GHSA-7hh5-prp2-mfh5
HIGHGHSA-7hh5-prp2-mfh5 is a high-severity (CVSS 7.2) CWE-312 vulnerability in sagemaker. O3 Security confirms whether GHSA-7hh5-prp2-mfh5 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
Cleartext storage of HMAC signing key in Amazon SageMaker Python SDK ModelBuilder/Serve path
Exploitation Status
No confirmed exploitation observed yet
- A successful exploit gives an attacker total control of the affected component, not partial access.
- CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.
Exploitation and automatability from CISA’s SSVC triage for GHSA-7hh5-prp2-mfh5.
EPSS Exploitation Probability
EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.
How urgent is this, really
GHSA-7hh5-prp2-mfh5 plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.
Where this sits among everything scored
Of 360,781 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.
Real-World Exposure
sagemaker🐍sagemakerReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
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 [email protected]. Please do not create a public GitHub issue.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | sagemaker | ≥ 2.199.0&&< 2.257.2 | 2.257.2 |
| 🐍PyPI | sagemaker | ≥ 3.0.0&&< 3.8.0 | 3.8.0 |
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for sagemaker. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.
Fix
Update sagemaker to 2.257.2 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-7hh5-prp2-mfh5 is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 pinpoints whether GHSA-7hh5-prp2-mfh5 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.
Tailored to GHSA-7hh5-prp2-mfh5. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-7hh5-prp2-mfh5 in your dependencies?
O3 detects GHSA-7hh5-prp2-mfh5 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.