Answer in brief
CVE-2026-115920 records a Medium severity (CVSS 6.2) vulnerability in ONNX: Out-of-bounds read in ConvTranspose shape inference via undersized weight rank. 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 | >=1.2.1 <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. From 1.2.1 until 1.22.0, ConvTranspose shape inference processes a weight tensor with fewer than three dimensions without a kernel_shape attribute by deriving an empty effective_kernel_shape and indexing it for the input's spatial dimensions. The flaw affects convTransposeShapeInference_opset11() and the current inference path, and an untrusted model that can pass onnx.checker.check_model() can crash onnx.shape_inference.infer_shapes(), causing denial of service without demonstrated confidentiality or integrity impact. This issue is fixed in version 1.22.0.
Quoted source text, attributed separately from HOL analysis.