CVE-2026-8596
HighSummary
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.
Risk Assessment
The organization may be exposed to unauthorized code execution in inference containers, potentially leading to data leakage or system compromise.
Recommendation
It is recommended to upgrade to Amazon SageMaker Python SDK v2.257.2 or v3.8.0 and rebuild any models previously created with ModelBuilder using the updated SDK.
Other vulnerabilities in Amazon SageMaker Python SDK
See all- CVE-2026-83551High
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.
- CVE-2026-8597High
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.
Original NVD description (English source)
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.