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

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No collected risk observation for these identifiers. This does not establish absence of exploitation or a zero probability.

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 19:48:35 UTC

Observed here

26 Oct 2026 20:35:31 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