CVE-2025-71342

picklescan before 0.0.30 fails to detect malicious pickle files using idlelib.run.Executive.runcode in reduce methods. Attackers can embed undetected code in pickle files that executes during pickle.load, enabling remote code execution in PyTorch models and supply chain attacks.

picklescan before 0.0.30 fails to detect malicious pickle files using idlelib.run.Executive.runcode in reduce methods. Attackers can embed undetected code in pickle files that executes during pickle.load, enabling remote code execution in PyTorch models and supply chain attacks.

picklescan before 0.0.30 fails to detect malicious pickle files using idlelib.run.Executive.runcode in reduce methods. Attackers can embed undetected code in pickle files that executes during pickle.load, enabling remote code execution in PyTorch models and supply chain attacks.

Summary

Using idlelib.run.Executive.runcode function, which is a built-in python library function to execute remote pickle file.

Details

The attack payload executes in the following steps:

First, the attacker craft the payload by calling to idlelib.run.Executive.runcode function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution.

PoC

from idlelib.run import Executive
from types import SimpleNamespace

class EvilIdlelibRunExecutiveRuncode:
    def __reduce__(self):
        payload = "__import__('os').system('whoami')"
        fake_self = SimpleNamespace(locals={})
        return Executive.runcode, (fake_self, payload)

Impact

Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models. What is the impact? Attackers can embed malicious code in pickle file that remains undetected but executes when the pickle file is loaded. Supply Chain Attack: Attackers can distribute infected pickle files across ML models, APIs, or saved Python objects.

Corresponding

https://github.com/FredericDT https://github.com/Qhaoduoyu

picklescan before 0.0.30 fails to detect malicious pickle files using idlelib.run.Executive.runcode in reduce methods. Attackers can embed undetected code in pickle files that executes during pickle.load, enabling remote code execution in PyTorch models and supply chain attacks.

picklescan before 0.0.30 fails to detect malicious pickle files using idlelib.run.Executive.runcode in reduce methods. Attackers can embed undetected code in pickle files that executes during pickle.load, enabling remote code execution in PyTorch models and supply chain attacks.

Summary

Using idlelib.run.Executive.runcode function, which is a built-in python library function to execute remote pickle file.

Details

The attack payload executes in the following steps:

First, the attacker craft the payload by calling to idlelib.run.Executive.runcode function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution.

PoC

from idlelib.run import Executive
from types import SimpleNamespace

class EvilIdlelibRunExecutiveRuncode:
    def __reduce__(self):
        payload = "__import__('os').system('whoami')"
        fake_self = SimpleNamespace(locals={})
        return Executive.runcode, (fake_self, payload)

Impact

Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models. What is the impact? Attackers can embed malicious code in pickle file that remains undetected but executes when the pickle file is loaded. Supply Chain Attack: Attackers can distribute infected pickle files across ML models, APIs, or saved Python objects.

Corresponding

https://github.com/FredericDT https://github.com/Qhaoduoyu