GHSA-6pwm-x34p-fvj2: Medium severity pip/onnx vulnerability

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 kernelshape attribute.

The inference function derived an empty effectivekernelshape 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.checkmodel() and caused SIGSEGV when passed to onnx.shapeinference.infershapes().

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 kernelshape from dimensions 2 onward produces an empty vector. The output loop nevertheless accesses effectivekernelshape[0].

The defect affected both the older convTransposeShapeInferenceopset11() 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.