Vulnerability Description
Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.4.0 until 1.22.0, ScanInferenceFunction, ScanInferenceFunction_opset8, and ScanInferenceFunction_opset9 do not safely validate the required num_scan_inputs attribute. A missing attribute is dereferenced as a null pointer, while a negative value is converted to size_t and can reach an exceptional oversized allocation path. An untrusted model can therefore terminate or disrupt shape inference, including a negative-value model that can pass onnx.checker.check_model(). This issue is fixed in version 1.22.0.
CVSS Score
MEDIUM
Related Weaknesses (CWE)
References
- https://github.com/onnx/onnx/commit/dd6bc92ea39fd2274fd671f0734b0b8b558356f5
- https://github.com/onnx/onnx/pull/7916
- https://github.com/onnx/onnx/releases/tag/v1.22.0
- https://github.com/onnx/onnx/security/advisories/GHSA-m4pr-6v3c-6fgr
FAQ
What is CVE-2026-115921?
CVE-2026-115921 is a vulnerability with a CVSS score of 6.2 (MEDIUM). Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.4.0 until 1.22.0, ScanInferenceFunction, ScanInferenceFunction_opset8, and ScanInferenceFunction_o...
How severe is CVE-2026-115921?
CVE-2026-115921 has been rated MEDIUM with a CVSS base score of 6.2/10. Review the CVSS metrics above for detailed severity breakdown.
Is there a patch for CVE-2026-115921?
Check the references section above for vendor advisories and patch information. Review vendor security bulletins for remediation guidance.