CVE-2025-71365

picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded.

picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded.

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

Duplicate Advisory

This advisory has been withdrawn because it is a duplicate of GHSA-3329-ghmp-jmv5. This link is maintained to preserve external references.

Original Description

picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded.

picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded.

picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded.

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

Duplicate Advisory

This advisory has been withdrawn because it is a duplicate of GHSA-3329-ghmp-jmv5. This link is maintained to preserve external references.

Original Description

picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded.

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