CVE-2026-48828 in Airflow
Summary
by MITRE • 07/07/2026
The Bulk Variables API in Apache Airflow called the redactor without passing the variable's key, so the key-based `should_hide_value_for_key` check (which triggers on secret-suffixed key names like `*_password` / `*_token` / `*_secret`) could not fire for JSON-decodable variable values. An authenticated UI/API user with bulk Variable read permission could retrieve plaintext values from JSON variables whose key would otherwise trigger redaction. Affects deployments that store sensitive values in JSON-typed Airflow Variables under secret-suffixed key names. Users are advised to upgrade to `apache-airflow` 3.3.0 or later (the fix landed on `main` after 3.2.2; no 3.2.x backport).
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Analysis
by VulDB Data Team • 07/07/2026
The vulnerability resides within the Bulk Variables API implementation of Apache Airflow, specifically in how it handles variable redaction processes during JSON decoding operations. This flaw represents a critical security oversight that undermines the platform's built-in protections for sensitive data management. The core issue stems from the redactor function being invoked without proper parameter passing that includes the variable's key information, which is essential for triggering the automated redaction mechanisms that protect secret-suffixed keys. When variables are stored with names ending in password, token, or secret suffixes, Airflow's security framework should automatically redact their values to prevent unauthorized disclosure.
The technical flaw manifests when authenticated users with bulk Variable read permissions access JSON-typed variables through the API interface. The system's variable redaction logic depends on key-based validation through the `should_hide_value_for_key` function, which operates on the variable name to determine whether its contents should be masked or displayed in plaintext. However, due to the missing key parameter during the redactor invocation, this validation mechanism fails entirely for JSON-decodable values, allowing attackers to extract sensitive information that would normally be protected. This behavior creates a significant information disclosure vulnerability that bypasses Airflow's intended security controls.
The operational impact of this vulnerability extends beyond simple data exposure, as it fundamentally undermines the trust model for sensitive configuration management within Apache Airflow deployments. Organizations relying on JSON-typed variables for storing credentials, API keys, or other confidential information face potential compromise when these variables are named with secret-suffixed identifiers. The vulnerability affects all deployments where sensitive values are stored in JSON format under key names that match the secret detection patterns, creating a wide attack surface for authenticated threat actors who can leverage their existing permissions to extract plaintext credentials. This issue particularly impacts environments where administrators store database passwords, API tokens, or cryptographic keys within Airflow's variable system.
Organizations should immediately implement the recommended mitigation by upgrading to Apache Airflow version 3.3.0 or later, as this release contains the necessary fix for the redactor parameter handling. The vulnerability was resolved in the main development branch after version 3.2.2, with no backport provided for the 3.2.x series, making the upgrade path critical for maintaining security posture. Security teams should also conduct immediate audits of existing JSON variables to identify any secret-suffixed keys that may have been exposed through this vulnerability. The fix addresses the underlying CWE-200 (Information Exposure) weakness by ensuring proper parameter validation and enforcement of access controls within the variable management system, aligning with ATT&CK technique T1552.001 (Credentials in Files) by preventing unauthorized retrieval of sensitive data through API interfaces.
This vulnerability demonstrates the importance of proper input validation and parameter handling in security-critical systems, particularly when dealing with sensitive configuration data. The flaw highlights how seemingly small implementation details in API design can create significant security gaps that bypass established protection mechanisms. Organizations should implement additional monitoring for unusual access patterns to variable endpoints and consider implementing more granular access controls for sensitive data operations. The incident underscores the necessity of comprehensive testing for parameter injection vulnerabilities and proper validation of security control enforcement within application programming interfaces.