Bug 2560980 (CVE-2026-115920)

Summary:	CVE-2026-115920 onnx: onnx: Denial of service via out-of-bounds read in ConvTranspose shape inference
Product:	[Other] Security Response	Reporter:	OSIDB Bzimport <bzimport>
Component:	vulnerability	Assignee:	Product Security <prodsec-ir-bot>
Status:	NEW ---	QA Contact:
Severity:	medium	Docs Contact:
Priority:	medium
Version:	unspecified	Keywords:	Security
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Hardware:	All
OS:	Linux
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Doc Text:	A flaw was found in ONNX. When processing a crafted machine learning model containing a transpose convolution (ConvTranspose) operator with an undersized weight tensor, an out-of-bounds read occurs during dimension calculation. An attacker can exploit this issue by supplying a malicious model to an application performing shape inference, causing the application to crash and resulting in a denial of service (DoS).	Story Points:	---
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oVirt Team:	---	RHEL 7.3 requirements from Atomic Host:
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Description OSIDB Bzimport 2026-10-26 17:43:48 UTC

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.