Apache Airflow has DAG Author Code Execution possibility in airflow-scheduler

The impact of CVE-2024-39877 is severe. A successful exploit allows an attacker, posing as an authenticated DAG author, to execute arbitrary code on the Airflow scheduler. This grants the attacker complete control over the scheduler's processes, potentially leading to data breaches, system compromise, and disruption of Airflow workflows. The attacker could steal sensitive data processed by Airflow, modify DAGs to execute malicious tasks, or even pivot to other systems within the network if the scheduler has access to them. This vulnerability is particularly concerning given Airflow's common use in data pipelines handling sensitive information.

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. 已保留2024-07-01
2. 发布日期2024-07-17
3. 修改日期2026-02-04
4. EPSS 更新日期2026-04-02

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 malicious crafting of docmd parameters. If an immediate upgrade is not feasible, consider implementing stricter input validation on the docmd parameter to sanitize potentially harmful code. While not a complete solution, this can reduce the attack surface. Review and audit existing DAGs for any suspicious code or unusual parameter usage. After upgrading, verify the fix by attempting to create a DAG with a crafted doc_md parameter and confirming that it is properly sanitized and does not result in code execution.