CVE-2026-115919

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.

ONNX versions from 1.4.0 until 1.22.0 do not safely validate the required num_scan_inputs attribute in ScanInferenceFunction, ScanInferenceFunction_opset8, and ScanInferenceFunction_opset9. A missing attribute is dereferenced as a null pointer, while a negative value is converted to size_t and can reach an oversized allocation path. An untrusted model can terminate or disrupt shape inference, including a negative-value model that passes onnx.checker.check_model().

ONNX versions from 1.2.1 until 1.22.0, during ConvTranspose shape inference, process a weight tensor with fewer than three dimensions without a kernel_shape attribute by deriving an empty effective_kernel_shape and indexing it for spatial dimensions. An untrusted model that passes onnx.checker.check_model() can crash onnx.shape_inference.infer_shapes(), causing denial of service without confidentiality or integrity impact.

ONNX before version 1.21.0 contains a vulnerability in the 'save_external_data' function that builds the external-data file path and opens it for writing without O_NOFOLLOW/O_EXCL, after a non-atomic 'os.path.isfile()' check. A local attacker can pre-plant a symlink, causing the victim's write to append to any file the victim can write.

A vulnerability in the ONNX library from version 1.3.0 to 1.22.0 allows an out-of-bounds read in Gemm_7_6::adapt_gemm_7_6() during opset 7 to 6 downgrade when a Gemm node has input tensors with fewer than two dimensions. This can cause a process crash.

A vulnerability in the ONNX library (versions 1.9.0 through 1.22.0) allows a null pointer dereference in the Upsample_6_7::adapt_upsample_6_7() function when processing a malicious model with an Upsample node having zero inputs. This leads to an unrecoverable denial of service (DoS).

A path traversal vulnerability via symlink was found in the ONNX library before version 1.21.0. It allows reading arbitrary files outside the model or user-provided directory.

In ONNX library versions up to 1.20.1, a security control bypass exists in onnx.hub.load() due to improper logic in repository trust verification. Using the silent=True parameter suppresses all security warnings, enabling zero-interaction supply-chain attacks.