CVE-2025-68438 Overview
CVE-2025-68438 is an information disclosure vulnerability in Apache Airflow that allows sensitive data to be exposed in cleartext through the Rendered Templates UI. When rendered template fields in a DAG exceed the configured [core] max_templated_field_length limit, the serialization process uses a secrets masker instance that fails to include user-registered mask_secret() patterns. This results in secrets not being reliably masked before truncation and display, potentially exposing credentials, API keys, and other sensitive configuration values to unauthorized users with access to the Airflow web interface.
Critical Impact
Sensitive secrets and credentials may be exposed in cleartext through the Airflow web UI when template fields exceed configured length limits, potentially compromising connected systems and services.
Affected Products
- Apache Airflow versions prior to 3.1.6
Discovery Timeline
- 2026-01-16 - CVE CVE-2025-68438 published to NVD
- 2026-01-21 - Last updated in NVD database
Technical Details for CVE-2025-68438
Vulnerability Analysis
This vulnerability falls under CWE-200 (Exposure of Sensitive Information to an Unauthorized Actor). The flaw exists in how Apache Airflow handles the serialization and display of templated field values when they exceed the maximum configured length.
Apache Airflow uses Jinja templating to allow dynamic configuration of tasks and DAGs. When these templates contain sensitive values such as database credentials, API tokens, or other secrets, Airflow normally masks them using a secrets masker before rendering them in the UI. However, when template fields exceed the max_templated_field_length configuration parameter, the truncation and serialization process bypasses the proper secrets masking implementation.
The vulnerability is particularly concerning in multi-tenant environments or organizations where Airflow web UI access may be granted to users who should not have visibility into the underlying secrets used by DAG tasks.
Root Cause
The root cause lies in the serialization logic that handles oversized template fields. When template field content exceeds the [core] max_templated_field_length threshold, the system creates a secrets masker instance for displaying the truncated content. However, this masker instance does not incorporate patterns registered via the mask_secret() API, meaning custom secrets that users have explicitly configured for masking are not hidden during the truncation process. Only default masking patterns are applied, leaving user-defined sensitive values exposed in cleartext.
Attack Vector
An attacker with access to the Apache Airflow web interface can exploit this vulnerability through the following attack path:
- The attacker identifies or creates a DAG containing template fields with sensitive data that exceed the max_templated_field_length limit
- The attacker navigates to the Rendered Templates UI view for the target task
- The vulnerable serialization process truncates and displays the template content without proper secret masking
- Sensitive values registered via mask_secret() are visible in cleartext in the rendered output
- The attacker extracts exposed credentials, API keys, or other sensitive information
This attack requires network access to the Airflow web interface and appropriate authentication to view DAG details. No user interaction is required beyond normal navigation of the Airflow UI.
Detection Methods for CVE-2025-68438
Indicators of Compromise
- Unusual access patterns to the Rendered Templates view in Airflow access logs
- Multiple users accessing rendered template views for DAGs they don't typically interact with
- Exported or copied content from rendered template pages in browser activity logs
- Unexpected API calls to template rendering endpoints
Detection Strategies
- Monitor Airflow web server logs for frequent access to /rendered-templates or similar template rendering endpoints
- Implement audit logging for all access to the Rendered Templates UI view
- Review Airflow access logs for users viewing DAGs outside their normal operational scope
- Deploy application-layer monitoring to detect template field content being exfiltrated
Monitoring Recommendations
- Enable verbose access logging on the Airflow webserver component
- Configure alerting for bulk access to rendered template views
- Implement session monitoring for sensitive DAG access patterns
- Review and restrict user permissions to limit Rendered Templates UI access to only necessary personnel
How to Mitigate CVE-2025-68438
Immediate Actions Required
- Upgrade Apache Airflow to version 3.1.6 or later immediately
- Review access logs to identify potential exposure of sensitive data through the Rendered Templates UI
- Rotate any credentials or secrets that may have been exposed through vulnerable Airflow instances
- Restrict access to the Airflow web interface to essential personnel only until patching is complete
Patch Information
Apache has released version 3.1.6 which addresses this vulnerability by ensuring that user-registered mask_secret() patterns are properly included in the secrets masker instance used during template field serialization. Users should upgrade to this version or later to remediate the vulnerability.
For detailed patch information and upgrade instructions, see the Apache Mailing List Discussion and the Openwall OSS Security Post.
Workarounds
- Reduce the [core] max_templated_field_length configuration value to ensure template fields are less likely to be truncated
- Remove or minimize sensitive data in template fields where possible, using Airflow Connections or Variables instead
- Implement network-level access controls to restrict who can reach the Airflow web interface
- Enable Airflow's built-in RBAC to limit access to sensitive DAG views and rendered templates
# Configuration example - Increase max_templated_field_length to reduce truncation
# Edit airflow.cfg or set environment variable
export AIRFLOW__CORE__MAX_TEMPLATED_FIELD_LENGTH=10000
# Or in airflow.cfg:
# [core]
# max_templated_field_length = 10000
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
