CVE-2026-115922

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to 1.22.0, _unpack_2bit() and _unpack_4bit() resize unpacked arrays according to attacker-controlled TensorProto.dims without verifying that raw_data or int32_data encodes the declared number of elements. Materializing an untrusted UINT2, INT2, UINT4, INT4, or FLOAT4E2M1 tensor through onnx.numpy_helper.to_array() or onnx.reference.ReferenceEvaluator can therefore expand a small packed payload into a disproportionately large allocation and exhaust memory. This issue is fixed in version 1.22.0.

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to 1.22.0, _unpack_2bit() and _unpack_4bit() resize unpacked arrays according to attacker-controlled TensorProto.dims without verifying that raw_data or int32_data encodes the declared number of elements. Materializing an untrusted UINT2, INT2, UINT4, INT4, or FLOAT4E2M1 tensor through onnx.numpy_helper.to_array() or onnx.reference.ReferenceEvaluator can therefore expand a small packed payload into a disproportionately large allocation and exhaust memory. This issue is fixed in version 1.22.0.

Summary

ONNX's Python tensor-materialization helpers could allocate memory according to attacker-controlled tensor dimensions without verifying that a packed sub-byte payload contained the declared number of elements.

A very small model could therefore cause a disproportionately large allocation when an application called onnx.numpy_helper.to_array() or constructed onnx.reference.ReferenceEvaluator with the model. The issue affected UINT2, INT2, UINT4, INT4, and FLOAT4E2M1 tensors stored in either raw_data or packed int32_data.

Technical details

The vulnerable _unpack_2bit() and _unpack_4bit() paths first allocated a small array based on the available packed bytes, then resized it to the shape declared in TensorProto.dims. They did not first confirm that the payload encoded enough logical elements for that shape.

For example, the reporter demonstrated a 66-byte model containing one packed byte and a declared dimension of 700,000,000. The model passed onnx.checker.check_model() and allocated approximately 700 MB while materializing the initializer, followed by another large allocation when executing the reference evaluator.

Loading, checking, or running shape inference on the serialized model alone did not trigger the allocation. The vulnerable tensor had to be materialized.

Impact

An application that materializes packed tensors from an untrusted model can exhaust available memory, causing denial of service. Memory corruption and code execution were not demonstrated.

Affected versions

• Affected: ONNX through 1.21.0
• Patched: ONNX 1.22.0

Remediation

Upgrade to ONNX 1.22.0 or later.

The primary fix validates the logical element count before resizing the unpacked NumPy array. A complementary checker change rejects an undersized packed payload before materialization.

• Materialization fix: onnx/onnx#8033
• Checker defense-in-depth: onnx/onnx#8038

The five affected tensor types and both storage formats were re-tested against the published ONNX 1.23.2 wheel and are rejected before the oversized allocation.

To verify the behavior using a released vulnerable package in an isolated environment, install onnx==1.21.0 with python -m pip install "onnx==1.21.0". Run memory-exhaustion demonstrations only with an appropriate process memory limit.

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.

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to 1.22.0, _unpack_2bit() and _unpack_4bit() resize unpacked arrays according to attacker-controlled TensorProto.dims without verifying that raw_data or int32_data encodes the declared number of elements. Materializing an untrusted UINT2, INT2, UINT4, INT4, or FLOAT4E2M1 tensor through onnx.numpy_helper.to_array() or onnx.reference.ReferenceEvaluator can therefore expand a small packed payload into a disproportionately large allocation and exhaust memory. This issue is fixed in version 1.22.0.

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to 1.22.0, _unpack_2bit() and _unpack_4bit() resize unpacked arrays according to attacker-controlled TensorProto.dims without verifying that raw_data or int32_data encodes the declared number of elements. Materializing an untrusted UINT2, INT2, UINT4, INT4, or FLOAT4E2M1 tensor through onnx.numpy_helper.to_array() or onnx.reference.ReferenceEvaluator can therefore expand a small packed payload into a disproportionately large allocation and exhaust memory. This issue is fixed in version 1.22.0.

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to 1.22.0, _unpack_2bit() and _unpack_4bit() resize unpacked arrays according to attacker-controlled TensorProto.dims without verifying that raw_data or int32_data encodes the declared number of elements. Materializing an untrusted UINT2, INT2, UINT4, INT4, or FLOAT4E2M1 tensor through onnx.numpy_helper.to_array() or onnx.reference.ReferenceEvaluator can therefore expand a small packed payload into a disproportionately large allocation and exhaust memory. This issue is fixed in version 1.22.0.

Summary

ONNX's Python tensor-materialization helpers could allocate memory according to attacker-controlled tensor dimensions without verifying that a packed sub-byte payload contained the declared number of elements.

A very small model could therefore cause a disproportionately large allocation when an application called onnx.numpy_helper.to_array() or constructed onnx.reference.ReferenceEvaluator with the model. The issue affected UINT2, INT2, UINT4, INT4, and FLOAT4E2M1 tensors stored in either raw_data or packed int32_data.

Technical details

The vulnerable _unpack_2bit() and _unpack_4bit() paths first allocated a small array based on the available packed bytes, then resized it to the shape declared in TensorProto.dims. They did not first confirm that the payload encoded enough logical elements for that shape.

For example, the reporter demonstrated a 66-byte model containing one packed byte and a declared dimension of 700,000,000. The model passed onnx.checker.check_model() and allocated approximately 700 MB while materializing the initializer, followed by another large allocation when executing the reference evaluator.

Loading, checking, or running shape inference on the serialized model alone did not trigger the allocation. The vulnerable tensor had to be materialized.

Impact

An application that materializes packed tensors from an untrusted model can exhaust available memory, causing denial of service. Memory corruption and code execution were not demonstrated.

Affected versions

• Affected: ONNX through 1.21.0
• Patched: ONNX 1.22.0

Remediation

Upgrade to ONNX 1.22.0 or later.

The primary fix validates the logical element count before resizing the unpacked NumPy array. A complementary checker change rejects an undersized packed payload before materialization.

• Materialization fix: onnx/onnx#8033
• Checker defense-in-depth: onnx/onnx#8038

The five affected tensor types and both storage formats were re-tested against the published ONNX 1.23.2 wheel and are rejected before the oversized allocation.

To verify the behavior using a released vulnerable package in an isolated environment, install onnx==1.21.0 with python -m pip install "onnx==1.21.0". Run memory-exhaustion demonstrations only with an appropriate process memory limit.

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.

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to 1.22.0, _unpack_2bit() and _unpack_4bit() resize unpacked arrays according to attacker-controlled TensorProto.dims without verifying that raw_data or int32_data encodes the declared number of elements. Materializing an untrusted UINT2, INT2, UINT4, INT4, or FLOAT4E2M1 tensor through onnx.numpy_helper.to_array() or onnx.reference.ReferenceEvaluator can therefore expand a small packed payload into a disproportionately large allocation and exhaust memory. This issue is fixed in version 1.22.0.