Apache Airflow hat eine Möglichkeit zur Ausführung von DAG-Autorcode in airflow-scheduler.

The impact of CVE-2024-39877 is significant due to the potential for remote code execution within the Airflow scheduler. A successful exploit allows an attacker, posing as an authenticated DAG author, to inject malicious code that will be executed with the privileges of the scheduler process. This could lead to complete compromise of the Airflow environment, including data exfiltration, modification of workflows, and potentially lateral movement to other systems accessible from the Airflow infrastructure. The scheduler's elevated privileges amplify the blast radius of this vulnerability, making it a critical security concern.

Organizations heavily reliant on Apache Airflow for data orchestration and ETL processes are at significant risk. Specifically, teams with less stringent access controls for DAG authors, or those using Airflow in environments with sensitive data, are particularly vulnerable. Shared hosting environments where multiple users can create and deploy DAGs also increase the risk surface.

• python: Monitor Airflow logs for unusual process executions or errors related to DAG parsing.

import logging
logging.basicConfig(filename='airflow.log', level=logging.ERROR)
# Monitor for suspicious code execution attempts

• python: Check for modified DAG files with suspicious doc_md parameters.

# Example: Check for unusual characters in doc_md
with open('my_dag.py', 'r') as f:
    content = f.read()
    if 'doc_md=' in content:
        print('Potential vulnerability: doc_md parameter found')

• generic web: Examine Airflow web server access logs for requests containing unusual or encoded characters in the doc_md parameter. Look for patterns indicative of code injection attempts.

1. 2024-07-17Disclosure

   disclosure

1. Reserviert2024-07-01
2. Veröffentlicht2024-07-17
3. Geändert2026-02-04
4. EPSS aktualisiert2026-04-02

The primary mitigation for CVE-2024-39877 is to upgrade Apache Airflow to version 2.9.3 or later. This version includes a fix that prevents the vulnerable code execution. If upgrading immediately is not feasible, consider implementing stricter input validation on the docmd parameter to sanitize user-provided data. While not a complete solution, this can reduce the attack surface. Review existing DAGs for any suspicious code or unusual parameter usage. After upgrading, verify the fix by attempting to create a DAG with a crafted docmd parameter and confirming that the scheduler does not execute arbitrary code.