onnx: Insufficient Verification of Data Authenticity
CVE-2026-28500 is a critical-severity insufficient verification of data authenticity vulnerability in onnx, with a CVSS 3.1 base score of 9.1 out of 10. It can be exploited remotely over the network by an unauthenticated attacker, without any action from a user. Affected onnx versions: <= 1.20.1. The record does not name a fixed version, so check the vendor advisory. There is no confirmed exploitation so far. EPSS estimates a 0.35% chance of exploitation within 30 days, which is low compared with other CVEs.
Critical severity: a successful attack would have severe impact.
What to do
- Check which version of onnx you run against the affected versions below, and install the vendor's latest release.
- Read the advisory on github.com for workarounds and exact fixed builds.
- Until you can update, restrict network access to the affected service so it is not reachable from untrusted networks.
Affected products and versions
| Vendor | Product | Affected versions | Fixed in |
|---|---|---|---|
| Onnx | onnx | <= 1.20.1 | Not stated |
| Red Hat | Red Hat OpenShift AI 2.25 | Not specified in the record | Not stated |
| Red Hat | Red Hat OpenShift AI (RHOAI) | Not specified in the record | Not stated |
Version data comes from the organisation that assigned the CVE. Confirm against the vendor advisory before deciding you are unaffected.
How the severity score breaks down
- Attack vector
- NetworkCan be exploited remotely over a network, including the internet.
- Attack complexity
- LowNo special conditions are needed; the attack works reliably.
- Privileges required
- NoneNo account or login is needed.
- User interaction
- NoneNo action by a victim is required.
- Scope
- UnchangedImpact stays within the vulnerable component.
- Confidentiality
- HighAll data handled by the component can be read.
- Integrity
- HighData or code can be modified at will.
- Availability
- NoneAvailability is not affected.
Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N. Scored by NVD.
| Scoring system | Score | Severity | Scored by |
|---|---|---|---|
| CVSS 3.1 | 8.6 | High | CNA (github.com) |
| CVSS 3.1 | 9.1 | Critical | NVD |
| CVSS 3.1 | 8.6 | High | CNA |
Official description
Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. In versions up to and including 1.20.1, a security control bypass exists in onnx.hub.load() due to improper logic in the repository trust verification mechanism. While the function is designed to warn users when loading models from non-official sources, the use of the silent=True parameter completely suppresses all security warnings and confirmation prompts. This vulnerability transforms a standard model-loading function into a vector for Zero-Interaction Supply-Chain Attacks. When chained with file-system vulnerabilities, an attacker can silently exfiltrate sensitive files (SSH keys, cloud credentials) from the victim's machine the moment the model is loaded. As of time of publication, no known patched versions are available.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Insufficient Verification of Data Authenticity
The product does not sufficiently verify the origin or authenticity of data, in a way that causes it to accept invalid data.
More Insufficient Verification of Data Authenticity vulnerabilities (CWE-345)
Timeline
- CVE record published.
- CISA published its SSVC assessment (exploitation: poc, automatable: yes, technical impact: partial).
- Record last changed materially (score, affected products, status or references).
References
Patches and release notes
Other references
Other vulnerabilities in onnx
- 7.1 High severity
- 8.6 High severity
- 7.5 High severity
- 9.1 Critical severity
- 9.1 Critical severity
- 7.5 High severity
Recent Insufficient Verification of Data Authenticity vulnerabilities
- 7.2 High severity
- 7.5 High severity
- 8.6 High severity
- 8.8 High severity
- 9.8 Critical severity
- 9.3 Critical severity
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