | [Semgrep with GitLab-managed rules](https://gitlab.com/gitlab-org/security-products/analyzers/semgrep), [SpotBugs with the find-sec-bugs plugin](https://gitlab.com/gitlab-org/security-products/analyzers/spotbugs), [MobSF (beta)](https://gitlab.com/gitlab-org/security-products/analyzers/mobsf) |\n| JavaScript | [Semgrep with GitLab-managed rules](https://gitlab.com/gitlab-org/security-products/analyzers/semgrep), [ESLint security plugin](https://gitlab.com/gitlab-org/security-products/analyzers/eslint)                                                                                                    |\n| Ruby       | [Brakeman](https://gitlab.com/gitlab-org/security-products/analyzers/brakeman)                                                                                                                                                                                                                        |\n\nGitLab uses a mix of open source tools developed in-house as well as commonly used tools within the open source community.\n\nIt is important to note that GitLab's security research team creates custom rules to better reduce false positives and enhance the number of vulnerabilities found.\n\nHere are some Python functions which can be exploited to obtain data via [SQL injection](https://owasp.org/www-community/attacks/SQL_Injection):\n\n```python\ndef select_note_by_id(conn, id=None, admin=False):\n   query = \"SELECT id, data FROM notes WHERE secret IS FALSE\"\n   cur = conn.cursor()\n\n   # Admin doesn't have search by id function, since only used in the UI\n   if admin:\n       query = \"SELECT id, data, ipaddress, hostname, secret FROM notes\"\n\n   if id:\n       if admin:\n           query = query + \" WHERE id = %s\" % id\n       else:\n           # NOTE: Vulnerable to SQL injection, can get secret notes\n           # by adding 'OR 1=1', since not parameterized\n           query = query + \" AND id = %s\" % id\n\n   try:\n       cur.execute(query)\n   except Exception as e:\n       note.logger.error(\"Error: cannot select note by id - %s\" % e)\n\n   allItems = cur.fetchall()\n   conn.close()\n\n   if len(allItems) == 0:\n       return []\n\n   return allItems\n```\n\nWhen running the GitLab SAST scanner, you can see the SQL injection vulnerability is detected. A solution is provided with the line of code affected as well as identifiers that provide more information on how the [CWE](https://cwe.mitre.org/data/definitions/89.html) can affect your system.\n\n![SQL Injection and solution](https://about.gitlab.com/images/blogimages/memory-safe-vs-manually-managed/CWE_89.png)\n\nNotice that there is also training to enable developers to understand the vulnerability and how it can be exploited, making them more security-aware.\n\n## Other application attack vectors\n\nUsing a memory-safe language along with a SAST scanner reduces vulnerability risk, but there are more attack vectors to consider, including configurations, infrastructure, and dependencies. This is why it is important to scan all aspects of your application.\n\nGitLab offers the following scanners to help you achieve full coverage:\n\n| Scanner type                                                                                                              | Description                                                                                                                                                                                      |\n| ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| [Dynamic application security testing (DAST)](https://docs.gitlab.com/ee/user/application_security/dast/)                 | Examines applications for vulnerabilities like these in deployed environments.                                                                                                                   |\n| [Infrastructure as code (IaC) scanning](https://docs.gitlab.com/ee/user/application_security/iac_scanning/)               | Scans your IaC (Terraform, Ansible, AWS CloudFormation, Kubernetes, etc.) configuration files for known vulnerabilities.                                                                         |\n| [Dependency scanning](https://docs.gitlab.com/ee/user/application_security/dependency_scanning/)                          | Finds security vulnerabilities in your software dependencies.                                                                                                                                    |\n| [Container scanning](https://docs.gitlab.com/ee/user/application_security/container_scanning/)                            | Scans your application's container images for known vulnerabilities.                                                                                                                             |\n| [License scanning - CycloneDX](https://docs.gitlab.com/ee/user/compliance/license_scanning_of_cyclonedx_files/index.html) | Capable of parsing and identifying over 500 different types of licenses and can extract license information from packages that are dual-licensed or have multiple different licenses that apply. |\n| [Secret detection](https://docs.gitlab.com/ee/user/application_security/secret_detection/)                                | Scans your repository for secrets.                                                                                                                                                               |\n| [Coverage-guided fuzzing](https://docs.gitlab.com/ee/user/application_security/coverage_fuzzing/)                         | Sends random inputs to an instrumented version of your application in an effort to cause unexpected behavior.                                                                                    |\n| [Web API fuzzing](https://docs.gitlab.com/ee/user/application_security/api_fuzzing/)                                      | Sets operation parameters to unexpected values in an effort to cause unexpected behavior and errors in the API backend.                                                                          |\n\nAside from full scanner coverage, it’s important to add guardrails to prevent vulnerable code from compromising a production environment. This can be done by requiring approval from the security team for merging any code with vulnerabilities using [GitLab policies](https://docs.gitlab.com/ee/user/application_security/policies/).\n\nTo get started using these tools and more, check out the [GitLab Application Security](https://docs.gitlab.com/ee/user/application_security/) page.\n\nIt's as simple as signing up for GitLab Ultimate and adding some templates to your `.gitlab-ci.yml`.\n\n## Managing vulnerabilities of all types\n\nAlthough we can find and address vulnerabilities before they make it into production, it is not possible to eliminate all risk. This is why it is important to be able to assess the security posture of your project or group of projects.\n\nFor this, GitLab provides [Vulnerability Reports](https://docs.gitlab.com/ee/user/application_security/vulnerability_report/), which allow you to manage and triage vulnerabilities within the main branch of the application. You can sort through all the vulnerabilities for a project or group of projects using a variety of criteria.\n\n![Vulnerability report screenshot](https://about.gitlab.com/images/blogimages/memory-safe-vs-manually-managed/vulnerability_report.png)\n\nClicking on a vulnerability sends you to its [Vulnerability Page](https://docs.gitlab.com/ee/user/application_security/vulnerabilities/).\n\nThere you can review details on the vulnerability, manage its status, collaborate with other members of the security team, as well as create confidential issues to assign to developers.\n\n![Vulnerability Page](https://about.gitlab.com/images/blogimages/memory-safe-vs-manually-managed/vulnerability_page.png)\n\n---\n\nThanks for reading! To learn more about available Security features, check out GitLab's [application security documentation](https://docs.gitlab.com/ee/user/application_security/) and get started securing your application today. You can also sign up for a [free GitLab Ultimate trial](https://about.gitlab.com/free-trial/) and test the [Simple Notes Application](https://gitlab.com/tech-marketing/devsecops/initech/simple-notes), which contains a [full tutorial](https://tech-marketing.gitlab.io/devsecops/initech/simple-notes/) on getting started with implementing and using many of GitLab's security features.\n\n## References\n\n* [The Federal Government is Moving on Memory Safety for Cybersecurity](https://www.nextgov.com/cybersecurity/2022/12/federal-government-moving-memory-safety-cybersecurity/381275/)\n* [Future of Memory Safety - Challenges and Recommendations](https://advocacy.consumerreports.org/wp-content/uploads/2023/01/Memory-Safety-Convening-Report-1-1.pdf)\n* [NSA Software Memory Safety Report](https://media.defense.gov/2022/Nov/10/2003112742/-1/-1/0/CSI_SOFTWARE_MEMORY_SAFETY.PDF)\n* [Memory Safety Wiki](https://en.wikipedia.org/wiki/Memory_safety)\n* [Manual Memory Management Wiki](https://en.wikipedia.org/wiki/Manual_memory_management)\n* [Unsafe Languages - University of Washington CS Lecture Notes](https://courses.cs.washington.edu/courses/cse341/04wi/lectures/26-unsafe-languages.html)\n* [GitLab SAST](https://docs.gitlab.com/ee/user/application_security/sast/)\n* [GitLab Application Security](https://docs.gitlab.com/ee/user/application_security/)\n* [GitLab Vulnerability Reports](https://docs.gitlab.com/ee/user/application_security/vulnerability_report/)\n\n*Cover image by [Mohammad Rahmani](https://unsplash.com/@afgprogrammer?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on 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your pipeline for AI-discovered zero-days","AI is finding vulnerabilities faster than teams can patch. Learn how pipeline enforcement, automated triage, and AI remediation close the gap.",[717],"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.",[721,9,532],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":27,"template":13,"slug":724},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":726,"config":737},{"title":727,"description":728,"authors":729,"heroImage":731,"date":732,"category":9,"tags":733,"body":736},"Manage vulnerability noise at scale with auto-dismiss policies","Learn how to cut through scanner noise and focus on the vulnerabilities that matter most with GitLab security, including use cases and templates.",[730],"Grant Hickman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1774375772/kpaaaiqhokevxxeoxvu0.png","2026-03-25",[9,24,23,734,735],"features","product","Security scanners are essential, but not every finding requires action. Test code, vendored dependencies, generated files, and known false positives create noise that buries the vulnerabilities that actually matter. Security teams waste hours manually dismissing the same irrelevant findings across projects and pipelines. They experience slower triage, alert fatigue, and developer friction that undermines adoption of security scanning itself.\n\nGitLab's auto-dismiss vulnerability policies let you codify your triage decisions once and apply them automatically on every default-branch pipeline. Define criteria based on file path, directory, or vulnerability identifier (CVE, CWE), choose a dismissal reason, and let GitLab handle the rest.\n\n## Why auto-dismiss?\nAuto-dismiss vulnerability policies enable security teams to:\n- **Eliminate triage noise**: Automatically dismiss findings in test code, vendored dependencies, and generated files.\n- **Enforce decisions at scale**: Apply policies centrally to dismiss known false positives across your entire organization.\n- **Maintain audit transparency**: Every auto-dismissed finding includes a documented reason and links back to the policy that triggered it.\n- **Preserve the record**: Unlike scanner exclusions, dismissed vulnerabilities remain in your report, so you can revisit decisions if conditions change.\n\n## How auto-dismiss policies work\n\n1. **Define your policy** in a vulnerability management policy YAML file. Specify match criteria (file path, directory, or identifier) and a dismissal reason.\n\n2. **Merge and activate.** Create the policy via **Secure > Policies > New  policy > Vulnerability management policy**. Merge the MR to enable it.\n3. **Run your pipeline.** On every default-branch pipeline, matching vulnerabilities are automatically set to \"Dismissed\" with the specified reason. Up to 1,000 vulnerabilities are processed per run.\n4. **Measure the impact.** Filter your vulnerability report by status \"Dismissed\" to see exactly what was cleaned up and validate that the right findings are being handled.\n\n## Use cases with ready-to-use configurations\n\nEach example below includes a policy configuration you can copy, customize, and apply immediately.\n\n### 1. Dismiss test code vulnerabilities\n\nSAST and dependency scanners flag hardcoded credentials, insecure fixtures, and dev-only dependencies in test directories. These are not production risks.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss test code vulnerabilities\"\n    description: \"Auto-dismiss findings in test directories\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"test/**/*\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"tests/**/*\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"spec/**/*\"\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"__tests__/*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: used_in_tests\n\n```\n\n### 2. Dismiss vendored and third-party code\n\nVulnerabilities in `vendor/`, `third_party/`, or checked-in `node_modules` are managed upstream and not actionable for your team.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss vendored dependency findings\"\n    description: \"Findings in vendored code are managed upstream\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"vendor/*\"\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"third_party/*\"\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"vendored/*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: not_applicable\n\n```\n\n### 3. Dismiss known false positive CVEs\n\nCertain CVEs are repeatedly flagged but don't apply to your usage context. Teams dismiss these manually every time they appear. Replace the example CVEs below with your own.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss known false positive CVEs\"\n    description: \"CVEs confirmed as false positives for our environment\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2023-44487\"\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2024-29041\"\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2023-26136\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: false_positive\n\n```\n\n### 4. Dismiss generated and auto-created code\n\nProtobuf, gRPC, OpenAPI generators, and ORM scaffolding tools produce files with flagged patterns that cannot be patched by your team.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss generated code findings\"\n    description: \"Generated files are not authored by us\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"generated/*\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"**/*.pb.go\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"**/*.generated.*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: not_applicable\n\n```\n\n### 5. Dismiss infrastructure-mitigated vulnerabilities\n\nVulnerability classes like XSS (CWE-79) or SQL injection (CWE-89) that are already addressed by WAF rules or runtime protection. Only use this when mitigating controls are verified and consistently enforced.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss CWEs mitigated by WAF\"\n    description: \"XSS and SQLi mitigated by WAF rules\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CWE-79\"\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CWE-89\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: mitigating_control\n\n```\n\n### 6. Dismiss CVE families across your organization\n\nA wave of related CVEs for a widely-used library your team has assessed? Apply at the group level to dismiss them across dozens of projects. The wildcard pattern (e.g., `CVE-2021-44*`) matches all CVEs with that prefix.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Accept risk for log4j CVE family\"\n    description: \"Log4j CVEs mitigated by version pinning and WAF\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2021-44*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: acceptable_risk\n\n```\n\n## Quick reference\n\n| Parameter | Details |\n|-----------|---------|\n| **Criteria types** | `file_path` (glob patterns, e.g., `test/**/*`), `directory` (e.g., `vendor/*`), `identifier` (CVE/CWE with wildcards, e.g., `CVE-2023-*`) |\n| **Dismissal reasons** | `acceptable_risk`, `false_positive`, `mitigating_control`, `used_in_tests`, `not_applicable` |\n| **Criteria logic** | Multiple criteria within a rule = AND (must match all). Multiple rules within a policy = OR (match any). |\n| **Limits** | 3 criteria per rule, 5 rules per policy, 5 policies per security policy project. Vulnerabilty management policy actions process 1000 vulnerabilities per pipeline run in the target project, until all matching vulnerabilities are processed. |\n| **Affected statuses** | Needs triage, Confirmed |\n| **Scope** | Project-level or group-level (group-level applies across all projects) |\n\n## Getting started\nHere's how to get started with auto-dismiss policies:\n\n1. **Identify the noise.** Open your vulnerability report and sort by \"Needs triage.\" Look for patterns: test files, vendored code, the same CVE across projects.\n\n2. **Pick a scenario.** Start with whichever use case above accounts for the most findings.\n\n3. **Record your baseline.** Note the number of \"Needs triage\" vulnerabilities before creating a policy.\n\n4. **Create and enable.** Navigate to **Secure > Policies > New policy > Vulnerability management policy**. Paste the configuration from the use case above, then merge the MR.\n\n5. **Validate results.** After the next default-branch pipeline, filter by status \"Dismissed\" to confirm the right findings were handled.\n\nFor full configuration details, see the [vulnerability management policy documentation](https://docs.gitlab.com/user/application_security/policies/vulnerability_management_policy/#auto-dismiss-policies).\n\n> Ready to take control of vulnerability noise? [Start a free GitLab Ultimate trial](https://about.gitlab.com/free-trial/) and configure your first auto-dismiss policy today.\n",{"slug":738,"featured":27,"template":13},"auto-dismiss-vulnerability-management-policy",{"content":740,"config":749},{"title":741,"description":742,"authors":743,"heroImage":745,"date":746,"body":747,"category":9,"tags":748},"GitLab 18.10 brings AI-native triage and remediation ","Learn about GitLab Duo Agent Platform capabilities that cut noise, surface real vulnerabilities, and turn findings into proposed fixes.",[744],"Alisa Ho","https://res.cloudinary.com/about-gitlab-com/image/upload/v1773843921/rm35fx4gylrsu9alf2fx.png","2026-03-19","GitLab 18.10 introduces new AI-powered security capabilities focused on improving the quality and speed of vulnerability management. Together, these features can help reduce the time developers spend investigating false positives and bring automated remediation directly into their workflow, so they can fix vulnerabilities without needing to be security experts.\n\nHere is what’s new:\n\n* [**Static Application Security Testing (SAST) false positive detection**](https://docs.gitlab.com/user/application_security/vulnerabilities/false_positive_detection/) **is now generally available.** This flow uses an LLM for agentic reasoning to determine the likelihood that a vulnerability is a false positive or not, so security and development teams can focus on remediating critical vulnerabilities first.  \n* [**Agentic SAST vulnerability resolution**](https://docs.gitlab.com/user/application_security/vulnerabilities/agentic_vulnerability_resolution/) **is now in beta.** Agentic SAST vulnerability resolution automatically creates a merge request with a proposed fix for verified SAST vulnerabilities, which can shorten time to remediation and reduce the need for deep security expertise.  \n* [**Secret false positive detection**](https://docs.gitlab.com/user/application_security/vulnerabilities/secret_false_positive_detection/) **is now in beta.** This flow brings the same AI-powered noise reduction to secret detection, flagging dummy and test secrets to save review effort.\n\nThese flows are available to GitLab Ultimate customers using GitLab Duo Agent Platform. \n\n## Cut triage time with SAST false positive detection\n\nTraditional SAST scanners flag every suspicious code pattern they find, regardless of whether code paths are reachable or frameworks already handle the risk. Without runtime context, they cannot distinguish a real vulnerability from safe code that just looks dangerous.\n\nThis means developers could spend hours investigating findings that turn out to be false positives. Over time, that can erode confidence in the report and slow down the teams responsible for fixing real risks.\n\nAfter each SAST scan, GitLab Duo Agent Platform automatically analyzes new critical and high severity findings and attaches:\n\n* A confidence score indicating how likely the finding is to be a false positive  \n* An AI-generated explanation describing the reasoning  \n* A visual badge that makes “Likely false positive” versus “Likely real” easy to scan in the UI\n\nThese findings appear in the [Vulnerability Report](https://docs.gitlab.com/user/application_security/vulnerability_report/), as shown below. You can filter the report to focus on findings marked as “Not false positive” so teams can spend their time addressing real vulnerabilities instead of sifting through noise.\n\n![Vulnerability report](https://res.cloudinary.com/about-gitlab-com/image/upload/v1773844787/i0eod01p7gawflllkgsr.png)\n\n\nGitLab Duo Agent Platform's assessment is a recommendation. You stay in control of every false positive to determine if it is valid, and you can audit the agent's reasoning at any time to build confidence in the model. \n\n\n## Turn vulnerabilities into automated fixes\n\nKnowing that a vulnerability is real is only half the work.  Remediation still requires understanding the code path, writing a safe patch, and making sure nothing else breaks.\n\nIf the vulnerability is identified as likely not be a false positive by the SAST false positive detection flow, the Agentic SAST vulnerability resolution flow automatically:\n\n1. Reads the vulnerable code and surrounding context from your repository  \n2. Generates high-quality proposed fixes  \n3. Validates fixes through automated testing   \n4. Opens a merge request with a proposed fix that includes:  \n   * Concrete code changes  \n   * A confidence score  \n   * An explanation of what changed and why\n\nIn this demo, you’ll see how GitLab can automatically take a SAST vulnerability all the way from detection to a ready-to-review merge request. Watch how the agent reads the code, generates and validates a fix, and opens an MR with clear, explainable changes so developers can remediate faster without being security experts.\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1174573325?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"GitLab 18.10 AI SAST False Positive Auto Remediation\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\nAs with any AI-generated suggestion, you should review the proposed merge request carefully before merging.\n\n## Surface real secrets\n\nSecret detection is only useful if teams trust the results. When reports are full of test credentials, placeholder values, and example tokens, developers may waste time reviewing noise instead of fixing real exposures. That can slow remediation and decrease confidence in the scan.\n\nSecret false positive detection helps teams focus on the secrets that matter so they can reduce risk faster. When it runs on the default branch, it will automatically:\n\n1. Analyze each finding to spot likely test credentials, example values, and dummy secrets  \n2. Assign a confidence score for whether the finding is a real risk or a likely false positive  \n3. Generate an explanation for why the secret is being treated as real or noise  \n4. Add a badge in the Vulnerability Report so developers can see the status at a glance\n\nDevelopers can also trigger this analysis manually from the Vulnerability Report by selecting **“Check for false positive”** on any secret detection finding, helping them clear out findings that do not pose risk and focus on real secrets sooner.\n\n## Try AI-powered security today\n\nGitLab 18.10 introduces capabilities that cover the full vulnerability workflow, from cutting false positive noise in SAST and secret detection to automatically generating merge requests with proposed fixes.\n\nTo see how AI-powered security can help cut review time and turn findings into ready-to-merge fixes, [start a free trial of GitLab Duo Agent Platform today](https://about.gitlab.com/gitlab-duo-agent-platform/?utm_medium=blog&utm_source=blog&utm_campaign=eg_global_x_x_security_en_).",[735,9,734],{"featured":12,"template":13,"slug":750},"gitlab-18-10-brings-ai-native-triage-and-remediation",{"promotions":752},[753,767,778,789],{"id":754,"categories":755,"header":757,"text":758,"button":759,"image":764},"ai-modernization",[756],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":760,"config":761},"Get your AI maturity score",{"href":762,"dataGaName":763,"dataGaLocation":241},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":765},{"src":766},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":768,"categories":769,"header":770,"text":758,"button":771,"image":775},"devops-modernization",[735,35],"Are you just managing tools or shipping innovation?",{"text":772,"config":773},"Get your DevOps maturity score",{"href":774,"dataGaName":763,"dataGaLocation":241},"/assessments/devops-modernization-assessment/",{"config":776},{"src":777},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":779,"categories":780,"header":781,"text":758,"button":782,"image":786},"security-modernization",[9],"Are you trading speed for security?",{"text":783,"config":784},"Get your security maturity score",{"href":785,"dataGaName":763,"dataGaLocation":241},"/assessments/security-modernization-assessment/",{"config":787},{"src":788},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":790,"paths":791,"header":794,"text":795,"button":796,"image":801},"github-azure-migration",[792,793],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":797,"config":798},"See how GitLab compares to GitHub",{"href":799,"dataGaName":800,"dataGaLocation":241},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":802},{"src":777},{"header":804,"blurb":805,"button":806,"secondaryButton":811},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":807,"config":808},"Get your free trial",{"href":809,"dataGaName":48,"dataGaLocation":810},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":503,"config":812},{"href":52,"dataGaName":53,"dataGaLocation":810},1777302616937]