Vulnerabilidades do AWS Amazon SageMaker Python SDK

Missing verification of source of a communication channel in the default_bucket() function in Amazon SageMaker Python SDK V3 before 3.10.0 and V2 before 2.257.3 might allow remote actors to achieve code execution in the target SageMaker execution environment and obtain IAM credentials via pre-creation of a predictably named S3 bucket that the SDK uses without ownership validation. To remediate this issue, users should upgrade to Amazon SageMaker Python SDK 3.10.0 (V3) or 2.257.3 (V2).

Missing integrity verification in the Triton inference handler in Amazon SageMaker Python SDK v2 before v2.257.2 and v3 before v3.8.0 might allow a remote authenticated actor to achieve code execution in inference containers via replacement of model artifacts in S3 with a specially crafted pickle payload that is deserialized without verification. This issue requires a remote authenticated actor with S3 write access to the model artifact path. To remediate this issue, we recommend upgrading to Amazon SageMaker Python SDK v2.257.2 or v3.8.0 and rebuild any Triton models previously created with ModelBuilder using the updated SDK.

Cleartext storage of sensitive information in the ModelBuilder/Serve component in Amazon SageMaker Python SDK before v2.257.2 and v3 before v3.8.0 might allow a remote authenticated actor to extract the HMAC signing key from SageMaker API responses and forge valid integrity signatures for specially crafted model artifacts, achieving code execution in inference containers. This issue requires a remote authenticated actor with permissions to call SageMaker describe APIs and S3 write access to the model artifact path. To remediate this issue, we recommend upgrading to Amazon SageMaker Python SDK v2.257.2 or v3.8.0 and rebuild any models previously created with ModelBuilder using the updated SDK.