> 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.
The vulnerability is introduced when a token is loaded from an user input.
```python
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.
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.
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](https://github.com/yaml/pyyaml/issues/420)
The vulnerable sinks are now `yaml.unsafe_load` and `yaml.load(input, Loader=yaml.UnsafeLoader)`.