CVE-2023-5207 Overview
A critical privilege escalation vulnerability was discovered in GitLab CE and EE that allows authenticated attackers to execute arbitrary pipelines under the context of another user. This vulnerability affects all GitLab versions starting from 16.0 and represents a significant security risk for organizations using affected versions, as it enables unauthorized code execution and potential supply chain compromise.
Critical Impact
Authenticated attackers can execute arbitrary CI/CD pipelines as other users, potentially leading to code injection, secrets theft, and unauthorized deployment of malicious code through GitLab's pipeline infrastructure.
Affected Products
- GitLab Community Edition (CE) versions 16.0 prior to 16.2.8
- GitLab Community Edition (CE) versions 16.3 prior to 16.3.5
- GitLab Community Edition (CE) version 16.4.0 prior to 16.4.1
- GitLab Enterprise Edition (EE) versions 16.0 prior to 16.2.8
- GitLab Enterprise Edition (EE) versions 16.3 prior to 16.3.5
- GitLab Enterprise Edition (EE) version 16.4.0 prior to 16.4.1
Discovery Timeline
- September 30, 2023 - CVE-2023-5207 published to NVD
- November 21, 2024 - Last updated in NVD database
Technical Details for CVE-2023-5207
Vulnerability Analysis
This vulnerability is classified under CWE-250 (Execution with Unnecessary Privileges), indicating a flaw in GitLab's pipeline execution authorization mechanism. The vulnerability allows an authenticated user to trigger pipeline execution in a way that assumes the identity and permissions of another user. This is particularly dangerous in GitLab's CI/CD environment where pipelines often have access to deployment credentials, secrets, and production infrastructure.
The attack requires only low-privilege authenticated access to the GitLab instance, making it accessible to any valid user. The impact is severe as it affects the confidentiality, integrity, and availability of the system—attackers can read protected variables and secrets, modify repository content through pipeline jobs, and potentially disrupt services.
Root Cause
The root cause stems from improper authorization checks during pipeline execution context assignment. GitLab's pipeline scheduling and triggering mechanism failed to properly validate that the user initiating a pipeline execution had the appropriate permissions to execute under a specific user context. This allowed authenticated users to manipulate pipeline execution parameters to assume another user's identity.
Attack Vector
The attack is network-accessible and requires only basic authentication to the GitLab instance. An attacker with valid credentials can exploit improper pipeline execution context handling to execute arbitrary pipelines as another user. This could be leveraged to:
- Access protected environment variables and CI/CD secrets belonging to other users
- Execute deployment jobs to production environments with elevated privileges
- Modify or inject malicious code into repositories through pipeline-based commits
- Exfiltrate sensitive data accessible to the impersonated user
The vulnerability can be exploited through GitLab's web interface or API endpoints that handle pipeline creation and scheduling. For detailed technical information, refer to the GitLab Issue #425604 and the HackerOne Report #2174141.
Detection Methods for CVE-2023-5207
Indicators of Compromise
- Unusual pipeline executions attributed to users who did not initiate them
- Pipeline audit logs showing execution context mismatches between the triggering user and the executing user identity
- Unexpected access to protected variables or environment-specific secrets
- Anomalous deployment activity to production environments from unauthorized user accounts
Detection Strategies
- Monitor GitLab audit logs for pipeline execution events with mismatched user contexts
- Implement alerting for pipelines accessing protected variables outside normal patterns
- Review CI/CD job logs for signs of unauthorized secret access or data exfiltration
- Correlate pipeline execution timestamps with user authentication events to identify anomalies
Monitoring Recommendations
- Enable comprehensive audit logging for all CI/CD pipeline activities in GitLab
- Configure SIEM integration to aggregate and analyze GitLab audit events in real-time
- Implement behavioral analysis for pipeline execution patterns per user account
- Set up alerts for pipeline executions during unusual hours or from unexpected IP addresses
How to Mitigate CVE-2023-5207
Immediate Actions Required
- Upgrade GitLab CE/EE to version 16.2.8, 16.3.5, or 16.4.1 or later immediately
- Audit recent pipeline executions for signs of exploitation
- Review CI/CD variable access logs for unauthorized secret access
- Rotate any secrets or deployment credentials that may have been exposed
Patch Information
GitLab has released security patches addressing this vulnerability in the following versions:
| Branch | Patched Version |
|---|---|
| 16.2.x | 16.2.8 |
| 16.3.x | 16.3.5 |
| 16.4.x | 16.4.1 |
Organizations should upgrade to the patched version corresponding to their current branch. For additional details, see GitLab Issue #425857.
Workarounds
- Implement strict network segmentation to limit access to GitLab instances from trusted networks only
- Enable and enforce multi-factor authentication for all GitLab users to reduce unauthorized access risk
- Restrict pipeline triggering permissions to trusted users through protected branch and tag policies
- Consider temporarily disabling pipeline scheduling features until patches can be applied
- Implement additional monitoring and alerting for CI/CD activity during the remediation period
# Verify your GitLab version
gitlab-rake gitlab:env:info
# Check for available updates
apt-get update && apt-cache policy gitlab-ce
# or for Enterprise Edition
apt-get update && apt-cache policy gitlab-ee
# Apply security update (Debian/Ubuntu)
apt-get install gitlab-ce=16.4.1-ce.0
# or for Enterprise Edition
apt-get install gitlab-ee=16.4.1-ee.0
# Reconfigure GitLab after update
gitlab-ctl reconfigure
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.


