{"api_version":"1","generated_at":"2026-10-27T06:26:01+00:00","cve":"CVE-2026-115922","urls":{"html":"https://cve.report/CVE-2026-115922","api":"https://cve.report/api/cve/CVE-2026-115922.json","docs":"https://cve.report/api","cve_org":"https://www.cve.org/CVERecord?id=CVE-2026-115922","nvd":"https://nvd.nist.gov/vuln/detail/CVE-2026-115922"},"summary":{"title":"ONNX: Denial of service via unchecked expansion of packed sub-byte tensors in ONNX Python APIs","description":"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. 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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.","Type":"Description","Title":"ONNX: Denial of service via unchecked expansion of packed sub-by"}]}}}