GHSA-RJRP-M2JW-PV9C

Vulnerability from github – Published: 2026-02-02 23:33 – Updated: 2026-02-03 19:01
VLAI?
Summary
SageMaker Python SDK has Exposed HMAC
Details

Summary

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.

Impact

  • Function and Payload Tampering: Attackers with DescribeTrainingJob permissions may extract HMAC secret keys and forge serialized function payloads stored in S3. These tampered payloads would be processed and executed without triggering integrity validation errors, enabling unintended code substitution.
  • Arbitrary Code Execution in the Training Environment: An third party with both DescribeTrainingJob permissions and write access to the job's S3 output location can extract the HMAC key, craft inappropriate Python objects, and achieve remote code execution in the client's Python process when the victim retrieves remote function results.
  • Data and Credentials Handling: Arbitrary remote code execution may interact with sensitive data, model artifacts, environment variables, and potentially AWS metadata.
  • Cross-Tenant or Shared Environment Risks: In multi-tenant, shared S3 bucket, a disclosed HMAC key could act as a pivot point to perform inappropriate actions against other users' remote function workloads. This could leverage the IAM permissions, shared S3 buckets, or VPC resources to compromise adjacent services or data.

Impacted versions

  • SageMaker Python SDK v3 < v3.2.0
  • SageMaker Python SDK v2 < v2.256.0

Patches

This issue has been addressed in SageMaker Python SDK version v3.2.0 and v2.256.0. Upgrading to the latest version immediately and ensuring any forked or derivative code is patched to incorporate the new fixes is recommended.

Workarounds

Customers using self-signed certificates for internal model downloads should add their private Certificate Authority (CA) certificate to the container image rather than relying on the SDK’s previous insecure configuration. This opt-in approach maintains security while accommodating internal trusted domains.

Resources

If there are 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
8.7 (High)
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
Show details on source website
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{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "sagemaker"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "3.0"
            },
            {
              "fixed": "3.2.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "sagemaker"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.256.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-1777"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-201",
      "CWE-295",
      "CWE-319"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-02-02T23:33:32Z",
    "nvd_published_at": "2026-02-02T23:16:04Z",
    "severity": "HIGH"
  },
  "details": "### Summary\n\nSageMaker 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.\n\n### Impact\n\n- Function and Payload Tampering: Attackers with DescribeTrainingJob permissions may extract HMAC secret keys and forge serialized function payloads stored in S3. These tampered payloads would be processed and executed without triggering integrity validation errors, enabling unintended code substitution.\n- Arbitrary Code Execution in the Training Environment: An third party with both DescribeTrainingJob permissions and write access to the job\u0027s S3 output location can extract the HMAC key, craft inappropriate Python objects, and achieve remote code execution in the client\u0027s Python process when the victim retrieves remote function results.\n- Data and Credentials Handling: Arbitrary remote code execution may interact with sensitive data, model artifacts, environment variables, and potentially AWS metadata.\n- Cross-Tenant or Shared Environment Risks: In multi-tenant, shared S3 bucket, a disclosed HMAC key could act as a pivot point to perform inappropriate actions against other users\u0027 remote function workloads. This could leverage the IAM permissions, shared S3 buckets, or VPC resources to compromise adjacent services or data.\n\n### Impacted versions\n\n- SageMaker Python SDK v3 \u003c v3.2.0\n- SageMaker Python SDK v2 \u003c v2.256.0\n\n### Patches\nThis issue has been addressed in SageMaker Python SDK version [v3.2.0](https://github.com/aws/sagemaker-python-sdk/tree/22d30f577a6139431a1fb9154b7b88a0e2a1ace6) and [v2.256.0](https://github.com/aws/sagemaker-python-sdk/tree/a140cfcd12abfee10254cb4dea3bb10758e4321c). Upgrading to the latest version immediately and ensuring any forked or derivative code is patched to incorporate the new fixes is recommended.\n\n### Workarounds\nCustomers using self-signed certificates for internal model downloads should add their private Certificate Authority (CA) certificate to the container image rather than relying on the SDK\u2019s previous insecure configuration. This opt-in approach maintains security while accommodating internal trusted domains.\n\n### Resources\nIf there are 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-rjrp-m2jw-pv9c",
  "modified": "2026-02-03T19:01:11Z",
  "published": "2026-02-02T23:33:32Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/aws/sagemaker-python-sdk/security/advisories/GHSA-rjrp-m2jw-pv9c"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-1777"
    },
    {
      "type": "WEB",
      "url": "https://github.com/aws/sagemaker-python-sdk/commit/708c7b2f4135ecaec55973d098f3dbe98b657933"
    },
    {
      "type": "WEB",
      "url": "https://github.com/aws/sagemaker-python-sdk/commit/fb0d789db4fd5fecde5509963939369f4c7ce63b"
    },
    {
      "type": "WEB",
      "url": "https://aws.amazon.com/security/security-bulletins/2026-004-AWS"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/aws/sagemaker-python-sdk"
    },
    {
      "type": "WEB",
      "url": "https://github.com/aws/sagemaker-python-sdk/releases/tag/v2.256.0"
    },
    {
      "type": "WEB",
      "url": "https://github.com/aws/sagemaker-python-sdk/releases/tag/v3.2.0"
    }
  ],
  "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:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "SageMaker Python SDK has Exposed HMAC"
}


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  • 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.
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  • 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.