CVE-2026-115921 · GHSA-m4pr-6v3c-6fgr
ONNX: Null Pointer Dereference in ScanInferenceFunction Due to Missing Required Attribute
- Risk score
- 43/100
- CVSS
- 6.2 v3.1
- Exploit chance (30 days)
- 0.1%
- Published
- 26 Oct 2026
Open in the live forecast Today's forecast
What to know
- What it is
- Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.4.0 until 1.22.0, ScanInferenceFunction, ScanInferenceFunction_opset8, and ScanInferenceFunction_opset9 do not safely validate the required num_scan_inputs attribute. A missing attribute is dereferenced as a null poin…
- Affected
- onnx (pip) >= 1.4.0, <= 1.21.0
- Fix
- Upgrade onnx to 1.22.0 or later.
- Exploitation
- No known exploitation
Story map
At a glance
onnx: Yellow, plan it in. 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…
The flaws
- CVE-2026-115921: ONNX: Null Pointer Dereference in ScanInferenceFunction Due to Missing Required Attribute (CVSS 6.2 · EPSS 0%)
- CVE-2026-115922: ONNX: Denial of service via unchecked expansion of packed sub-byte tensors in ONNX Python APIs (CVSS 6.2 · EPSS 0%)
- CVE-2026-115919: ONNX: Out-of-Bounds Read in RNN/GRU/LSTM Shape Inference (Old Opsets, Missing Rank Check) (CVSS 6.2 · EPSS 0%)
- CVE-2026-115920: ONNX: Out-of-bounds read in ConvTranspose shape inference via undersized weight rank (CVSS 6.2 · EPSS 0%)
The fix
Upgrade onnx: 1.22.0 or later on your release line
Who is exposed: onnx (pip) <= 1.21.0
Where it stands
- No known exploitation
Connects to
- Same weakness: CWE-195 (CWE-195). 8 other warnings in 30 days
Why it is Yellow
- CVSS 6.2 medium
- On your watchlist: PyPI
Affected packages
| Package | Ecosystem | Vulnerable | Fixed in |
|---|---|---|---|
| onnx | pip | >= 1.4.0, <= 1.21.0 | 1.22.0 |
References
- https://github.com/onnx/onnx/commit/dd6bc92ea39fd2274fd671f0734b0b8b558356f5
- https://github.com/onnx/onnx/pull/7916
- https://github.com/onnx/onnx/releases/tag/v1.22.0
- https://github.com/onnx/onnx/security/advisories/GHSA-m4pr-6v3c-6fgr
- https://github.com/advisories/GHSA-m4pr-6v3c-6fgr
- https://nvd.nist.gov/vuln/detail/CVE-2026-115921
- https://github.com/advisories/GHSA-m4pr-6v3c-6fgr
Original description
Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.4.0 until 1.22.0, ScanInferenceFunction, ScanInferenceFunction_opset8, and ScanInferenceFunction_opset9 do not safely validate the required num_scan_inputs attribute. A missing attribute is dereferenced as a null pointer, while a negative value is converted to size_t and can reach an exceptional oversized allocation path. An untrusted model can therefore terminate or disrupt shape inference, including a negative-value model that can pass onnx.checker.check_model(). This issue is fixed in version 1.22.0.
Last activity 28 Oct 2026. Patch Forecast updates every hour.