Python Deserialization Attacks
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The document discusses Python deserialization attacks, outlining the concepts of serialization and deserialization and their related vulnerabilities. It highlights various Python modules, including pickle, jsonpickle, and pyyaml, that can be exploited through deserialization, providing examples of potential attacks. Recommendations for remediation emphasize the importance of not deserializing untrusted data and using safer methods available in the mentioned modules.
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- 1. Python Deserialization Attacks By Manmeet Singh Date - 28/04/2020
- 2. Contents ● Serialization Concept ● Why Deserialization tends to a vulnerability ? ● Python Modules vulnerable to Deserialization Vuln. ● Pickle Module ● JSONPickle Module ● PyYAML Module ● Remediation
- 4. Structured Data Variables Lists Strings Custom Objects Text Readable or Unreadable (Bytes) stream format
- 5. Why we need serialization? 1. Recovery of original Structure. 2. Minimize the bandwidth. 3. Calling of class objects.
- 6. ● Thick client application and desktop programs. Example : metasploit, Pycharm, Intellij IDEA etc. ● APIs. ● Mobile applications Where is Serialization getting used ?
- 7. Why Deserialization tends to a vulnerability ?
- 8. Objects of classes can be serialized… And when they get deserialized, the class objects are reformed and do it purpose.
- 9. Yes, Calling of any existing class method is possible .. Even os.system()
- 10. Do developer of serialization libraries knew this?
- 11. Why it was made then? Application Class A Class B def abc(): ... Dynamically working with classes
- 12. Python Serialization Modules Vulnerable To Deserialization Vulnerability
- 13. ● Pickle ● jsonpickle ● Pyyaml ● ruamel.yaml
- 14. Pickle Module
- 15. Pickling is a way to convert a python object (list, dict, etc.) into a character stream. The idea is that this character stream contains all the information necessary to reconstruct the object in another python script.
- 16. Serialization using pickle - pickle.dumps(Object) Deserialization using pickle - pickle.loads(stream) How to pickle and de-pickle ?
- 17. Byte stream ending with . (dot) Detecting use of pickle module
- 18. from pickle import dumps import os class payload(objects): def __reduce__(self): return os.system, (“dir”,) print(dumps(payload())) How to exploit pickle deserialization ?
- 19. from pickle import loads loads(stream) How to exploit pickle deserialization ?
- 21. jsonpickle will serialize complex Python objects to and from JSON.It also convert a pickled object into human readable form.
- 22. Serialization using jsonpickle - jsonpickle.encode(Object) Deserialization using jsonpickle - jsonpickle.decode(stream) How to jsonpickle and json de-pickle ?
- 23. It looks like normal JSON stream of data. Sometimes have a tag “py/” in it. Detecting use of jsonpickle module
- 24. from jsonpickle import encode import os class payload(objects): def __reduce__(self): return os.system, (“dir”,) print(decode(payload())) How to exploit jsonpickle deserialization ?
- 25. from jsonpickle import decode decode(stream) How to exploit jsonpickle deserialization ?
- 26. PyYAML Module
- 27. Pyyaml python module is used to serialize objects in YAML (Yet Another Markup Language) format. So this module is used to process YAML data. ● Pyyaml version < 5.1 is directly vulnerable. (CVE-2017-18342) ● Pyyaml version >=5.1 and < 5.2 is vulnerable under certain condition. (CVE-2019-20477) ● Latest version 5.3.1 of Pyyaml is not vulnerable.
- 28. Serialization using pyyaml - yaml.dump(Object) Deserialization using pyyaml - yaml.load(stream) How to YAML serialize and deserialize ?
- 29. It will be in a YAML format. Detecting use of pyyaml/ruamel.yaml modules
- 30. from yaml import dump import os class payload(objects): def __reduce__(self): return os.system, (“dir”,) print(dump(payload())) How to exploit pyyaml deserialization ?
- 31. from yaml import load load(stream) How to exploit pyyaml deserialization ?
- 32. Remediations For jsonpickle and pickle, Here, the general take-away would be the rule of thumb “Do not deserialize untrusted data” For Pyyaml, ● Use safe_dump() and safe_load() instead of dump() and load(). ● Use latest version of pyyaml.
- 33. Questions ?