ONNX: Out-of-bounds read in ConvTranspose shape inference via undersized weight rank
Default status is the baseline for the product, each version can override it (e.g. patched versions marked unaffected).
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GHSA-6pwm-x34p-fvj2 | ONNX: Out-of-bounds read in ConvTranspose shape inference via undersized weight rank |
No CVSS v4.0
No CVSS v3.0
No CVSS v2
This CVE is not in the KEV list.
Exploit prediction score: < 1% (0.178% probability within 30 days)
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| 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. | |
| Title | ONNX: Out-of-bounds read in ConvTranspose shape inference via undersized weight rank | |
| Weaknesses | CWE-125 | |
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cvssV3_1
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