| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| Before Airflow 3.2.0, it was unclear that secure Airflow deployments require the Deployment Manager to take appropriate actions and pay attention to security details and security model of Airflow. Some assumptions the Deployment Manager could make were not clear or explicit enough, even though Airflow's intentions and security model of Airflow did not suggest different assumptions. The overall security model [1], workload isolation [2], and JWT authentication details [3] are now described in more detail. Users concerned with role isolation and following the Airflow security model of Airflow are advised to upgrade to Airflow 3.2, where several security improvements have been implemented. They should also read and follow the relevant documents to make sure that their deployment is secure enough. It also clarifies that the Deployment Manager is ultimately responsible for securing your Airflow deployment. This had also been communicated via Airflow 3.2.0 Blog announcement [4].
[1] Security Model: https://airflow.apache.org/docs/apache-airflow/stable/security/jwt_token_authentication.html
[2] Workload isolation: https://airflow.apache.org/docs/apache-airflow/stable/security/workload.html
[3] JWT Token authentication: https://airflow.apache.org/docs/apache-airflow/stable/security/jwt_token_authentication.html
[4] Airflow 3.2.0 Blog announcement: https://airflow.apache.org/blog/airflow-3.2.0/
Users are recommended to upgrade to version 3.2.0, which fixes this issue. |
| Dag Authors, who normally should not be able to execute code in the webserver context could craft XCom payload causing the webserver to execute arbitrary code. Since Dag Authors are already highly trusted, severity of this issue is Low.
Users are recommended to upgrade to Apache Airflow 3.2.0, which resolves this issue. |
| The example example_xcom that was included in airflow documentation implemented unsafe pattern of reading value
from xcom in the way that could be exploited to allow UI user who had access to modify XComs to perform arbitrary
execution of code on the worker. Since the UI users are already highly trusted, this is a Low severity vulnerability.
It does not affect Airflow release - example_dags are not supposed to be enabled in production environment, however
users following the example could replicate the bad pattern. Documentation of Airflow 3.2.0 contains version of
the example with improved resiliance for that case.
Users who followed that pattern are advised to adjust their implementations accordingly. |
| The `access_key` and `connection_string` connection properties were not marked as sensitive names in secrets masker. This means that user with read permission could see the values in Connection UI, as well as when Connection was accidentaly logged to logs, those values could be seen in the logs. Azure Service Bus used those properties to store sensitive values. Possibly other providers could be also affected if they used the same fields to store sensitive data.
If you used Azure Service Bus connection with those values set or if you have other connections with those values storing sensitve values, you should upgrade Airflow to 3.1.8 |
| JWT Tokens used by tasks were exposed in logs. This could allow UI users to act as Dag Authors.
Users are advised to upgrade to Airflow version that contains fix.
Users are recommended to upgrade to version 3.2.0, which fixes this issue. |
| When user logged out, the JWT token the user had authtenticated with was not invalidated, which could lead to reuse of that token in case it was intercepted. In Airflow 3.2 we implemented the mechanism that implements token invalidation at logout. Users who are concerned about the logout scenario and possibility of intercepting the tokens, should upgrade to Airflow 3.2+
Users are recommended to upgrade to version 3.2.0, which fixes this issue. |
| Apache Airflow versions 3.0.0 - 3.1.7, has vulnerability that allows authenticated UI users with permission to one or more specific Dags to view import errors generated by other Dags they did not have access to.
Users are advised to upgrade to 3.1.7 or later, which resolves this issue |
| In AWS Auth manager, the origin of the SAML authentication has been used as provided by the client and not verified against the actual instance URL.
This allowed to gain access to different instances with potentially different access controls by reusing SAML response from other instances.
You should upgrade to 9.22.0 version of provider if you use AWS Auth Manager. |
| Apache Airflow versions 3.0.0 through 3.1.8 DagRun wait endpoint returns XCom result values even to users who only have DAG Run read permissions, such as the Viewer role.This behavior conflicts with the FAB RBAC model, which treats XCom as a separate protected resource, and with the security model documentation that defines the Viewer role as read-only.
Airflow uses the FAB Auth Manager to manage access control on a per-resource basis. The Viewer role is intended to be read-only by default, and the security model documentation defines Viewer users as those who can inspect DAGs without accessing sensitive execution results.
Users are recommended to upgrade to Apache Airflow 3.2.0 which resolves this issue. |
| Improper Certificate Validation vulnerability in Apache Airflow Provider for Databricks. Provider code did not validate certificates for connections to Databricks back-end which could result in a man-of-a-middle attack that traffic is intercepted and manipulated or credentials exfiltrated w/o notice.
This issue affects Apache Airflow Provider for Databricks: from 1.10.0 before 1.12.0.
Users are recommended to upgrade to version 1.12.0, which fixes the issue. |
| Apache Airflow versions 3.1.0 through 3.1.7 session token (_token) in cookies is set to path=/ regardless of the configured [webserver] base_url or [api] base_url.
This allows any application co-hosted under the same domain to capture valid Airflow session tokens from HTTP request headers, allowing full session takeover without attacking Airflow itself.
Users are recommended to upgrade to Apache Airflow 3.1.8 or later, which resolves this issue. |
| Apache Airflow versions 3.1.0 through 3.1.7 missing authorization vulnerability in the Execution API's Human-in-the-Loop (HITL) endpoints that allows any authenticated task instance to read, approve, or reject HITL workflows belonging to any other task instance.
Users are recommended to upgrade to Apache Airflow 3.1.8 or later, which resolves this issue. |
| Apache Airflow versions 3.0.0 through 3.1.7 FastAPI DagVersion listing API does not apply per-DAG authorization filtering when the request is made with dag_id set to "~" (wildcard for all DAGs). As a result, version metadata of DAGs that the requester is not authorized to access is returned.
Users are recommended to upgrade to Apache Airflow 3.1.8 or later, which resolves this issue. |
| Apache Airflow versions 3.1.0 through 3.1.7 /ui/dependencies endpoint returns the full DAG dependency graph without filtering by authorized DAG IDs. This allows an authenticated user with only DAG Dependencies permission to enumerate DAGs they are not authorized to view.
Users are recommended to upgrade to Apache Airflow 3.1.8 or later, which resolves this issue. |
| Airflow versions before 2.11.1 have a vulnerability that allows authenticated users with audit log access to see sensitive values in audit logs which they should not see. When sensitive connection parameters were set via airflow CLI, values of those variables appeared in the audit log and were stored unencrypted in the Airflow database. While this risk is limited to users with audit log access, it is recommended to upgrade to Airflow 2.11.1 or a later version, which addresses this issue. Users who previously used the CLI to set connections should manually delete entries with those connection sensitive values from the log table. This is similar but not the same issue as CVE-2024-50378 |
| A user with access to the DB could craft a database entry that would result in executing code on Triggerer - which gives anyone who have access to DB the same permissions as Dag Author. Since direct DB access is not usual and recommended for Airflow, the likelihood of it making any damage is low.
You should upgrade to version 6.0.0 of the provider to avoid even that risk. |
| When a DAG failed during parsing, Airflow’s error-reporting in the UI could include the full kwargs passed to the operators. If those kwargs contained sensitive values (such as secrets), they might be exposed in the UI tracebacks to authenticated users who had permission to view that DAG.
The issue has been fixed in Airflow 3.1.4 and 2.11.1, and users are strongly advised to upgrade to prevent potential disclosure of sensitive information. |
| Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection') vulnerability in Apache Airflow Common SQL Provider.
When using the partition clause in SQLTableCheckOperator as parameter (which was a recommended pattern), Authenticated UI User could inject arbitrary SQL command when triggering DAG exposing partition_clause to the user.
This allowed the DAG Triggering user to escalate privileges to execute those arbitrary commands which they normally would not have.
This issue affects Apache Airflow Common SQL Provider: before 1.24.1.
Users are recommended to upgrade to version 1.24.1, which fixes the issue. |
| API users via `/api/v2/dagReports` could perform Dag code execution in the context of the api-server if the api-server was deployed in the environment where Dag files were available. |
| An example dag `example_dag_decorator` had non-validated parameter that allowed the UI user to redirect the example to a malicious server and execute code on worker. This however required that the example dags are enabled in production (not default) or the example dag code copied to build your own similar dag. If you used the `example_dag_decorator` please review it and apply the changes implemented in Airflow 3.0.5 accordingly. |
