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CVE-2026-115920: ONNX: Out-of-bounds read in ConvTranspose shape inference via undersized weight rank

• Severity: medium (CVSS 6.2 v3.1)
• Exploited in the wild: not listed in CISA KEV
• EPSS: 0.2% probability of exploitation in the next 30 days
• Public exploit or detection template: none known
• Published: 2026-10-26 · updated 2026-10-26
• Weakness: CWE-125

Description

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.

Affected products

• onnx: from 1.2.1, < 1.22.0

References

• https://github.com/onnx/onnx/commit/2ec288908952eea9dcf48d4b197915e1f72c6abf
• https://github.com/onnx/onnx/pull/7997
• https://github.com/onnx/onnx/releases/tag/v1.22.0
• https://github.com/onnx/onnx/security/advisories/GHSA-6pwm-x34p-fvj2

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