Answer in brief
CVE-2026-115922 records a Medium severity (CVSS 6.2) vulnerability in ONNX: Denial of service via unchecked expansion of packed sub-byte tensors in ONNX Python APIs. 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, _unpack_2bit() and _unpack_4bit() resize unpacked arrays according to attacker-controlled TensorProto.dims without verifying that raw_data or int32_data encodes the declared number of elements. Materializing an untrusted UINT2, INT2, UINT4, INT4, or FLOAT4E2M1 tensor through onnx.numpy_helper.to_array() or onnx.reference.ReferenceEvaluator can therefore expand a small packed payload into a disproportionately large allocation and exhaust memory. This issue is fixed in version 1.22.0.
Quoted source text, attributed separately from HOL analysis.