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.

Affected: PyPI: onnx

## What's the issue Passing `silent=True` to `onnx.hub.load()` kills all trust warnings and user prompts. This means a model can be downloaded from any unverified GitHub repo with zero user awareness. ```python if not _verify_repo_ref(repo) and not silent: # completely skipped when silent=True print("The model repo... is not trusted") if input().lower() != "y": return None ``` On top of that, the SHA256 integrity check is useless here — it validates against a manifest that lives in the same repo the attacker controls, so the hash will always match.

## Impact Any pipeline using `hub.load()` with `silent=True` and an external repo string is silently loading whatever the repo owner ships. If that model executes arbitrary code on load, the attacker has access to the machine. ## Resolved by removing the feature ## References - [Write-up](https://github.com/ZeroXJacks/CVEs/blob/main/2026/CVE-2026-28500.md)

Affected: PyPI: onnx

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.

bouncycastle: BC-JAVA: GOSTCTR implementation unable to process more than 255 blocks correctly vllm: HTTP header size limit not enforced allows Denial of Service from Unauthenticated requests golang: net/url: Memory exhaustion in query parameter parsing in net/url axios: Axios: Server-Side Request Forgery and proxy bypass due to improper hostname normalization aiohttp: aiohttp: Denial of Service via specially crafted POST request aiohttp: aiohttp: Denial of Service via memory exhaustion from crafted POST request keras: Keras: Arbitrary Code Execution Vulnerability Bypassing Safe Mode lodash: lodash: Arbitrary code execution via untrusted input in template imports fast-uri: fast-uri: Path traversal vulnerability allows bypass of security policies pyasn1: pyasn1: Denial of Service due to memory exhaustion from malformed RELATIVE-OID pytorch: PyTorch: Arbitrary code execution via malicious checkpoint file loading xgrammar: xgrammar: Denial of Service via multi-level nested syntax vLLM: vLLM: Server-Side Request Forgery bypass via inconsistent URL parsing onnx: ONNX: Information Disclosure via Path Traversal Vulnerability vllm: vLLM: Remote code execution due to hardcoded trust_remote_code setting onnx: ONNX: Untrusted Model Repository Warnings Suppressed python-dotenv: python-dotenv: Arbitrary file overwrite via symbolic link following immutable-js: Immutable.js: Arbitrary code execution via Prototype Pollution svgo: SVGO: Denial of Service via XML entity expansion tornado-pyth…

Affected: Ubuntu:22.04:LTS: onnx, Ubuntu:Pro:24.04:LTS: onnx, Ubuntu:25.10: onnx, Ubuntu:26.04:LTS: onnx

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.