CVE-2026-28500CRITICAL 9.1EPSS p16.8%

CVE-2026-28500CVE-2026-28500

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.

Scoring

CVSS 3.19.1 (CRITICAL)
VectorCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N
EPSS0.26% probability of exploitation · percentile 16.8% · 2026-06-19T12:03:05Z
Published2026-03-18
Last modified2026-03-18

Underlying weaknesses· 3

CWE-345CWE-494CWE-693

References

  1. https://github.com/ZeroXJacks/CVEs/blob/main/2026/CVE-2026-28500.md
  2. https://github.com/onnx/onnx/security/advisories/GHSA-hqmj-h5c6-369m
  3. https://github.com/ZeroXJacks/CVEs/blob/main/2026/CVE-2026-28500.md

3

TypeTargetConfidenceTier
WeaknessInsufficient Verification of Data Authenticitycwe-3450%live
WeaknessDownload of Code Without Integrity Checkcwe-4940%live
WeaknessProtection Mechanism Failurecwe-6930%live

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Sourced from NVD + FIRST.org EPSS. Curated for EU compliance use cases by Adam Lundqvist, Founder at SQUR.