GHSA-53Q9-R3PM-6PQ6
Vulnerability from github – Published: 2025-04-18 15:19 – Updated: 2025-08-29 19:45
VLAI
Summary
PyTorch: `torch.load` with `weights_only=True` leads to remote code execution
Details
Description
I found a Remote Command Execution (RCE) vulnerability in PyTorch. When loading model using torch.load with weights_only=True, it can still achieve RCE.
Background knowledge
https://github.com/pytorch/pytorch/security
As you can see, the PyTorch official documentation considers using torch.load() with weights_only=True to be safe.
Since everyone knows that weights_only=False is unsafe, so they will use the weights_only=True to mitigate the seucirty issue.
But now, I just proved that even if you use weights_only=True, it can still achieve RCE.
Credit
This vulnerability was found by Ji'an Zhou.
Severity
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "torch"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.6.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2025-32434"
],
"database_specific": {
"cwe_ids": [
"CWE-502"
],
"github_reviewed": true,
"github_reviewed_at": "2025-04-18T15:19:28Z",
"nvd_published_at": "2025-04-18T16:15:23Z",
"severity": "CRITICAL"
},
"details": "# Description\nI found a Remote Command Execution (RCE) vulnerability in PyTorch. When loading model using torch.load with weights_only=True, it can still achieve RCE. \n\n# Background knowledge\nhttps://github.com/pytorch/pytorch/security \n As you can see, the PyTorch official documentation considers using `torch.load()` with `weights_only=True` to be safe.\n\nSince everyone knows that weights_only=False is unsafe, so they will use the weights_only=True to mitigate the seucirty issue.\nBut now, I just proved that even if you use weights_only=True, it can still achieve RCE.\n\n# Credit\nThis vulnerability was found by Ji\u0027an Zhou.",
"id": "GHSA-53q9-r3pm-6pq6",
"modified": "2025-08-29T19:45:46Z",
"published": "2025-04-18T15:19:28Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/pytorch/pytorch/security/advisories/GHSA-53q9-r3pm-6pq6"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-32434"
},
{
"type": "WEB",
"url": "https://github.com/pytorch/pytorch/commit/8d4b8a920a2172523deb95bf20e8e52d50649c04"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/torch/PYSEC-2025-41.yaml"
},
{
"type": "PACKAGE",
"url": "https://github.com/pytorch/pytorch"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "PyTorch: `torch.load` with `weights_only=True` leads to remote code execution"
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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