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

Summary

ONNX ConvTranspose shape inference performed an out-of-bounds read when the weight tensor had fewer than three dimensions and the model did not provide a kernel_shape attribute.

The inference function derived an empty effective_kernel_shape vector from the undersized weight tensor, then indexed that vector once for each spatial dimension of the input tensor. A crafted model passed onnx.checker.check_model() and caused SIGSEGV when passed to onnx.shape_inference.infer_shapes().

Technical details

For an input shape such as [1, 1, 5], shape inference computes one spatial input dimension. A weight shape such as [1, 3] has no spatial dimensions, so deriving kernel_shape from dimensions 2 onward produces an empty vector. The output loop nevertheless accesses effective_kernel_shape[0].

The defect affected both the older convTransposeShapeInference_opset11() path and the then-current ConvTranspose inference implementation.

Impact

An application that performs shape inference on an untrusted model can be terminated, causing denial of service. The observed result was a deterministic process crash. No confidentiality or integrity impact was demonstrated.

Affected versions

• Affected: ONNX 1.2.1 through 1.21.0
• Patched: ONNX 1.22.0

Remediation

Upgrade to ONNX 1.22.0 or later. The fix rejects input and weight tensors with fewer than three dimensions before deriving the spatial kernel shape.

• Fix: onnx/onnx#7997
• Commit: 2ec288908

The original malformed model was re-tested against the published ONNX 1.23.2 wheel and correctly raises InferenceError.

Severity

The vulnerable ONNX component processes the malicious model through a local library API or file boundary and is not itself bound to a network stack. Applications that expose ONNX model processing over a network should calculate an Environmental score with Modified Attack Vector set to Network.