CVE-2025-23265
Megatron-LM vulnerable to code injection via malicious file, leading to code execution.
- CVSS 7.8
- CWE-94
- Input Validation and Sanitization
- Remote
NVIDIA Megatron-LM for all platforms contains a vulnerability in a python component where an attacker may cause a code injection issue by providing a malicious file. A successful exploit of this vulnerability may lead to Code Execution, Escalation of Privileges, Information Disclosure and Data Tampering.
- CWE
- CWE-94
- CVSS base score
- 7.8
- Published
- 2025-06-24
- OWASP
- A03 Injection
- Orthogonal defect classification
- Interface
- Code defect classification
- Incorrect Functionality
- Category
- Input Validation and Sanitization
- Subcategory
- Insecure Parsing or Deserialization
- Accessibility scope
- Remote
- Impact
- Arbitrary Code Execution
- Affected component
- Python
- Fixed by upgrading
- Yes
Solution
Upgrade to a patched version of NVIDIA Megatron-LM that addresses the code injection vulnerability in the Python component. Check NVIDIA's security advisories for the specific affected versions and patched versions.
Vulnerable code sample
import argparse
import os
import subprocess
def load_config(config_file):
"""
Loads configuration from a Python file. Vulnerable to code injection.
"""
try:
# Directly executing the contents of the config file!
exec(open(config_file).read())
return config
except Exception as e:
print(f"Error loading config: {e}")
return None
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Megatron-LM Config Loader")
parser.add_argument("--config", type=str, required=True, help="Path to the configuration file")
args = parser.parse_args()
config = load_config(args.config)
if config:
print("Config loaded successfully.")
# Example usage of a config parameter
if 'model_name' in config:
print(f"Model name: {config['model_name']}")
#Potentially dangerous usage. Consider a case where config['data_path'] contains an injected command
if 'data_path' in config:
print(f"Data path: {config['data_path']}")Patched code sample
import os
import pickle
import safe_eval # Assuming safe_eval is a library designed for safe evaluation of expressions
def load_and_execute(file_path):
"""
Loads data from a file and safely evaluates a specific expression within it.
Args:
file_path (str): The path to the file containing the data and expression.
Returns:
The result of the safe evaluation, or None if an error occurs.
"""
try:
with open(file_path, 'rb') as f:
data = pickle.load(f) # Load data using pickle
# Before eval, check if 'expression' exists and is a string.
if 'expression' in data and isinstance(data['expression'], str):
expression_str = data['expression']
# Use safe_eval to evaluate the expression safely. Limit globals and locals.
result = safe_eval.safe_eval(expression_str, globals={}, locals={})
return result
else:
print("Error: 'expression' key not found or not a string in the loaded data.")
return None
except (FileNotFoundError, pickle.PickleError, AttributeError, safe_eval.SafeEvalException) as e:
print(f"Error processing file: {e}")
return None
# Example of using the function
if __name__ == "__main__":
# Create a dummy file (for testing purposes)
data_to_save = {'expression': '1 + 1'} # A safe expression, for demo. This is normally created by the attacker
file_path = "data.pkl"
with open(file_path, 'wb') as f:
pickle.dump(data_to_save, f)
result = load_and_execute(file_path)
if result is not None:
print(f"Result of safe evaluation: {result}")
# Clean up the dummy file (optional)
os.remove(file_path)Payload
import os
os.system("whoami > /tmp/owned.txt")
Cite this entry
@misc{vaitp:cve202523265,
title = {{Megatron-LM vulnerable to code injection via malicious file, leading to code execution.
}},
author = {Bogaerts, Fr\'ed\'eric and Ivaki, Naghmeh and Fonseca, Jos\'e},
year = {2025},
note = {VAITP Python Vulnerability Dataset, entry CVE-2025-23265},
howpublished = {\url{https://netpack.pt/vaitp/vulnerability/CVE-2025-23265/}}
}
Introducing the "VAITP dataset": a specialized repository of Python vulnerabilities and patches, meticulously compiled for the use of the security research community. As Python's prominence grows, understanding and addressing potential security vulnerabilities become crucial. Crafted by and for the cybersecurity community, this dataset offers a valuable resource for researchers, analysts, and developers to analyze and mitigate the security risks associated with Python. Through the comprehensive exploration of vulnerabilities and corresponding patches, the VAITP dataset fosters a safer and more resilient Python ecosystem, encouraging collaborative advancements in programming security.
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