Choronzon - An evolutionary knowledge-based fuzzer
This document aims to explain in brief the theory behind Choronzon. Moreover, it provides details about its internals and how one can extend Choronzon to meet new requirements. An overview of the architecture of Choronzon was initially presented at the ZeroNights 2015 Conference. A recording of the presentation and the slide deck are also available.
Choronzon is an evolutionary fuzzer. It tries to imitate the evolutionary process in order to keep producing better results. To achieve this, it has an evaluation system to classify which of the fuzzed files are interesting and which should be dropped.
Moreover, Choronzon is a knowledge-based fuzzer. It uses user-defined information to read and write files of the targeted file format. To become familiar with Choronzon's terminology, you should consider that each file is represented by a chromosome. Users should describe the elementary structure of the file format under consideration. A high level overview of the file format is preferred instead of describing every detail and aspect of it. Each one of those user-defined elementary structures is considered a gene. Each chromosome contains a tree of genes and it is able to build the corresponding file from it.
Choronzon is divided into three subsystems, the Tracer module, the Chromosome module and the fuzzer.
Briefly, the Chromosome component is used to describe the target file format. Users are able to write their own modules to support new or custom formats. As a test-case, a PNG module is provided with Choronzon.
On the other hand, the Tracer component is responsible to monitor the target application and collect various information about its execution. This version of Choronzon uses Intel's Pin binary instrumentation tool in order to log the basic blocks that were visited during the execution. However, Choronzon is able to support other tracing backends as well. Also keep in mind that in the next version of Choronzon, Pin is going to be replaced due to its staggering performance impact.
Lastly, the fuzzer component is used to alter the contents of the files to be tested. The module contains a corpus of Mutators and Recombinators. Mutators, simply, are changing the file like common fuzzers do. For example, they perform byte flipping, byte swapping, random byte mutation and so on. But Choronzon has another feature that is not that common across fuzzers. Recombinators are using the information about the structure of the file format, provided by the Chromosome module, in order to perform intelligent fuzzing.
In the directory
chromosome/parsers you can find the file
Python module describes the PNG file format to the fuzzer. You may add your
custom modules for other file formats in this directory.
The fundamental idea behind the Chromosome subsystem is to convert the initial seed files using a Deserializer into a tree of Genes. At some point, the (fuzzed) Genes will be written into a file, using a Serializer.
Consider that in Choronzon the aim of the parser module is to provide the elementary structure of the file format, instead of every minor detail. This will help the fuzzer to construct files that are mostly sane, avoiding early exiting from the target application. Additionally, this approach saves time, because describing every aspect of the file format is time consuming and introduces significant development overhead.
How to write a custom parser
A new parser module must import:
and it must implement
- a Gene class derived from chromosome.gene.AbstractGene,
- a Serializer class derived from chromosome.serializer.BaseSerializer,
- and a Deserializer class derived from chromosome.deserializer.BaseDeserializer.
In the example shipped with Choronzon, each PNGGene corresponds to a PNG chunk. Generally, you may think of a Gene as an elementary data structure of the target format. Each Chromosome is comprised from a tree of Genes, and represents a unique file. Each Gene must be able to produce a byte string that contains its data combined with the data of the lower Genes in the tree.
The PNGSerializer must be able to produce (a mostly sane) file from when a list of Genes is given to it. On the other hand, PNGDeserializer must be a able to parse a valid file of the target format and deserialize it to a tree of Genes.
chromosome/parsers/PNG.py for a commented example for the PNG format.
The Tracer module is used to disassemble the target application (and/or one or more of its libraries). In this version of Choronzon this is achieved with IDA. We used this approach because we can correlate any interesting information from the fuzzing campaign with our IDBs. However, we may drop the dependency on IDA in the near future in order to make Choronzon more portable and accessible.
A file is tested against an application with the help of a Pin utility. In the
analyzer/coverage directory there's the source code of this Pin tool, which
injects hooks in the beginning of each basic block at the target application.
When the execution is finished, we correlate the basic block that was hit, with
the basic block from the binary. Thus, we're able to calculate metrics that are
valuable for us (coverage etc).
The Fuzzer component is using the Chromosome representation to fuzz a file. As mentioned earlier, there are two fuzzing methods in Choronzon.
For the first method, Choronzon gets the content from one or more genes
and applies one of the Mutators. Mutators implement common but effective
fuzzing methods like random byte mutation, high bit set, byte swapping
and many more. You may also write your own custom mutators and add them in
The second fuzzing method is called recombination. Recombinators are used to change the structure of the file. Here's an example with the PNG format.
PNG files are comprised by consecutive chunks that contain four fields,
- chunk's type,
- chunk's data,
- and a CRC.
Let's assume we have a PNG file that only has IHDR, IDAT and IEND chunks. Its structure would look like the following:
[ PNG signature ] [ IHDR ] [ IDAT ] [ IEND ]
Since Choronzon is aware of the basic structures (i.e the PNG chunks), it is able to alter their sequence. After a successful recombination the fuzzed PNG output file can look like this:
[ PNG signature ] [ IDAT ] [ IHDR ] [ IEND ]
Choronzon contains many more recombination strategies that make it able to cope even with complicated file formats.
Choronzon has been tested with Python 2.7, Pin 3, IDA Pro 6.6 to 6.9, on Ubuntu 16.04 LTS (Linux kernel 4.4) and Windows 10.
In order to run it you'll need to install the sortedcontainers Python package. You may find it here or install it via pip.
Moreover, Choronzon needs IDA Pro (actually, its terminal version). The path of IDA Pro should be specified in your configuration file like this:
DisassemblerPath = 'C:\\Program Files (x86)\\IDA 6.6'
It has been tested successfully with IDA Pro 6.6, 6.7, 6.8 and 6.9.
Choronzon's coverage Pin tool is located at
analyzer/coverage and must be
compiled. You may want to check Pin's documentation for details, or you can
perform the following steps:
- Copy the
make. If you're on Windows you should run the Visual Studio command line, and use the
makeutility and its dependencies from Cygwin
- Copy back to
/path/to/choronzon/analysis/coveragethe newly created
obj-ia32for 32 bit systems)
In order to fuzz with Choronzon, you must provide a configuration
file. In the
settings directory there is an example of Choronzon's