Fuzzing: Mutation vs. generation
Many of you have undoubtedly come across the word “Fuzzing” and wondered about it. But if you have ever tried modifying some parameter; some sort of input/arguments; tried deviating it from the normal expected input – then you have indeed tried fuzzing, all by yourself.
The standard definition of Fuzzing (according to the Standard Glossary of Software Engineering Terminology, IEEE) is
“The degree to which a system or component can function correctly in the presence of invalid inputs or stressful environmental conditions.”
Fuzzing (or “fuzz testing”) is basically nothing more than a software testing technique used to uncover a variety of issues, among them: coding errors; security vulnerabilities like Cross Site Scripting; Buffer Overflow; Denial of Service, and so forth, using unexpected, malformed, random data (called “fuzz”) as program inputs. You are, in effect, trying to crash the program or make it behave unexpectedly.
Fuzzing can be traced back to the University of Wisconsin in 1988. There, Professor Barton Miller gave a class project titled “Operating System Utility Program Reliability – The Fuzz Generator.” It was the first – and simplest – form of fuzzing, and included sending a stream of random bits to UNIX programs by the use of a command line fuzzer.
In a response by email when asked about the origins of the term “fuzz,” Professor Miller replied:
“The original work was inspired by being logged on to a modem during a storm with lots of line noise. And the line noise was generating junk characters that seemingly were causing programs to crash. The noise suggested the term ‘fuzz'”.
Even though the term was coined by Barton Miller, early examples of fuzzing include “The
Monkey.” It was a small desk accessory that engaged the journaling hooks to feed random events to the current application. As a result, the Macintosh seemed to be operated by an incredibly fast, somewhat angry monkey, banging away at the mouse and keyboard, generating clicks and drags in random positions with wild abandon.
In 1998, the PROTOS project at University of Oulu was proposed for the purpose of enabling the software industry themselves to find security critical problems, using new model-based test automation techniques, as well as other next generation fuzzing techniques. Later (in 2001), Codenomicon (another network protocol fuzz testing solution) was founded, based on PROTOS. With this, the Fuzzers just kept on improving in technology and methodology, encompassing a new generation of fuzzing frameworks, including SPIKE, PeachFuzzer etc.
When test cases are defined for any product, they might be defined by considering how they are designed to behave (as well as how they shouldn’t behave). But within these criteria there is always an undefined area beyond the imagination of test developer. The challenge is to explore that undefined region and the companion here is fuzzing. The main purpose of fuzzing is not to test the correct functionality of the product/program as such, but to explore and test that undefined area.
Enormous classifications exist for fuzzing depending on attack vectors, fuzzing targets, fuzzing method, and so forth. Fuzzing targets for an application include file formats, network protocols, command-line args, environment variables, web applications and many others.
The first classification which we will consider is the way test cases are generated; or in other words, how the application is altered if fuzzed. There are two approaches here: mutation and generation, explained below.
Mutation vs. generation (dumb vs. intelligent)
Mutation (dumb fuzzers)
Here, it’s all about mutating the existing input values (blindly). That’s why it is known as “dumb” fuzzers, as in lacking understanding of the format/structure of the data. One example can be just replacing/appending a random section of data.
Example: Bit Flipping is the one of the techniques used in the mutation, wherein the bits are flipped in some sequence or randomly.
Similarly, we can append some string at the end of the existing input.
Bit flipping with ZZUF (mutation):
One of the most common fuzzers available for bit flipping is zzuf. It is a transparent application input fuzzer, whose purpose is to find bugs in applications by corrupting their user-contributed data, changing the random-bits in the input.
ZZUF architecture consists of two parts: the first being zzuf executable; the second is Libzzuf shared library file. Once the zzuf executable is run, it first reads the command line fuzzing options, then saves these in the internal environment. Libzzuf is preloaded into the process, executed and then reads the saved fuzzing options from the environment. In order to intercept file and network operations, signal handlers and memory allocations, Libzzuf diverts and re-implements the functions, which can sometimes be private C library symbols. Hence; all the diverted calls go through Libzzuf.
Consider the example of fuzzing an input file with zzuf and cat (Linux utility).
Screen 1: It shows the input file “fuzz.txt” which we will use as the input to zzuf utility.
Screen 2: With zzuf, we can change the amount of bits we want to fuzz and can specify it with –r flag. As shown, 0.002 means fuzz 0.2% of the bits and 0.01 means fuzz 1% of the bits.
Screen 3: With the same fuzzing ratio, zzuf behaviour is reproducible. Sometimes, we may want to change the fuzzed output without changing the fuzzing ratio; in that case, zzuf provides another command line option specified by –s flag, called the seed flag. This sets the initial random seed used internally by zzuf random number generator and hence, even though the random ratio is same, the fuzzed output is different.
Generation (intelligent fuzzer)
In contrast to Dumb Fuzzers, here an understanding of the file format / protocol is very important. It’s about “generating” the inputs from the scratch based on the specification/format.
Intelligent fuzzing with Peach fuzzer:
Peach Fuzzer is a smart fuzzer with both the generation and mutation capabilities. It works by creating PeachPit files, which are the XML files containing the complete information about the data structure, type information and the relationship of the data.
Peach works on several components: among them, Data Modelling; State Modelling; Publisher; Agents; Monitor, and Loggers, and so forth. Here, in this section, we will be discussing the modelling part, mainly because it is related to the intelligent fuzzer. Later, in the second part of fuzzing, we will be discussing other components of Peach Fuzzer as well.
Let’s discuss the creation of a sample PeachPIT file for HTTP Protocol. As an example:
Peach focuses heavily on both the data modelling and state modelling. The level of detail that is put into these models serves to illustrate the striking difference between a dumb Peach Fuzzer and a smart one.
Peach Pit files will contain at least one data model. Data Models define the structure of a data block by specifying additional child elements, such as Number and String.
Complex protocols are split into parts: each part with its own data model for reuse. A data model can then further be split into Blocks. Also, if a reference (ref attribute) is supplied, the contents of the reference are copied to create the base of the new data model. Any child elements in the data model will override elements that already exist with the same name.
<!– Indicate a relation between this field and the “Body” field. –>
Here, it will override both the String definition and will be equivalent to :-
<!– Indicate a relation between this field and the “Body” field. –>
We can also see the relation attribute which is used to model the relationship of different data. So in this case, the value is equivalent to the size of the Body element which is described as :-
Sample data model for HTTP Protocol would be:-
A State Model consists of at least one state and one model. In the case of multiple states , initialState attribute determines the first State of the model.
Here “<Action>” performs various actions in the state model, such as sending output to the publisher or reading input via publisher specified in some data model, and so forth. In our case, there is only one State and one Action which would be performed on the DataModel HttpRequest. Now, Action has an optional child element – “Data” – which is used to create and load a default set of data into the data models.
This would create a set of default values for any data model used and would override the default values, if any. Similar to the data model references, we have references in Data also .
Sample State Model and Data for HTTP would be :-
Now, after data modelling and state modelling is completed, our aim is to run the fuzzer against web server. The test element configures a specific fuzzing test that combines a state model with a publisher. (We will discuss more about publisher later). The publisher here is basically targeting our requests to the web server on the localhost.
<!– Target a local web server on port 80 –>
Similarly , another test :-
Finally, we need a run element to group all the elements together.
<!– The set of tests to run –>
That completes the creation of basic PeachPIT file and we will run it with Peach Fuzzer.
Screen 1: -t flag is to perform the parsing of the Peach XML File.
Screen 2: -1 option is to run only 1 iteration and debug enables the debug messages.
With configuration of loggers and agent , we can track the progress and the faults .
Even though the mutation approach is easier than the generation based approach (because it doesn’t require understanding of the protocol), generation is better because it submits valid combinations of input and has better code coverage and code paths. Even though the generation based approach takes more time to do, it is considered to be a more thorough process.
Advantages of fuzzing
Fuzzing is a random way of testing, using an approach that enables it to find the bugs which are impossible to find in the defined testing or approach-based testing. It will detect exploitable issues of real value, and with hardly any assumptions/presumptions made before starting the process. It’s very easy and quick to setup, and once the setup is done, easy to repeat it for regression.
Limitations of fuzzing
Fuzzing effectively locates simple bugs. It is valuable when attacking a black box system, but carries an inherent limitation, in that there is hardly any information to check the impact of the fuzzing. Writing a protocol specification or file format specification is a tedious task. The random approach has its advantages, but on the downside, it does lag behind in finding the boundary value issues.
Part 2 is about Application and File Fuzzing and is now published here: /application-and-file-fuzzing/
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