CVE-2025-71347

picklescan before 0.0.33 fails to detect malicious pickle files using numpy.f2py.crackfortran.param_eval function in reduce methods, allowing attackers to bypass security checks. Remote attackers can embed undetected code in pickle files that executes during deserialization, enabling arbitrary code execution in applications loading untrusted pickle data.

picklescan before 0.0.33 fails to detect malicious pickle files using numpy.f2py.crackfortran.param_eval function in reduce methods, allowing attackers to bypass security checks. Remote attackers can embed undetected code in pickle files that executes during deserialization, enabling arbitrary code execution in applications loading untrusted pickle data.

picklescan before 0.0.33 fails to detect malicious pickle files using numpy.f2py.crackfortran.param_eval function in reduce methods, allowing attackers to bypass security checks. Remote attackers can embed undetected code in pickle files that executes during deserialization, enabling arbitrary code execution in applications loading untrusted pickle data.

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 ([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 malicious pickle files using numpy.f2py.crackfortran.param_eval function in reduce methods, allowing attackers to bypass security checks. Remote attackers can embed undetected code in pickle files that executes during deserialization, enabling arbitrary code execution in applications loading untrusted pickle data.

picklescan before 0.0.33 fails to detect malicious pickle files using numpy.f2py.crackfortran.param_eval function in reduce methods, allowing attackers to bypass security checks. Remote attackers can embed undetected code in pickle files that executes during deserialization, enabling arbitrary code execution in applications loading untrusted pickle data.

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