https://cve.radiocsirt.org Contains only the most 10 recent entries. http://www.rssboard.org/rss-specification python-feedgen en Fri, 02 Oct 2026 13:01:31 +0000 https://cve.radiocsirt.org/vuln/euvd-2026-337385 EUVD-2026-337385 https://cve.radiocsirt.org/vuln/euvd-2026-337385 https://cve.radiocsirt.org/vuln/fkie_cve-2026-28500 <p>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&#39;s machine the moment the model is loaded. As of time of publication, no known patched versions are available.</p> <p>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&#39;s machine the moment the model is loaded. As of time of publication, no known patched versions are available.</p> https://cve.radiocsirt.org/vuln/fkie_cve-2026-28500 https://cve.radiocsirt.org/vuln/ghsa-hqmj-h5c6-369m <p><strong>Affected:</strong> PyPI: onnx</p> <p>## What&#39;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(&#34;The model repo... is not trusted&#34;) if input().lower() != &#34;y&#34;: 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.</p> <p>## 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)</p> <p><strong>Affected:</strong> PyPI: onnx</p> <p>## What&#39;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(&#34;The model repo... is not trusted&#34;) if input().lower() != &#34;y&#34;: 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.</p> <p>## 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)</p> https://cve.radiocsirt.org/vuln/ghsa-hqmj-h5c6-369m https://cve.radiocsirt.org/vuln/pysec-2026-103 <p><strong>Affected:</strong> PyPI: onnx</p> <p>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&#39;s machine the moment the model is loaded. As of time of publication, no known patched versions are available.</p> <p><strong>Affected:</strong> PyPI: onnx</p> <p>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&#39;s machine the moment the model is loaded. As of time of publication, no known patched versions are available.</p> https://cve.radiocsirt.org/vuln/pysec-2026-103 https://cve.radiocsirt.org/vuln/rhsa-2026:24977 <p>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…</p> <p>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…</p> https://cve.radiocsirt.org/vuln/rhsa-2026:24977 https://cve.radiocsirt.org/vuln/ubuntu-cve-2026-28500 <p><strong>Affected:</strong> Ubuntu:22.04:LTS: onnx, Ubuntu:Pro:24.04:LTS: onnx, Ubuntu:25.10: onnx, Ubuntu:26.04:LTS: onnx</p> <p>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&#39;s machine the moment the model is loaded. As of time of publication, no known patched versions are available.</p> <p><strong>Affected:</strong> Ubuntu:22.04:LTS: onnx, Ubuntu:Pro:24.04:LTS: onnx, Ubuntu:25.10: onnx, Ubuntu:26.04:LTS: onnx</p> <p>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&#39;s machine the moment the model is loaded. As of time of publication, no known patched versions are available.</p> https://cve.radiocsirt.org/vuln/ubuntu-cve-2026-28500