CVE-2026-8597

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.

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.

Summary

Amazon SageMaker Python SDK is an open-source library for training and deploying machine learning models on Amazon SageMaker. An issue exists where, under certain circumstances, the Triton inference handler deserializes model artifacts without performing integrity verification, allowing specially crafted pickle payloads to execute arbitrary code.

Impact

When using ModelBuilder with the Triton inference server, the Triton handler did not perform integrity verification before deserializing model artifacts. A remote authenticated actor with S3 write access to the model artifact path could replace model files with a crafted payload that would execute automatically on the next container lifecycle event, achieving code execution with the SageMaker execution role's IAM permissions.

Impacted versions: >= v2.199.0 AND <= v2.257.1, >= v3.0.0 AND <= v3.7.1

Patches

This issue has been addressed in Amazon SageMaker Python SDK v2.257.2 and v3.8.0. The Triton inference handler now performs integrity verification before deserializing model artifacts. AWS recommend upgrading to the latest version and rebuilding any Triton models previously created with ModelBuilder using the updated SDK. Ensure any forked or derivative code is patched to incorporate the new fixes.

Workarounds

If upgrading is not immediately possible, users should restrict S3 write access to model artifact paths to only trusted principals and monitor for unintended modifications to files in model artifact S3 locations.

References

If there any questions or comments about this advisory, contact AWS Security via vulnerability reporting page or directly via email to [email protected]. Please do not create a public GitHub issue.

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.

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.

Summary

Amazon SageMaker Python SDK is an open-source library for training and deploying machine learning models on Amazon SageMaker. An issue exists where, under certain circumstances, the Triton inference handler deserializes model artifacts without performing integrity verification, allowing specially crafted pickle payloads to execute arbitrary code.

Impact

When using ModelBuilder with the Triton inference server, the Triton handler did not perform integrity verification before deserializing model artifacts. A remote authenticated actor with S3 write access to the model artifact path could replace model files with a crafted payload that would execute automatically on the next container lifecycle event, achieving code execution with the SageMaker execution role's IAM permissions.

Impacted versions: >= v2.199.0 AND <= v2.257.1, >= v3.0.0 AND <= v3.7.1

Patches

This issue has been addressed in Amazon SageMaker Python SDK v2.257.2 and v3.8.0. The Triton inference handler now performs integrity verification before deserializing model artifacts. AWS recommend upgrading to the latest version and rebuilding any Triton models previously created with ModelBuilder using the updated SDK. Ensure any forked or derivative code is patched to incorporate the new fixes.

Workarounds

If upgrading is not immediately possible, users should restrict S3 write access to model artifact paths to only trusted principals and monitor for unintended modifications to files in model artifact S3 locations.

References

If there any questions or comments about this advisory, contact AWS Security via vulnerability reporting page or directly via email to [email protected]. Please do not create a public GitHub issue.

Summary

Amazon SageMaker Python SDK is an open-source library for training and deploying machine learning models on Amazon SageMaker. An issue exists where, under certain circumstances, the Triton inference handler deserializes model artifacts without performing integrity verification, allowing specially crafted pickle payloads to execute arbitrary code.

Impact

When using ModelBuilder with the Triton inference server, the Triton handler did not perform integrity verification before deserializing model artifacts. A remote authenticated actor with S3 write access to the model artifact path could replace model files with a crafted payload that would execute automatically on the next container lifecycle event, achieving code execution with the SageMaker execution role's IAM permissions.

Impacted versions: >= v2.199.0 AND <= v2.257.1, >= v3.0.0 AND <= v3.7.1

Patches

This issue has been addressed in Amazon SageMaker Python SDK v2.257.2 and v3.8.0. The Triton inference handler now performs integrity verification before deserializing model artifacts. AWS recommend upgrading to the latest version and rebuilding any Triton models previously created with ModelBuilder using the updated SDK. Ensure any forked or derivative code is patched to incorporate the new fixes.

Workarounds

If upgrading is not immediately possible, users should restrict S3 write access to model artifact paths to only trusted principals and monitor for unintended modifications to files in model artifact S3 locations.

References

If there any questions or comments about this advisory, contact AWS Security via vulnerability reporting page or directly via email to [email protected]. Please do not create a public GitHub issue.