ONNX up to 1.21.x 2-bit/4-bit Tensor Unpacking _unpack_2bit/_unpack_4bit allocation of resources

Summaryinfo

A vulnerability was found in ONNX up to 1.21.x. It has been declared as problematic. This vulnerability affects the function _unpack_2bit/_unpack_4bit of the component 2-bit/4-bit Tensor Unpacking. Such manipulation leads to allocation of resources. This vulnerability is uniquely identified as CVE-2026-115922. The attack can be launched remotely. No exploit exists. It is recommended to upgrade the affected component.

Detailsinfo

A vulnerability was found in ONNX up to 1.21.x. It has been rated as problematic. Affected by this issue is the function _unpack_2bit/_unpack_4bit of the component 2-bit/4-bit Tensor Unpacking. The manipulation with an unknown input leads to a allocation of resources vulnerability. Using CWE to declare the problem leads to CWE-770. The product allocates a reusable resource or group of resources on behalf of an actor without imposing any restrictions on the size or number of resources that can be allocated, in violation of the intended security policy for that actor. Impacted is availability. CVE summarizes:

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.

The advisory is shared for download at github.com. This vulnerability is handled as CVE-2026-115922 since 10/23/2026. The exploitation is known to be easy. The attack may be launched remotely. No form of authentication is required for exploitation. There are known technical details, but no exploit is available. The MITRE ATT&CK project declares the attack technique as T1499.

Upgrading to version 1.22.0 eliminates this vulnerability.

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Productinfo

Name

• ONNX

Version

• 1.0
• 1.1
• 1.2
• 1.3
• 1.4
• 1.5
• 1.6
• 1.7
• 1.8
• 1.9
• 1.10
• 1.11
• 1.12
• 1.13
• 1.14
• 1.15
• 1.16
• 1.17
• 1.18
• 1.19
• 1.20
• 1.21

Website

• Product: https://github.com/onnx/onnx/

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