Amazon SageMaker Python SDK vulnerabilities

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.

The ModelBuilder/Serve components in Amazon SageMaker Python SDK before v2.257.2 and v3 before v3.8.0 have cleartext storage of sensitive information. This may 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, leading to code execution in inference containers.

Insertion of sensitive information into log files in the git_utils[.]py component in Amazon SageMaker Python SDK V3 before 3.10.0 and V2 before 2.257.3 might allow an authenticated remote user to obtain plaintext repository credentials from CloudWatch Logs when a git clone operation fails, because the runclone_command() function does not redact credentials from subprocess error output. To remediate this issue, users should upgrade to Amazon SageMaker Python SDK V3 3.10.0 or V2 2.257.3.

Cleartext storage of sensitive information in the @step and @remote decorator pipeline component in Amazon SageMaker Python SDK before v3.11.0 and v2.256.0 might allow an authenticated remote user to extract the HMAC signing key from DescribePipeline API responses and forge valid integrity signatures for crafted function payloads, achieving code execution in another user's pipeline execution context within the same AWS account.