Vulnerability-Lookup

GHSA-7HH5-PRP2-MFH5

Vulnerability from github – Published: 2026-05-21 17:42 – Updated: 2026-05-21 17:42

Summary

Cleartext storage of HMAC signing key in Amazon SageMaker Python SDK ModelBuilder/Serve path

Details

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 ModelBuilder/Serve component stores an HMAC signing key in cleartext as a container environment variable, which is returned in plaintext by SageMaker describe APIs.

Impact

When using ModelBuilder to build and deploy models with affected model servers (TorchServe, Multi-Model Server, TensorFlow Serving, SMD, or Triton), the SDK generates an HMAC secret key for model artifact integrity verification and stores it as the SAGEMAKER_SERVE_SECRET_KEY environment variable in the SageMaker model container configuration. This environment variable is returned in plaintext by the DescribeModel, DescribeEndpointConfig, and DescribeModelPackage APIs. A remote authenticated actor with permissions to call these describe APIs and S3 write access to the model artifact path could extract the key, forge valid integrity signatures for specially crafted model artifacts, and achieve code execution in inference containers 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. AWS recommend upgrading to the latest version and rebuilding any models previously created with ModelBuilder using the updated SDK. Models created with affected versions may still have the HMAC key stored in their container environment variables until they are rebuilt with the patched SDK. Ensure any forked or derivative code is patched to incorporate the new fixes.

Workarounds

If upgrading is not immediately possible, users can manually remove the SAGEMAKER_SERVE_SECRET_KEY environment variable from existing SageMaker models by recreating the model without this variable in the container environment configuration.

References

If there any questions or comments about this advisory, contact AWS Security via the vulnerability reporting page or directly via email to aws-security@amazon.com. Please do not create a public GitHub issue.

Severity

7.2 (High)


                  
                    CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H

8.5 (High)


                  
                    CVSS:4.0/AV:N/AC:L/AT:N/PR:H/UI:N/VC:H/VI:N/VA:N/SC:H/SI:H/SA:H

Show details on source website

JSON

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{
  "affected": [
    {
      "database_specific": {
        "last_known_affected_version_range": "\u003c= 2.257.1"
      },
      "package": {
        "ecosystem": "PyPI",
        "name": "sagemaker"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.199.0"
            },
            {
              "fixed": "2.257.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "database_specific": {
        "last_known_affected_version_range": "\u003c= 3.7.1"
      },
      "package": {
        "ecosystem": "PyPI",
        "name": "sagemaker"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "3.0.0"
            },
            {
              "fixed": "3.8.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-8596"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-312"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-05-21T17:42:40Z",
    "nvd_published_at": "2026-05-14T20:17:21Z",
    "severity": "HIGH"
  },
  "details": "## Summary\nAmazon 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.\n\n## Impact\nWhen using ModelBuilder to build and deploy models with affected model servers (TorchServe, Multi-Model Server, TensorFlow Serving, SMD, or Triton), the SDK generates an HMAC secret key for model artifact integrity verification and stores it as the SAGEMAKER_SERVE_SECRET_KEY environment variable in the SageMaker model container configuration. This environment variable is returned in plaintext by the DescribeModel, DescribeEndpointConfig, and DescribeModelPackage APIs. A remote authenticated actor with permissions to call these describe APIs and S3 write access to the model artifact path could extract the key, forge valid integrity signatures for specially crafted model artifacts, and achieve code execution in inference containers with the SageMaker execution role\u0027s IAM permissions.\n\n**Impacted versions:** \u003e= v2.199.0 AND \u003c= v2.257.1, \u003e= v3.0.0 AND \u003c= v3.7.1\n\n## Patches\nThis issue has been addressed in Amazon SageMaker Python SDK v2.257.2 and v3.8.0. AWS recommend upgrading to the latest version and rebuilding any models previously created with ModelBuilder using the updated SDK. Models created with affected versions may still have the HMAC key stored in their container environment variables until they are rebuilt with the patched SDK. Ensure any forked or derivative code is patched to incorporate the new fixes.\n\n## Workarounds\nIf upgrading is not immediately possible, users can manually remove the SAGEMAKER_SERVE_SECRET_KEY environment variable from existing SageMaker models by recreating the model without this variable in the container environment configuration.\n\n## References\nIf there any questions or comments about this advisory, contact AWS Security via the [vulnerability reporting page](https://aws.amazon.com/security/vulnerability-reporting) or directly via email to [aws-security@amazon.com](mailto:aws-security@amazon.com). Please do not create a public GitHub issue.",
  "id": "GHSA-7hh5-prp2-mfh5",
  "modified": "2026-05-21T17:42:40Z",
  "published": "2026-05-21T17:42:40Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/aws/sagemaker-python-sdk/security/advisories/GHSA-7hh5-prp2-mfh5"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-8596"
    },
    {
      "type": "WEB",
      "url": "https://aws.amazon.com/security/security-bulletins/2026-031-aws"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/aws/sagemaker-python-sdk"
    },
    {
      "type": "WEB",
      "url": "https://github.com/aws/sagemaker-python-sdk/releases/tag/v2.257.2"
    },
    {
      "type": "WEB",
      "url": "https://github.com/aws/sagemaker-python-sdk/releases/tag/v3.8.0"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:H/UI:N/VC:H/VI:N/VA:N/SC:H/SI:H/SA:H",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Cleartext storage of HMAC signing key in Amazon SageMaker Python SDK ModelBuilder/Serve path"
}

CVE-2026-8596 (GCVE-0-2026-8596)

Vulnerability from cvelistv5 – Published: 2026-05-14 19:35 – Updated: 2026-05-16 03:56

Title

Cleartext storage of HMAC signing key in Amazon SageMaker Python SDK ModelBuilder/Serve path

Summary

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.

Severity

7.2 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H

8.5 (High)


                        
                          CVSS:4.0/AV:N/AC:L/AT:N/PR:H/UI:N/VC:H/VI:N/VA:N/SC:H/SI:H/SA:H

CWE

CWE-312 - Cleartext storage of sensitive information

Assigner

AMZN

References

4 references

URL	Tags
https://github.com/aws/sagemaker-python-sdk/relea…	patch
https://github.com/aws/sagemaker-python-sdk/relea…	patch
https://aws.amazon.com/security/security-bulletin…	vendor-advisory
https://github.com/aws/sagemaker-python-sdk/secur…	third-party-advisory

Impacted products

1 product

Vendor	Product	Version
Amazon SageMaker Python SDK	AWS	Affected: 2.199.0 , ≤ 2.257.1 (custom) Affected: 3.0.0 , ≤ 3.7.1 (custom)

Show details on NVD website

JSON

To clipboard

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Sightings

Author	Source	Type	Date	Other

Nomenclature

Seen: The vulnerability was mentioned, discussed, or observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Detection rules are retrieved from Rulezet.

Action not permitted

GHSA-7HH5-PRP2-MFH5

Summary

Impact

Patches

Workarounds

References

CVE-2026-8596 (GCVE-0-2026-8596)

Tags

Sightings

Nomenclature