Answer in brief
CVE-2026-115919 records a Medium severity (CVSS 6.2) vulnerability in ONNX: Out-of-Bounds Read in RNN/GRU/LSTM Shape Inference (Old Opsets, Missing Rank Check). The current sources do not mark it as known exploited. The current feed maps onnx/onnx (generic). Check affected ranges and fixed versions before updating.
Analysis pending evidence review
HOL Guard separates source facts from reviewed analysis. See the methodology.
CVSS is 6.2. The current sources do not mark it as known exploited. Treat this as a source-backed prioritization signal, not a statement about your environment.
Analysis status
Analysis pending evidence review
Factual feed record only; HOL analysis is not approved for indexing. Read the methodology.
The current feed maps onnx/onnx (generic). Check affected ranges and fixed versions before updating.
| Package | Affected range | Fixed version |
|---|---|---|
| onnx/onnxgeneric | <1.22.0 | 1.22.0 |
Published upstream
Oct 26, 2026
Evidence: source:cvelist:source_dates:source-dates:recordSource modified
Oct 26, 2026
Evidence: source:cvelist:source_dates:source-dates:recordFirst seen by HOL
Oct 26, 2026
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.
Quoted source text, attributed separately from HOL analysis.