Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.3.0 until 1.22.0, onnx.version_converter.convert_version() can perform an out-of-bounds read in Gemm_7_6::adapt_gemm_7_6() in onnx/version_converter/adapters/gemm_7_6.h when a Gemm node has input tensors with fewer than two dimensions because B_shape[1], A_shape[0], or A_shape[1] is accessed without a rank check, potentially causing a process crash during an opset 7 to 6 downgrade. This issue is fixed in version 1.22.0.

Affected: PyPI: onnx

### Summary

Heap-buffer-overflow READ (16 bytes) in `Gemm_7_6::adapt_gemm_7_6()` (`onnx/version_converter/adapters/gemm_7_6.h:41`) when `ConvertVersion()` processes a model with a Gemm node whose input tensors have fewer than 2 dimensions. The adapter accesses `B_shape[1]` without checking rank. On Release builds the OOB read is silent; ASan confirms 16-byte read past a 48-byte allocation.

### Details

The Gemm 7→6 downgrade adapter reads input shapes without bounds checking: ```cpp // gemm_7_6.h:26-42 const auto& A_shape = inputs[0]->sizes(); // May have < 2 elements const auto& B_shape = inputs[1]->sizes(); // May have < 2 elements

if (node->hasAttribute(ktransB) && node->i(ktransB) == 1) { MN.emplace_back(B_shape[0]); // OOB if B has 0 dims } else { MN.emplace_back(B_shape[1]); // OOB if B has < 2 dims ← CRASH } ```

The PoC has input B with shape `[28]` (1 dimension). `B_shape` has 1 element. Accessing `B_shape[1]` reads 16 bytes past the `std::vector<Dimension>` internal storage into adjacent heap memory.

The same unchecked pattern applies to `A_shape[0]` and `A_shape[1]` at lines 34 and 36.

**Entry point:** `onnx.version_converter.convert_version(model, 6)` — different from the `InferShapes` bugs reported in separate advisories. This triggers during opset downgrade (7→6).

### PoC ```python import base64 import onnx from onnx import version_converter

poc_b64 = "CAM6rwEKUQoBQQoBQgoBQRIBWSIER2VtbSoPCgVhbHBoYRUBAQA+oAEBKg4KBGJldGEVAAAAOqABASoNCgZ0dGZsc…

Affected: PyPI: onnx

### Summary

Heap-buffer-overflow READ (16 bytes) in `Gemm_7_6::adapt_gemm_7_6()` (`onnx/version_converter/adapters/gemm_7_6.h:41`) when `ConvertVersion()` processes a model with a Gemm node whose input tensors have fewer than 2 dimensions. The adapter accesses `B_shape[1]` without checking rank. On Release builds the OOB read is silent; ASan confirms 16-byte read past a 48-byte allocation.

### Details

The Gemm 7→6 downgrade adapter reads input shapes without bounds checking: ```cpp // gemm_7_6.h:26-42 const auto& A_shape = inputs[0]->sizes(); // May have < 2 elements const auto& B_shape = inputs[1]->sizes(); // May have < 2 elements

if (node->hasAttribute(ktransB) && node->i(ktransB) == 1) { MN.emplace_back(B_shape[0]); // OOB if B has 0 dims } else { MN.emplace_back(B_shape[1]); // OOB if B has < 2 dims ← CRASH } ```

The PoC has input B with shape `[28]` (1 dimension). `B_shape` has 1 element. Accessing `B_shape[1]` reads 16 bytes past the `std::vector<Dimension>` internal storage into adjacent heap memory.

The same unchecked pattern applies to `A_shape[0]` and `A_shape[1]` at lines 34 and 36.

**Entry point:** `onnx.version_converter.convert_version(model, 6)` — different from the `InferShapes` bugs reported in separate advisories. This triggers during opset downgrade (7→6).

### PoC ```python import base64 import onnx from onnx import version_converter

poc_b64 = "CAM6rwEKUQoBQQoBQgoBQRIBWSIER2VtbSoPCgVhbHBoYRUBAQA+oAEBKg4KBGJldGEVAAAAOqABASoNCgZ0dGZsc…

Affected: Ubuntu:22.04:LTS: onnx, Ubuntu:Pro:24.04:LTS: onnx, Ubuntu:26.04:LTS: onnx

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.3.0 until 1.22.0, onnx.version_converter.convert_version() can perform an out-of-bounds read in Gemm_7_6::adapt_gemm_7_6() in onnx/version_converter/adapters/gemm_7_6.h when a Gemm node has input tensors with fewer than two dimensions because B_shape[1], A_shape[0], or A_shape[1] is accessed without a rank check, potentially causing a process crash during an opset 7 to 6 downgrade. This issue is fixed in version 1.22.0.