Apache Airflow's Teradata provider embedded cloud storage credentials directly into SQL statements. `S3ToTeradataOperator` and `AzureBlobStorageToTeradataOperator` interpolate the source bucket's credentials as plain string literals into the `CREATE MULTISET TABLE ... LOCATION` statement whenever the bucket is private and no `teradata_authorization_name` is configured — which is the default credential path for both operators. The statement is then logged and executed, so the credentials reach two places outside the operator's control.

The two operators expose different credentials through different channels, and deployments should check both. `S3ToTeradataOperator` takes its values from `s3_hook.get_credentials()`, which under an instance profile or IRSA returns runtime AWS credentials that were never registered with Airflow's secrets masker — and the STS session token is runtime-generated and therefore unmasked even when an AWS connection is configured. Those credentials appear **in the Airflow task log**, readable by any user with log-view permission on the Dag. `AzureBlobStorageToTeradataOperator` takes its storage account key from the connection, so the masker usually redacts the task-log copy; its exposure is the Teradata side. **Both** operators write the credentials into Teradata's DBQL query logs and live monitoring views, where Airflow's masking never applies and the values persist for that system's log retention period.

Affects deployments using either operator a…

Apache Airflow's Teradata provider embedded cloud storage credentials directly into SQL statements. `S3ToTeradataOperator` and `AzureBlobStorageToTeradataOperator` interpolate the source bucket's credentials as plain string literals into the `CREATE MULTISET TABLE ... LOCATION` statement whenever the bucket is private and no `teradata_authorization_name` is configured — which is the default credential path for both operators. The statement is then logged and executed, so the credentials reach two places outside the operator's control.

The two operators expose different credentials through different channels, and deployments should check both. `S3ToTeradataOperator` takes its values from `s3_hook.get_credentials()`, which under an instance profile or IRSA returns runtime AWS credentials that were never registered with Airflow's secrets masker — and the STS session token is runtime-generated and therefore unmasked even when an AWS connection is configured. Those credentials appear **in the Airflow task log**, readable by any user with log-view permission on the Dag. `AzureBlobStorageToTeradataOperator` takes its storage account key from the connection, so the masker usually redacts the task-log copy; its exposure is the Teradata side. **Both** operators write the credentials into Teradata's DBQL query logs and live monitoring views, where Airflow's masking never applies and the values persist for that system's log retention period.

Affects deployments using either operator a…