CVE-2026-28500

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.

Attacker registers a plausible-looking model on a public ONNX model hub (e.g., `org/bert-finetuned-security-v2`), embedding a malicious custom operator that reads `~/.ssh/id_rsa`, `~/.aws/credentials`, and `~/.config/gcloud/` on load and POSTs them to an attacker-controlled endpoint. Attacker then promotes the model via a GitHub README, a benchmark leaderboard entry, or a poisoned documentation PR. A developer or automated pipeline calls `onnx.hub.load('org/bert-finetuned-security-v2', silent=True)` — the `silent=True` flag suppresses the non-official-source trust warning entirely. Model loads, malicious operator executes, credentials exfiltrated in under 500ms. No exception raised, no log entry, pipeline continues normally. Attacker uses exfiltrated AWS credentials to pivot into cloud infrastructure.