CVE-2026-115919: onnx · Out-of-bounds Read
Affects onnxSource CVE Database V5
Not known to be exploited · CVSS 6.2 Medium : upgrade to 1.22.0 when convenient.
Data as of 26 Oct 2026, 19:57 UTC
- Patch
- Available – fixed in 1.22.0
CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HExploitation evidence
Disclosed: evidence found.
CVE published 26 Oct 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.
What to do
Upgrade onnx to 1.22.0.
Affected software
| Product | Affected | Fixed in |
|---|---|---|
| onnxonnx | Affected
| Fixed in
|
PyPIonnxpkg:pypi/onnxOther threats | Affected
| Fixed in
|
Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.
AI-suggested
Upgrade ONNX to version 1.22.0 or later, where this vulnerability is fixed. No other mitigation is required as the issue is resolved in the official patch.
Description
Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to 1.22.0, RNNShapeInference_opset1_to_6() processes malformed RNN and LSTM nodes from opsets 1 through 6 and GRU nodes from opsets 3 through 6 without verifying that the first input has rank three. The function accesses dim(0) and dim(1) on an undersized protobuf shape, which can read outside the intended repeated field and terminate an application when onnx.shape_inference.infer_shapes() processes an untrusted model that can pass onnx.checker.check_model(). This issue is fixed in version 1.22.0.