Apache Airflow: JWT Token Exposure in KubernetesExecutor Command-Line Arguments
A bug in Apache Airflow's KubernetesExecutor caused JWT tokens used by worker pods to authenticate against the Execution API to be passed to the worker container as command-line arguments visible in the pod spec. An authenticated UI/API user with Kubernetes read-only access to the cluster (e.g. `pods/get` in the Airflow namespace) could harvest the JWT from `kubectl describe pod` output and then call state-mutating Execution API endpoints — triggering Dag runs, clearing runs, reading or writing Variables / Connections / XComs — as if they were a running task. Affects deployments using the `KubernetesExecutor`. Users are advised to upgrade to
Secrets in Variables saved as JSON dictionaries were not properly redacted - in case thee variables were retrieved by the user the secrets stored as nested fields were not masked.
If you do not store variables with sensitive values in JSON form, you are not affected. Otherwise please upgrade to Apache Airflow 3.2.0 that has the fix implemented
Apache Airflow: Exposing stack trace in case of constraint error
In case of SQL errors, exception/stack trace of errors was exposed in API even if "api/expose_stack_traces" was set to false. That could lead to exposing additional information to potential attacker. Users are recommended to upgrade to Apache Airflow 3.2.0, which fixes the issue.
Apache Airflow: API extra-links triggers XCom deserialization/class instantiation (Airflow 3.1.5)
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 fixes the issue.
Apache Airflow: Users with asset materialization permisssions could trigger Dags they had no access to
UI / API User with asset materialize permission could trigger dags they had no access to.
Users are advised to migrate to Airflow version 3.2.0 that fixes the issue.