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PYSEC-2026-4131
picklescanSummary
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).
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The vulnerability can be exploited over the network without needing physical access. It is easy for an attacker to exploit this vulnerability. An attacker does not need any special privileges or access rights. The attacker needs the user to perform some action, like clicking a link. The impact is confined to the system where the vulnerability exists. There is a high impact on the confidentiality of the information. There is a high impact on the integrity of the data.
Exploitation activity has been observed. Apply available patches or mitigations urgently.
The exploit probability is very low. The vulnerability is unlikely to be exploited in the next 30 days.
We did not find any exploit available. Neither in GitHub repositories nor in the Exploit-Database.
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