Python pickle exploit github


Skip to content. Instantly share code, notes, and snippets. Code Revisions 7. Embed What would you like to do? Embed Embed this gist in your website. Share Copy sharable link for this gist. Learn more about clone URLs. Download ZIP. This Gist creates payloads to exploit pythons pickle function. It is pre-setup to create reverse shells but could be tweaked for whatever fun uses you can think of. This script can pickle any python code and execute it on the target when it is unpickled as long as the target has whatever modules you try to import.

It optionally URL encodes it so that it can be submitted through webforms ect. Notes: This code is written for Python 2. If these don't match the target you can get weird import errors.

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It will be modified to include our custom function s in the wrapper below. Do not modify this unless you know what you are doing. You could have multiple functions etc. The default reverse shell code is fairly simple and doesn't make use of any functions. You can see an example of using functions in the commented out section below. Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment. You signed in with another tab or window.

Reload to refresh your session. You signed out in another tab or window. Author: Benjamin Grewell. Purpose: This script creates a reverse shell that will be executed when the python pickle package attempts to unpickle it. This script can pickle any python code and execute it on the target when it is unpickled as long as the target has whatever.

This is the main skeleton for building our payloads. It will be modified to include our custom function s in the wrapper. Your custom code goes inside here. The default reverse shell code is fairly. You can see an example of using functions in the commented out section. Get a base64 encoded version of our wrapper code. Build our raw base64 payload using our wrapper payload and any custom append.

URL encode our pickle if requested. Swap out line endings could do this in other places but it's easy enough here, although a little hacky and only modifies the line endings on urlencoded payloads.Pretty much every Python programmer out there has broken down at one point and and used the ' pickle ' module for writing objects out to disk. The advantage of using pickle is that it can serialize pretty much any Python object, without having to add any extra code.

Its also smart in that in will only write out any single object once, making it effective to store recursive structures like graphs. For these reasons pickle is usually the default serialization mechanism in Python, used in modules likes python-memcached. This benchmark measures the number of objects a second each of these libraries can read and write.

The data being serialized here are just randomly generated fake 'Tweet' objects containing just four fields:. Pickle is the clear underperformer here. Pickle also produces serialized values that are around double the size of Thrift or MessagePack. I've put the code for this benchmark up on github for those that are interested. Another reason not to use pickle is that unpickling malicious data can cause security issues, including arbitrary code execution.

An example that the brave and foolish can try is below. Unpickling the data there will open a shell prompt that will delete all the files in your home directory:.

Even worse, an attacker could use pickle to get remote shell access to your computer. Its not like this is an unknown issue. The pickle module even comes with a big warning about this right in the documentation:. Warning: The pickle module is not intended to be secure against erroneous or maliciously constructed data.

Never unpickle data received from an untrusted or unauthenticated source. But its not always clear that your data hasn't been altered since you wrote it. Say for instance that an attacker gains access to your network, but can't yet run any code on your servers.

If you are using the default python-memcached bindings, all the attacker has to do is make a network call to your memcache server to set a carefully chosen pickle value, and wait for it to be read back in. Once your Python process reads in the data, whatever code the attacker wants will be running on your server.

For most common tasks, just use JSON for serializing your data. Its fast enough, human readable, doesn't cause security issues, and can be parsed in all programming languages that are worth knowing. MessagePack is also a good alternative, I was surprised by how well it performed in the benchmark I put together.

Pickle on the other hand is slow, insecure, and can be only parsed in Python. The only real advantage to pickle is that it can serialize arbitrary Python objects, whereas both JSON and MessagePack have limits on the type of data they can write out. Given the downsides though, its worth writing the little bit of code necessary to convert your objects to a JSON-able form if your code is ever going to be used by people other than yourself.

Ben Frederickson. Don't Pickle Your Data. Serialization Rate. Packed Size. Update Feb I've changed the graphs above to address a couple of issues people have brought up. The default pickle protocol is slow, so I've added a faster version. The effects aren't nearly as strong, but even with both changes Pickle isn't a great option. Get new posts by email! Enter your email address to get an email whenever I write a new post:.Luckily, all major libraries that I make use of have already made the small leap and as behaviour has only been slightly changed I never ran into any Py3 specific issues.

Until today, that is. After writing quite the collection of classes, and storing their initialized states along with several module-specific objects all in one container, it was ready to be pickled and transferred.

My fingers being crossed and naive high hopes notwithstanding, after a few lines of log code rolled over the screen the following presented itself:.

python pickle exploit github

Having no clue what went wrong yet, I instinctively looked up the error - and was presented by the dreaded scenario as so well described by xkcd:. In my case, however, it was just a single dumped log snippet. The chances of these dumps being linked in a chat somewhere are way higher than them actually being traceable. And although this was the case, the IRC server was friendly enough to log the conversation. That pretty much saved my monday morning. Turned out the bug was actually not that complex, and it has to do with the Py2 to Py3 transition of merging the good old dict.

In retrospect, this makes a lot of sense as the iter method was intended to be a generator version for dealing with big dictionaries. There are very good reasons why one should not want to pickle a generator. Quoting Alexandre Vassalotti :. And this latter is rather cumbersome to accomplish, since it basically requires to make the whole interpreter picklable.

Anyway, problem solved. To make this mistake better recorded I thought I might as well post it. Hope it helps!Splat expands a collection into positional arguments, while splatty-splat expands a dictionary into keyword arguments. Splat combines zero or more positional arguments into a tuple, while splatty-splat combines zero or more keyword arguments into a dictionary. If variable is being assigned to anywhere in the scope, it is regarded as a local variable, unless it is declared as a 'global' or a 'nonlocal'.

Decorator that caches function's return values. All function's arguments must be hashable. A decorator that accepts arguments and returns a normal decorator that accepts a function.

Into the Jar | Jsonpickle Exploitation

MRO determines the order in which parent classes are traversed when searching for a method:. Mechanism that restricts objects to attributes listed in 'slots' and significantly reduces their memory footprint. A duck type is an implicit type that prescribes a set of special methods. Any object that has those methods defined is considered a member of that duck type. Using scandir instead of listdir can significantly increase the performance of code that also needs file type information.

Opens a connection to the database file. Creates a new file if path doesn't exist. In this example values are not actually saved because 'db. Bytes object is an immutable sequence of single bytes. Mutable version is called bytearray. List that can only hold numbers of a predefined type. Available types and their sizes in bytes are listed above.

A thread-safe list with efficient appends and pops from either side. Pronounced "deck". Type is the root class.

python pickle exploit github

If only passed an object it returns its type class. Otherwise it creates a new class. Right before a class is created it checks if it has a 'metaclass' attribute defined.All new content for Exploit Database.

Python 1. EDB-ID: EDB Verified:. Author: Jeff Epler. Type: local. Platform: Linux. Date: Vulnerable App:. The Python Pickle module is provided to convert object variables into a serialized form "pickling"and later recover the data back into an object hierarchy "unpickling". A vulnerability has been reported in the Pickle implementation included with some versions of Python. If specially crafted malicious object data is "unpickled", it may cause arbitrary Python commands to be executed, including system calls.

This is accomplished by specifying an available function as the class constructor.

python pickle exploit github

Exploitation of this vulnerability will be highly dependent on a specific Python application which accepts a pickle string from an untrusted source. This behavior has been reported in some implementations of the Python SmartCookie class. But the damage is done Kali Linux. Penetration Testing. Kali NetHunter. Advanced Attack Simulation.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

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Sign up. Branch: master. Find file Copy path. Raw Blame History. See module copyreg for a mechanism for registering custom picklers. See module pickletools source for extensive comments. Only bump this if the oldest still supported version of Python already includes it.

See pickletools. The listing here is in kind-of alphabetical order of 1-character pickle code. The concatenation is expected to be less expensive than issuing an additional call to write. BytesIO instance so that the file object can have delayed access to the previous frame contents via an unreleased memoryview of the previous io. BytesIO instance. BytesIO def write selfdata : if self. Be careful not to concatenate the header and the payload prior to calling 'write' as we do not want to allocate a large temporary bytes object.

We intentionally do not insert a protocol 4 frame opcode to make it possible to optimize file. BytesIO self. Note that 0 is a special case, returning an empty string, to save a byte in the LONG1 pickling context. The default protocol is 4.

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It was introduced in Python 3. Specifying a negative protocol version selects the highest protocol version supported.Written By: Versprite. The dangers are so prevalent in fact that the pickle documentation explicitly states that it is not intended to be secure against erroneous or malicious constructed data. Interestingly enough, a few libraries have popped up over the years that support encoding pickled objects in different formats such as JSON and XML. We will explore whether or not we can create malicious encoded JSON pickles, use them for exploitation through a vulnerable web application, and some techniques we can use to dynamically trace the libraries functionality when used.

The libraries usage is pretty straight forward. Here is an example from its documentation:. This is very nice JSON representation of a pickled object.

However, there are a few things we need to understand about how the library seemingly encodes and decodes pickles, and how we can build a properly JSON encoded malicious pickle as well. Before we dive into how we are going to construct a valid JSON encoded malicious pickle, lets revisit some key items about the reconstruction process of pickles to Python objects. Included within the pickle architecture is a virtual machine.

Comprehensive Python Cheatsheet

The PVM contains three important elements:. The instruction engine is used to process the instructions within the pickle stream, and the stack is a typical stack like structure implemented as a list.

The memo is a register scratch space, which is built from an indexed list array. The pickle virtual machine will take a pickle stream and attempt to recreate an object based off of the read instructions. It does this by reconstructing a Python dict from the pickled object, creating a class instance, then populating the class instance from the dictionary elements.

Lets take a look at this example class and its corresponding shellcode:. So this definitely makes more sense when having some previous knowledge of what the instructions will look like within the pickle stream. However, we really want to understand what happens after the decoding takes place. We can potentially assume at a high level that jsonpickle takes the JSON format, creates a valid pickle, then reconstructs the object from the pickle. You can use this function by passing it a callback with arguments that define the current stack frame, and a keyword event.

It is relatively simple to use this for basic function tracing within a Python program. What would this look like in a real word scenario? Lets take a look at the code:. Now we can take our previous JSON encoded malicious pickle, base64 encode it, and deliver it to our vulnerable web application. VerSprite's Research and Development division a. Our clients rely on VerSprite's unique offerings of zero-day vulnerability research and exploit development to protect their assets from various threat actors.

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