CVE-2020-11978: Apache Airflow RCE Vulnerability

CVE-2020-11978 Overview

A critical command injection vulnerability exists in Apache Airflow versions 1.10.10 and below. The vulnerability was discovered in one of the example DAGs (Directed Acyclic Graphs) shipped with Airflow, which allows any authenticated user to execute arbitrary commands as the user running the airflow worker or scheduler process. The specific impact depends on the executor configuration in use.

Organizations running Apache Airflow with the default example DAGs enabled are at significant risk, as this vulnerability provides a direct path to remote code execution. However, deployments that have disabled examples by setting load_examples=False in the configuration are not affected.

Critical Impact

Authenticated attackers can achieve remote code execution on the Airflow server, potentially compromising the entire workflow orchestration infrastructure and any connected systems or data pipelines.

Affected Products

• Apache Airflow versions 1.10.10 and below
• Apache Airflow installations with default example DAGs enabled (load_examples=True)
• Deployments using any executor type (worker or scheduler)

Discovery Timeline

• 2020-07-17 - CVE CVE-2020-11978 published to NVD
• 2025-10-23 - Last updated in NVD database

Technical Details for CVE-2020-11978

Vulnerability Analysis

This vulnerability is classified as CWE-78 (Improper Neutralization of Special Elements used in an OS Command), commonly known as OS Command Injection. The flaw resides in one of the example DAG files distributed with Apache Airflow installations.

The attack can be executed over the network by any authenticated user without requiring user interaction. The vulnerability allows complete compromise of confidentiality, integrity, and availability of the affected system. Successful exploitation enables attackers to run arbitrary shell commands with the same privileges as the Airflow process, potentially leading to full system compromise.

This vulnerability has been listed in the CISA Known Exploited Vulnerabilities Catalog, confirming active exploitation in the wild.

Root Cause

The root cause lies in improper input sanitization within an example DAG file. The vulnerable code accepts user-controlled input and passes it directly to a shell command without adequate validation or escaping. This allows an attacker to inject malicious commands that are then executed by the system.

Example DAGs are loaded by default in Apache Airflow installations, meaning out-of-the-box deployments are vulnerable unless administrators explicitly disable them through configuration.

Attack Vector

The attack is network-based and requires only low-privilege authenticated access to the Airflow web interface. An attacker can:

1. Authenticate to the Apache Airflow web interface with valid credentials
2. Interact with the vulnerable example DAG through the UI or API
3. Inject malicious commands through improperly sanitized input fields
4. The injected commands execute with the privileges of the Airflow worker or scheduler process

The vulnerability does not require any special privileges beyond basic authenticated access, making it particularly dangerous in multi-tenant environments or systems with multiple users.

Detection Methods for CVE-2020-11978

Indicators of Compromise

• Unexpected command execution patterns in Airflow worker or scheduler logs
• Unusual process spawning from Airflow service accounts
• Network connections originating from Airflow processes to unexpected destinations
• Modifications to DAG files or Airflow configuration outside of normal change windows
• Evidence of reconnaissance commands (whoami, id, uname) in execution logs

Detection Strategies

• Monitor Airflow logs for suspicious command patterns or shell metacharacters in task parameters
• Implement command-line auditing on systems running Airflow components
• Deploy file integrity monitoring on DAG directories and Airflow configuration files
• Use network detection to identify unusual outbound connections from Airflow infrastructure
• Correlate authentication events with DAG trigger activities for anomalous patterns

Monitoring Recommendations

• Enable verbose logging for Airflow workers and schedulers to capture execution details
• Configure SIEM rules to alert on command injection patterns in Airflow-related logs
• Implement behavioral analytics to detect abnormal command execution by Airflow processes
• Monitor for exploitation attempts using available exploit signatures from public sources

How to Mitigate CVE-2020-11978

Immediate Actions Required

• Upgrade Apache Airflow to a version newer than 1.10.10 immediately
• If immediate upgrade is not possible, disable example DAGs by setting load_examples=False in the Airflow configuration
• Review Airflow access controls and remove unnecessary user accounts
• Audit existing DAGs for similar command injection vulnerabilities
• Implement network segmentation to limit the blast radius of potential compromise

Patch Information

Apache has addressed this vulnerability in versions after 1.10.10. Organizations should upgrade to the latest stable release of Apache Airflow. Refer to the Apache Airflow User Discussion for official vendor guidance on remediation.

Additional technical details about the exploitation method are available on Packet Storm Security.

Workarounds

• Disable example DAGs by adding load_examples = False to the [core] section of your airflow.cfg file
• Delete example DAG files from the Airflow DAGs directory manually if configuration change is not immediately feasible
• Restrict network access to the Airflow web interface to trusted IP ranges only
• Implement additional authentication layers such as VPN or network-level access controls
• Enable Airflow RBAC (Role-Based Access Control) to limit which users can trigger DAGs

# Configuration example - Disable example DAGs in airflow.cfg
# Add or modify the following in your airflow.cfg file:

[core]
# Disable loading of example DAGs to mitigate CVE-2020-11978
load_examples = False

# After modifying the configuration, restart Airflow services:
# systemctl restart airflow-webserver
# systemctl restart airflow-scheduler
# systemctl restart airflow-worker