ghsa-3329-ghmp-jmv5
Vulnerability from github
Summary
Picklescan uses numpy.f2py.crackfortran.myeval, which is a function in numpy to execute remote pickle files.
Details
The attack payload executes in the following steps:
- First, the attacker crafts the payload by calling the numpy.f2py.crackfortran.myeval function in its reduce method
- Then, when the victim checks whether the pickle file is safe by using the Picklescan library and this library doesn't detect any dangerous functions, they decide to use pickle.load() on this malicious pickle file, thus leading to remote code execution.
PoC
class RCE:
def __reduce__(self):
from numpy.f2py.crackfortran import myeval
return (myeval, ("os.system('ls')",))
Impact
Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models. Attackers can embed malicious code in pickle file that remains undetected but executes when the pickle file is loaded. Attackers can distribute infected pickle files across ML models, APIs, or saved Python objects.
Report by
Pinji Chen (cpj24@mails.tsinghua.edu.cn) from the NISL lab (https://netsec.ccert.edu.cn/about) at Tsinghua University, Guanheng Liu (coolwind326@gmail.com).
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "picklescan"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "0.0.33"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": true,
"github_reviewed_at": "2025-12-29T20:04:09Z",
"nvd_published_at": null,
"severity": "HIGH"
},
"details": "### Summary\nPicklescan uses numpy.f2py.crackfortran.myeval, which is a function in numpy to execute remote pickle files.\n\n### Details\nThe attack payload executes in the following steps:\n\n- First, the attacker crafts the payload by calling the numpy.f2py.crackfortran.myeval function in its reduce method\n- Then, when the victim checks whether the pickle file is safe by using the Picklescan library and this library doesn\u0027t detect any dangerous functions, they decide to use pickle.load() on this malicious pickle file, thus leading to remote code execution.\n\n### PoC\n```\nclass RCE:\n def __reduce__(self):\n from numpy.f2py.crackfortran import myeval\n return (myeval, (\"os.system(\u0027ls\u0027)\",))\n```\n\n\n### Impact\nAny organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models.\nAttackers can embed malicious code in pickle file that remains undetected but executes when the pickle file is loaded.\nAttackers can distribute infected pickle files across ML models, APIs, or saved Python objects.\n\n### Report by\nPinji Chen (cpj24@mails.tsinghua.edu.cn) from the NISL lab (https://netsec.ccert.edu.cn/about) at Tsinghua University, Guanheng Liu (coolwind326@gmail.com).",
"id": "GHSA-3329-ghmp-jmv5",
"modified": "2025-12-29T20:04:09Z",
"published": "2025-12-29T20:04:09Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/mmaitre314/picklescan/security/advisories/GHSA-3329-ghmp-jmv5"
},
{
"type": "WEB",
"url": "https://github.com/mmaitre314/picklescan/pull/53"
},
{
"type": "WEB",
"url": "https://github.com/mmaitre314/picklescan/commit/70c1c6c31beb6baaf52c8db1b6c3c0e84a6f9dab"
},
{
"type": "PACKAGE",
"url": "https://github.com/mmaitre314/picklescan"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:P",
"type": "CVSS_V4"
}
],
"summary": "Picklescan is vulnerable to RCE through missing detection when calling numpy.f2py.crackfortran.myeval"
}
Sightings
| Author | Source | Type | Date |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or seen somewhere by the user.
- Confirmed: The vulnerability is confirmed from an analyst perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: This vulnerability was exploited and seen by the user reporting the sighting.
- Patched: This vulnerability was successfully patched by the user reporting the sighting.
- Not exploited: This vulnerability was not exploited or seen by the user reporting the sighting.
- Not confirmed: The user expresses doubt about the veracity of the vulnerability.
- Not patched: This vulnerability was not successfully patched by the user reporting the sighting.