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GitLab

Modernize Java with Cursor and GitLab Automate work item assignment with GitLab Duo GitLab Transcend Hackathon: What developers built on GitLab Orbit Turn multi-step software delivery into agentic flows you can trust Bring GitLab Duo Agent Platform to your terminal Forrester Consulting: GitLab Duo Agent Platform delivers 400% ROI GitLab 19.2 release notes | GitLab Docs When a version bump breaks your build, GitLab fixes it Green DevOps: Why carbon measurement belongs in your CI/CD pipeline GitLab Patch Release: 19.1.2, 19.0.4, 18.11.7 How we used AI agents to migrate GitLab rate limiting Keep your GitLab seats in check with restricted access GitLab Patch Release: 18.8.11 | GitLab Docs Claude Sonnet 5 on GitLab: More reliable, more efficient What Google Antigravity agents get full context with GitLab Orbit GitLab Patch Release: 19.1.1, 19.0.3, 18.11.6 GitLab 19.1 release notes | GitLab Docs AI Catalog updates for governance and operations One vulnerability view: From scanner coverage to AI governance GitLab named a Leader in the 2026 Gartner® Magic Quadrant™ for DevSecOps Platforms GitLab and Capgemini accelerate DevSecOps transformation Introducing the 2026 EMEA GitLab Partner Award winners GitLab Patch Release: 19.0.2, 18.11.5, 18.10.8 Introducing GitLab Orbit GitLab Flex: Commit once, reshape your seats and AI spend GitLab: Built for the agentic engineering era GitLab on Google Cloud: Fully managed, compliant, and AI-ready Shai-Hulud copycat campaign targets Python developers through PyPI typosquatting Mythos-class Claude Fable 5 arrives on GitLab Duo Agent Platform GitLab Patch Release: 19.0.1, 18.11.4, 18.10.7 Claude Opus 4.8 on GitLab: Complex agentic work, less disruption Agentic coding is only as good as its context GitLab Patch Release: 18.9.8, 18.8.10, 18.7.7, 18.6.8, 18.5.7 Full security scanner coverage of your codebase in minutes Reduce supply chain risk with SBOM-based dependency scanning Transform MRs from manual tasks to an automated workflow Track CI component usage across your organization Manage CI/CD credentials with GitLab Secrets Manager More AI models for GitLab Duo Agent Platform Self-Hosted GitLab 19.0 | GitLab Docs GitLab Dedicated for Government now GovRAMP-authorized Beyond BYOK: Why governance matters for AI agents Fix bugs with Codex and GitLab 5 ways to fix misleading vulnerability severities with policy Harden your pipeline perimeter for the era of AI-assisted coding GitLab Patch Release: 18.11.3, 18.10.6, 18.9.7 GitLab Act 2 Consolidate your GitLab stack with Gitaly on Kubernetes Limit token exposure with fine-grained PATs Automate deployment processes with GitLab Duo Agent Platform Claude Code and GitLab: Three workflows that ship 8 Agentic AI patterns reshaping team collaboration How to detect and prevent Contagious Interview IDE attacks Atlassian will train on your data: Opt out with GitLab Automate detection testing with GitLab CI/CD and Duo
GitLab Duo Security Review spots logic flaws scanners miss
Mark Settle · 2026-07-16 · via GitLab

Static scanners excel at catching vulnerabilities that fit a known pattern, like unsanitized query inputs, hardcoded secrets, and unsafe deserialization. They struggle against flaws in your application’s logic, where there is no pattern to match — only valid code doing the wrong thing for your domain. Undetected, these flaws surface late and cost more to fix.

Security Review Flow, now in public beta, scrutinizes code changes the way a security engineer would. It traces intent rather than matching signatures to catch logic flaws before they hit production. It's a major step toward uncovering dangerous flaws that scanners usually miss.

Where pattern-based scanners go blind

The most damaging application vulnerabilities often look correct line by line, but violate context the code doesn't contain, like your authorization model, data sensitivity rules, and intended workflows. Consider three of the most common vulnerability classes:

Access and authorization: Whether a user may read or change a resource is defined by your authorization model, not any language construct. Broken object level authorization (accessing another user's data by changing an ID) has topped the OWASP API Security Top 10 since 2019.

Data exposure: Serializing an object and returning it is ordinary, correct-looking code. Whether it leaks depends on which fields are sensitive and who receives them — facts about your domain, not your syntax.

Control flow and workflow: Business-logic and race-condition flaws occur when valid operations run in the wrong order, repeat unexpectedly, or get manipulated. Examples include checkout reachable without payment, a state re-entered under a race, or a parameter tweaked to change a price.

Catching these flaws has previously required manual security review, which is expensive to scale across every merge request (MR), or penetration testing and bug bounties, which arrive too late. The result is a growing gap between the pace of development and how quickly security expertise can be applied.

Bring security judgment to every MR

Security Review Flow, a foundational flow on GitLab Duo Agent Platform, closes that gap by reasoning about what your code is meant to do. It detects exactly the classes of flaws described above: Broken object level authorization and function level authorization, missing authorization on state-changing operations, information disclosure, mass assignment, business logic errors, and race conditions in stateful workflows.

It complements traditional scanners and human analysis rather than replacing them, and it reviews code at the point of change, when a fix is cheapest. GitLab's own application security team has used Security Review Flow across internal MRs throughout its development.

See Security Review Flow in action:

How it works

When your MR is ready, request a review from Duo Security Review, the same way you would from a person. It analyzes the diff in context: the original files, changed lines, MR discussion, and related code. Its reasoning is optimized for precision, and an independent validation pass examines each finding to filter out likely false positives.

Findings appear as diff threads on the relevant lines, along with a summary in an internal note. On public projects, they’re confined to the internal note, so security details aren’t exposed.

Each finding arrives with the context reviewers need:

  • Vulnerability type, with a CWE reference
  • Severity: critical, high, medium, or low
  • Tier: Tier 1 (Exploitable), Tier 2 (Logic Flaw), or Tier 3 (Design Issue)
  • A plain-language explanation of the issue
  • A suggested fix, when one is available

Severity determines the reviewer state: A critical or high finding sets it to Request changes, while medium or low findings result in Comment. The flow never approves, even when it finds nothing — a human always owns the final call.

From there, mention your organization’s Duo Security Review service account in a comment thread to ask a question, discuss remediation, or challenge a finding. Resolve each finding by applying the fix as a standard MR suggestion, dismissing it as a false positive, or accepting the risk. After committing your fixes, request a fresh review to check what changed.

Run your first Security Review Flow

Security Review Flow is in public beta for GitLab Ultimate customers. It is available on GitLab.com, GitLab Self-Managed, and GitLab Dedicated.

Learn how to get started in the Security Review Flow documentation.

You can get access to Security Review Flow with a free trial of GitLab Duo Agent Platform. Already a GitLab Ultimate subscriber? Turn on Duo Agent Platform and use the GitLab Credits included with your subscription.

Cost varies with the complexity of the diff and the model you select, so try it on a few MRs before running it broadly. Pricing may be updated at general availability.

Share what you find in our feature feedback issue, so your input shapes what we build.