ONNX: Out-of-Bounds Read in RNN/GRU/LSTM Shape Inference (Old Opsets, Missing Rank Check)

Summary

CVECVE-2026-115919
StatePUBLISHED
AssignerGitHub_M
Source PriorityCVE Program / NVD first with legacy fallback
Published2026-10-26 18:17:39 UTC
Updated2026-10-26 20:17:52 UTC
DescriptionOpen Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to 1.22.0, RNNShapeInference_opset1_to_6() processes malformed RNN and LSTM nodes from opsets 1 through 6 and GRU nodes from opsets 3 through 6 without verifying that the first input has rank three. The function accesses dim(0) and dim(1) on an undersized protobuf shape, which can read outside the intended repeated field and terminate an application when onnx.shape_inference.infer_shapes() processes an untrusted model that can pass onnx.checker.check_model(). This issue is fixed in version 1.22.0.

Risk And Classification

Primary CVSS: v3.1 6.2 MEDIUM from [email protected]

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

Problem Types: CWE-125 | CWE-125 CWE-125: Out-of-bounds Read


VersionSourceTypeScoreSeverityVector
3.1[email protected]Secondary6.2MEDIUMCVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
3.1CNADECLARED6.2MEDIUMCVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

CVSS v3.1 Breakdown

Attack Vector
Local
Attack Complexity
Low
Privileges Required
None
User Interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

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

Vendor Declared Affected Products

SourceVendorProductVersionPlatforms
CNA Onnx Onnx affected < 1.22.0 Not specified

References

ReferenceSourceLinkTags
github.com/onnx/onnx/security/advisories/GHSA-48m4-24cq-37qf [email protected] github.com
github.com/onnx/onnx/releases/tag/v1.22.0 [email protected] github.com
github.com/onnx/onnx/pull/7879 [email protected] github.com
github.com/onnx/onnx/commit/92e541266af5dd38cfa48aeb92206f4268a066cd [email protected] github.com
CVE Program record CVE.ORG www.cve.org canonical
NVD vulnerability detail NVD nvd.nist.gov canonical, analysis

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