GHSA-CFFC-MXRF-MHH4
Vulnerability from github – Published: 2025-12-29 22:44 – Updated: 2025-12-29 22:44
VLAI?
Summary
Picklescan is vulnerable to RCE via missing detection when calling numpy.f2py.crackfortran.param_eval
Details
Summary
Picklescan uses numpy.f2py.crackfortran.param_eval, 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.param_eval function via 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 param_eval
return (param_eval,("os.system('ls')",None,None,None))
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-502",
"CWE-94"
],
"github_reviewed": true,
"github_reviewed_at": "2025-12-29T22:44:26Z",
"nvd_published_at": null,
"severity": "MODERATE"
},
"details": "### Summary\nPicklescan uses numpy.f2py.crackfortran.param_eval, 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.param_eval function via 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\n### PoC\n```\nclass RCE:\n def __reduce__(self):\n from numpy.f2py.crackfortran import param_eval\n return (param_eval,(\"os.system(\u0027ls\u0027)\",None,None,None))\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-cffc-mxrf-mhh4",
"modified": "2025-12-29T22:44:27Z",
"published": "2025-12-29T22:44:26Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/mmaitre314/picklescan/security/advisories/GHSA-cffc-mxrf-mhh4"
},
{
"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"
},
{
"type": "WEB",
"url": "https://github.com/mmaitre314/picklescan/releases/tag/v0.0.33"
}
],
"schema_version": "1.4.0",
"severity": [],
"summary": "Picklescan is vulnerable to RCE via missing detection when calling numpy.f2py.crackfortran.param_eval"
}
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Sightings
| Author | Source | Type | Date |
|---|
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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