CVE-2026-1777 is a vulnerability identified in the AWS SageMaker Python SDK versions before 3.2.0 and 2.256.0. The issue arises because the DescribeTrainingJob API call returns the ModelBuilder HMAC signing key in cleartext within its response elements. This key is intended to secure the integrity and authenticity of model-building operations. However, its exposure allows an attacker who has permissions to both call the DescribeTrainingJob API and modify objects in the S3 bucket used for Training Job outputs to upload arbitrary artifacts. These malicious artifacts can be executed the next time the training job runs, effectively enabling remote code execution within the SageMaker training environment. The vulnerability is classified under CWE-319, which concerns the cleartext transmission of sensitive information. The CVSS v3.1 base score is 7.2, reflecting high severity due to network attack vector, low attack complexity, required privileges, and high impact on confidentiality, integrity, and availability. The exploit does not require user interaction but does require the attacker to have elevated permissions, which may be obtained through misconfiguration or insider threat. No public patches are linked yet, and no known exploits have been reported in the wild. The vulnerability highlights the risk of sensitive key exposure in cloud SDKs and the critical need for strict access controls and secure handling of cryptographic keys in AI/ML pipelines.

For European organizations leveraging AWS SageMaker for machine learning workloads, this vulnerability poses a significant risk. Exposure of the ModelBuilder HMAC key can lead to unauthorized modification of training artifacts, resulting in execution of arbitrary code within the training environment. This can compromise the confidentiality of sensitive training data and models, integrity of AI workflows, and availability of AI services. Attackers could implant backdoors or malicious code into models, potentially undermining AI decision-making processes or causing denial of service. Given the increasing reliance on AI/ML in sectors such as finance, healthcare, and manufacturing across Europe, the impact could extend to critical infrastructure and sensitive personal data processing. The requirement for elevated permissions reduces the attack surface but also indicates that insider threats or compromised credentials could be exploited. The lack of known exploits suggests limited immediate risk but does not diminish the urgency for remediation due to the high potential impact.

European organizations should immediately upgrade the AWS SageMaker Python SDK to versions 3.2.0 or later (or 2.256.0 or later) where this vulnerability is addressed. Until patches are applied, organizations must enforce strict IAM policies to limit who can call the DescribeTrainingJob API and who can modify objects in the S3 output buckets associated with training jobs. Implement fine-grained access controls and monitor for unusual API calls or S3 object modifications. Employ logging and alerting on these activities to detect potential exploitation attempts. Additionally, consider isolating training jobs and their output storage to minimize the blast radius of any compromise. Regularly audit permissions and rotate any exposed keys or credentials. Finally, integrate security reviews into AI/ML pipeline development to ensure cryptographic keys and sensitive information are never exposed in cleartext responses or logs.