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 ModelBuilder/Serve component stores an HMAC signing key in cleartext as a container environment variable, which is returned in plaintext by SageMaker describe APIs.
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.
This advisory addresses the use of the search_hub() function within the SageMaker Python SDK's JumpStart search functionality. An actor with the ability to control query parameters passed to the search_hub() function could potentially provide malformed input that causes the eval() function to execute arbitrary commands, access sensitive data, or compromise the execution environment. A defense-in-depth enhancement has been implemented to replace code evaluation with safe string operations when processing search …
SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. An issue where SSL certificate verification was globally disabled in the Triton Python backend has been found.
SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. An issue where the HMAC secret key is stored in environment variables and disclosed via the DescribeTrainingJob API has been identified.
A vulnerability in the SageMaker Workflow component of aws/sagemaker-python-sdk allows for the possibility of MD5 hash collisions in all versions. This can lead to workflows being inadvertently replaced due to the reuse of results from different configurations that produce the same MD5 hash. This issue can cause integrity problems within the pipeline, potentially leading to erroneous processing outcomes.
sagemaker.base_deserializers.NumpyDeserializer module before v2.218.0 allows potentially unsafe deserialization when untrusted data is passed as pickled object arrays. This consequently may allow an unprivileged third party to cause remote code execution, denial of service, affecting both confidentiality and integrity. Impacted versions: <2.218.0.
The capture_dependencies function in sagemaker.serve.save_retrive.version_1_0_0.save.utils module before version 2.214.3 allows for potentially unsafe Operating System (OS) Command Injection if inappropriate command is passed as the “requirements_path” parameter. This consequently may allow an unprivileged third party to cause remote code execution, denial of service, affecting both confidentiality and integrity. Impacted versions: <2.214.3