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

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.

Source published

26 Oct 2026 17:26:40 UTC

Source modified

26 Oct 2026 17:26:40 UTC

Observed here

26 Oct 2026 18:35:30 UTC

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CVSS 3.1 · 6.2 CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H