Bug 2560983 (CVE-2026-115922)

Summary: CVE-2026-115922 onnx: onnx: Denial of Service via excessive memory allocation in tensor unpacking
Product: [Other] Security Response Reporter: OSIDB Bzimport <bzimport>
Component: vulnerabilityAssignee: Product Security DevOps Team <prodsec-dev>
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Version: unspecifiedKeywords: Security
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OS: Linux   
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A flaw was found in ONNX. This vulnerability allows an attacker to cause a Denial of Service (DoS) through excessive memory consumption by providing a specially crafted machine learning model. The issue occurs because tensor materialization routines resize memory arrays based on dimensions declared in the model without verifying that the packed data contains the required number of elements.
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Description OSIDB Bzimport 2026-10-26 17:45:48 UTC 
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.