Open 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.
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Fixes
Solution
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References
History
Mon, 26 Oct 2026 19:15:00 +0000
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Mon, 26 Oct 2026 17:45:00 +0000
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| Description | Open 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. | |
| Title | ONNX: Out-of-Bounds Read in RNN/GRU/LSTM Shape Inference (Old Opsets, Missing Rank Check) | |
| Weaknesses | CWE-125 | |
| References |
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| Metrics |
cvssV3_1
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Projects
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Status: PUBLISHED
Assigner: GitHub_M
Published:
Updated: 2026-10-26T19:34:47.244Z
Reserved: 2026-10-23T20:21:45.107Z
Link: CVE-2026-115919
No data.
Status : Awaiting Analysis
Published: 2026-10-26T18:17:39.607
Modified: 2026-10-26T20:17:52.020
Link: CVE-2026-115919
No data.
OpenCVE Enrichment
Updated: 2026-10-26T19:00:08Z
Weaknesses