CVE-2026-115921
ONNX: Null Pointer Dereference in ScanInferenceFunction Due to Missing Required Attribute
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.
INFO
Published Date :
Oct. 26, 2026, 6:17 p.m.
Last Modified :
Oct. 26, 2026, 6:34 p.m.
Remotely Exploit :
No
Source :
[email protected]
CVSS Scores
| Score | Version | Severity | Vector | Exploitability Score | Impact Score | Source |
|---|---|---|---|---|---|---|
| CVSS 3.1 | MEDIUM | MITRE-CVE | ||||
| CVSS 3.1 | MEDIUM | [email protected] |
Solution
- Update ONNX to version 1.22.0 or later.
- Apply relevant security patches for ONNX.
- Validate ONNX model inputs for num_scan_inputs.
References to Advisories, Solutions, and Tools
Here, you will find a curated list of external links that provide in-depth
information, practical solutions, and valuable tools related to
CVE-2026-115921.
CWE - Common Weakness Enumeration
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specific instances of vulnerabilities, CWE categorizes the common flaws or
weaknesses that can lead to vulnerabilities. CVE-2026-115921 is
associated with the following CWEs:
Common Attack Pattern Enumeration and Classification (CAPEC)
Common Attack Pattern Enumeration and Classification
(CAPEC)
stores attack patterns, which are descriptions of the common attributes and
approaches employed by adversaries to exploit the CVE-2026-115921
weaknesses.
We scan GitHub repositories to detect new proof-of-concept exploits. Following list is a collection of public exploits and proof-of-concepts, which have been published on GitHub (sorted by the most recently updated).
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CVE-2026-115921 vulnerability anywhere in the article.
The following table lists the changes that have been made to the
CVE-2026-115921 vulnerability over time.
Vulnerability history details can be useful for understanding the evolution of a vulnerability, and for identifying the most recent changes that may impact the vulnerability's severity, exploitability, or other characteristics.
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New CVE Received by [email protected]
Oct. 26, 2026
Action Type Old Value New Value Added 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. Added CVSS V3.1 AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H Added CWE CWE-476 Added CWE CWE-195 Added Affected New affected value received. <a href="https://github.com/CVEProject/cvelistV5/blob/main/cves/2026/115xxx/CVE-2026-115921.json">CVE-2026-115921</a> Added Reference https://github.com/onnx/onnx/commit/dd6bc92ea39fd2274fd671f0734b0b8b558356f5 Added Reference https://github.com/onnx/onnx/pull/7916 Added Reference https://github.com/onnx/onnx/releases/tag/v1.22.0 Added Reference https://github.com/onnx/onnx/security/advisories/GHSA-m4pr-6v3c-6fgr