CVE-2026-8596 in Amazon SageMaker Python SDK
Summary
by MITRE • 05/15/2026
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.
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Analysis
by VulDB Data Team • 05/21/2026
The vulnerability under discussion represents a critical security flaw in the Amazon SageMaker Python SDK affecting versions prior to v2.257.2 and v3.8.0 within the ModelBuilder/Serve component. This issue stems from the improper handling of sensitive cryptographic information during API response processing, specifically exposing HMAC signing keys that should remain confidential. The flaw allows authenticated attackers to extract these keys from SageMaker API responses and subsequently forge valid integrity signatures for malicious model artifacts. From a cybersecurity perspective, this vulnerability directly relates to CWE-312, which addresses the cleartext storage of sensitive information, and potentially CWE-310, concerning cryptographic issues. The attack vector requires a remote authenticated actor possessing legitimate permissions to invoke SageMaker describe APIs and sufficient S3 write access to the model artifact storage path, making it particularly concerning for environments where privilege escalation or compromised credentials are possible.
The technical exploitation of this vulnerability involves the extraction of HMAC signing keys from API responses that contain model artifact metadata and integrity information. When SageMaker processes model artifacts through ModelBuilder, it generates API responses that inadvertently include cryptographic material used for signature verification. An attacker with appropriate permissions can intercept these responses and parse the embedded HMAC keys, which are then used to craft forged signatures that appear legitimate to SageMaker's validation mechanisms. This process enables the attacker to upload malicious model artifacts that will be accepted and executed within the inference containers, effectively bypassing the integrity checks designed to prevent unauthorized modifications. The operational impact extends beyond simple privilege escalation as it allows for complete code execution within the inference environment, potentially leading to data exfiltration, system compromise, or lateral movement within the cloud infrastructure. The vulnerability particularly affects machine learning workflows where model artifacts are frequently uploaded and validated, making it a significant concern for organizations relying heavily on SageMaker's automated model building capabilities.
The remediation strategy focuses on upgrading to the patched versions of the Amazon SageMaker Python SDK, specifically v2.257.2 for the v2.x series and v3.8.0 for the v3.x series. This upgrade addresses the core issue by implementing proper cryptographic key handling and ensuring that HMAC signing keys are not exposed in API responses. Organizations must also rebuild any models previously created with the vulnerable SDK versions to ensure that the compromised artifacts are replaced with properly secured versions. The mitigation process requires careful planning as it involves not only updating the SDK but also validating that all existing model deployments remain functional and secure. From an operational security standpoint, this vulnerability highlights the importance of proper cryptographic key management and the need for regular security assessments of development and deployment tools. The ATT&CK framework categorizes this type of vulnerability under T1548.001, which deals with abuse of cloud credentials, and T1059, concerning command and control through execution of code, as the compromised system could be used to execute further malicious activities. Organizations should implement monitoring for unusual API access patterns and S3 write operations that might indicate exploitation attempts, while also establishing secure development practices that prevent similar issues in other components of their machine learning pipelines.