CVE-2026-31987: Apache Airflow Information Disclosure Flaw

CVE-2026-31987 Overview

CVE-2026-31987 is an Information Exposure vulnerability affecting Apache Airflow. JWT tokens used by tasks were inadvertently exposed in application logs, potentially allowing unauthorized UI users to impersonate DAG Authors. This credential leakage vulnerability (CWE-532: Insertion of Sensitive Information into Log File) represents a significant security risk for Airflow deployments where log access is shared among users with different privilege levels.

Critical Impact

Exposed JWT tokens in logs could enable privilege escalation, allowing UI users to act as DAG Authors and potentially manipulate workflow configurations and execution.

Affected Products

• Apache Airflow versions prior to 3.2.0

Discovery Timeline

• April 16, 2026 - CVE-2026-31987 published to NVD
• April 16, 2026 - Last updated in NVD database

Technical Details for CVE-2026-31987

Vulnerability Analysis

This vulnerability falls under CWE-532 (Insertion of Sensitive Information into Log File), which occurs when an application writes sensitive authentication credentials to log files. In the context of Apache Airflow, JWT tokens that authenticate and authorize task execution were being written to logs in plaintext format.

The exposure of these tokens creates a privilege escalation pathway where users with read access to logs could extract valid JWT tokens and use them to authenticate as DAG Authors. DAG Authors have elevated permissions within Airflow, including the ability to create, modify, and execute Directed Acyclic Graphs (DAGs) that define workflow orchestration.

Root Cause

The root cause of this vulnerability is improper handling of sensitive authentication data during the logging process. When tasks execute within Airflow, the JWT tokens used for authentication were not being properly sanitized or masked before being written to application logs. This violates the security principle that authentication credentials should never appear in logs, regardless of the log access controls in place.

Attack Vector

An attacker with read access to Airflow logs could exploit this vulnerability through the following approach:

1. Access Airflow application logs through the UI, file system, or log aggregation systems
2. Search log entries for exposed JWT token strings
3. Extract valid JWT tokens from task execution log entries
4. Use the extracted tokens to authenticate API requests as a DAG Author
5. Perform privileged operations such as modifying DAGs, triggering executions, or accessing sensitive workflow data

The attack requires the adversary to have legitimate access to view logs, making this primarily an insider threat or a risk in environments with overly permissive log access controls.

Detection Methods for CVE-2026-31987

Indicators of Compromise

• Unusual API requests authenticated with JWT tokens that don't correlate with normal user sessions
• Multiple requests using the same JWT token from different source IP addresses
• DAG modifications or executions performed by users who don't normally have DAG Author privileges
• Authentication patterns showing token reuse after extraction from log timestamps

Detection Strategies

• Implement log monitoring to detect patterns of log file access followed by elevated API operations
• Deploy user behavior analytics (UBA) to identify privilege escalation patterns in Airflow
• Monitor for JWT token reuse across different client sessions or IP addresses
• Audit log access patterns for users querying task execution logs extensively

Monitoring Recommendations

• Enable detailed audit logging for all DAG Author operations in Apache Airflow
• Configure alerts for JWT token usage patterns that deviate from normal authentication flows
• Implement log access monitoring to track which users are viewing task execution logs
• Review authentication logs for signs of token replay attacks

How to Mitigate CVE-2026-31987

Immediate Actions Required

• Upgrade Apache Airflow to version 3.2.0 or later which contains the security fix
• Rotate all existing JWT tokens and secrets used by Airflow tasks
• Review recent log access to identify potential token exposure
• Audit DAG Author activities for any unauthorized changes since deployment

Patch Information

Apache has released a fix for this vulnerability in Airflow version 3.2.0. The patch addresses the log exposure issue by implementing proper sanitization of JWT tokens before they are written to logs. Users can track the fix through GitHub Airflow Pull Request #62964. Additional context is available in GitHub Airflow Issue #62428 and GitHub Airflow Issue #62773.

For more information, refer to the Apache Mailing List Discussion and the Openwall OSS-Security Update.

Workarounds

• Restrict log access permissions to only essential personnel until the upgrade can be completed
• Implement log scrubbing or masking solutions to redact JWT tokens from existing logs
• Enable additional authentication factors for DAG Author operations to reduce token-only attack impact
• Consider implementing network segmentation to limit access to log storage systems

# Configuration example - Restrict log file permissions
chmod 600 /var/log/airflow/*.log
chown airflow:airflow /var/log/airflow/*.log

# Review and rotate Airflow secrets
airflow config get-value core fernet_key
# Generate new Fernet key and update configuration
python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())"