Hmac — CraftedSignal Threat Feedhttps://feed.craftedsignal.io/tags/hmac/Trending threats, MITRE ATT&CK coverage, and detection metadata. Fed continuously.Hugoenhello@craftedsignal.iohello@craftedsignal.ioThu, 21 May 2026 17:44:00 +0000Amazon SageMaker Python SDK HMAC Key Leakage via API Exposurehttps://feed.craftedsignal.io/briefs/2026-05-sagemaker-hmac-leak/Thu, 21 May 2026 17:44:00 +0000hello@craftedsignal.iohttps://feed.craftedsignal.io/briefs/2026-05-sagemaker-hmac-leak/Amazon SageMaker Python SDK exposes an HMAC signing key in cleartext via API calls, enabling a remote authenticated actor to forge model artifacts and achieve code execution.The Amazon SageMaker Python SDK, a library for training and deploying machine learning models on Amazon SageMaker, contains a vulnerability related to the ModelBuilder/Serve component. Specifically, when building and deploying models with affected model servers (TorchServe, Multi-Model Server, TensorFlow Serving, SMD, or Triton), the SDK inadvertently stores an HMAC secret key in cleartext as the SAGEMAKER_SERVE_SECRET_KEY environment variable within the SageMaker model container configuration. This sensitive environment variable is then exposed in plaintext through the DescribeModel, DescribeEndpointConfig, and DescribeModelPackage APIs. This vulnerability affects versions >= v2.199.0 AND <= v2.257.1, as well as versions >= v3.0.0 AND <= v3.7.1. Defenders must upgrade to the patched versions and rebuild models to remediate the risk.

Attack Chain

  1. Attacker gains authenticated access to the AWS environment.
  2. Attacker enumerates SageMaker models via DescribeModel, DescribeEndpointConfig, or DescribeModelPackage API calls.
  3. The API response reveals the SAGEMAKER_SERVE_SECRET_KEY in plaintext within the container environment configuration.
  4. Attacker gains S3 write access to the model artifact path.
  5. Attacker crafts a malicious model artifact, forging a valid integrity signature using the leaked HMAC key.
  6. Attacker uploads the forged model artifact to the S3 bucket, replacing the original model.
  7. The compromised model is deployed to an inference container.
  8. Upon execution, the malicious model executes code within the SageMaker execution role’s IAM permissions, leading to potential privilege escalation.

Impact

Successful exploitation of this vulnerability allows an attacker with authenticated access and S3 write permissions to achieve code execution within SageMaker inference containers. The attacker can leverage the SageMaker execution role’s IAM permissions, potentially leading to privilege escalation, data exfiltration, or other malicious activities. The number of affected SageMaker models is dependent on the number of organizations using ModelBuilder with vulnerable SDK versions to create and deploy models. If this attack succeeds, it allows attackers to take complete control over SageMaker machine learning models.

Recommendation

  • Upgrade the Amazon SageMaker Python SDK to versions v2.257.2 or v3.8.0 or later to address the vulnerability as stated in the advisory.
  • Rebuild any models previously created with ModelBuilder using the updated SDK to ensure the sensitive HMAC key is not stored in the container environment variables.
  • Monitor AWS CloudTrail logs for API calls to DescribeModel, DescribeEndpointConfig, and DescribeModelPackage to detect potential enumeration attempts by attackers.
  • Implement strict IAM policies to limit access to the DescribeModel, DescribeEndpointConfig, and DescribeModelPackage APIs and S3 write access to model artifact paths.
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