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Fuzzing or fuzz testing is a software testing technique, often automated or semi-automated, that involves providing invalid, unexpected, or random data to the inputs of a computer program. The program is then monitored for exceptions such as crashes, or failing built-in code assertions or for finding potential memory leaks. Fuzzing is a form of random testing commonly used to test for security problems in software or computer systems.

For the purpose of security, input that crosses a trust boundary is often the most interesting.[1] For example, it is more important to fuzz code that handles the upload of a file by any user than it is to fuzz the code that parses a configuration file that is accessible only to a privileged user.


Random input was used in some early testing work, notably in "The Monkey", a classic Mac OS application developed by Steve Capps prior to 1983. It used journaling hooks to feed random events into Mac programs, and was used to test for bugs in MacPaint.[2][3]

The term "fuzz" or "fuzzing" originates from a 1988 class project, taught by Barton Miller at the University of Wisconsin.[4][5] The project developed a basic command-line fuzzer to test the reliability of Unix programs by bombarding them with random data until they crashed. This early work includes not only the use of random unstructured testing, but also a systematic set of tools to evaluate a wide variety of software utilities on a variety of platforms, along with a systematic analysis of the kinds of errors that were exposed by this kind of testing. In addition, they provided public access to their tool source code, test procedures and raw result data. The test was repeated in 1995, expanded to include testing of GUI-based tools (such as the X Window System), network protocols, and system library APIs.[6] Follow-on work included testing command- and GUI-based applications on both Windows and Mac OS X.

As is often the case, similar techniques existed long before the name was applied. Jerry Weinberg reports using card decks of random numbers, or cards pulled out of the trash, as input to programs in the 1950s.[7]

What distinguishes this work is quantitative demonstration of the abilities of fuzz testing, and the careful identification of the kinds of flaws it could trigger in the code. As part of this demonstration, all tools and results were made public, creating a transparent process that is now common and familiar to the open source community

An early fuzz testing tool was crashme, first released in 1991, which was intended to test the robustness of Unix and Unix-like operating systems by executing random machine instructions.[8]

Fuzzing has gone well beyond these early works, and is now an integral part of the security and testing communities, with a myriad of open source and commercial tools, and related research efforts.


Fuzz testing is often employed as a black-box testing methodology. The technique can only provide a random sample of the system's behavior, and in many cases passing a fuzz test may only demonstrate that a piece of software can handle exceptions without crashing, rather than behaving correctly.[9] This means fuzz testing is an assurance of overall quality, rather than a bug-finding tool, and not a substitute for exhaustive testing or formal methods.

As a gross measurement of reliability, fuzzing can suggest which parts of a program should get special attention, in the form of a code audit, application of static code analysis, or partial rewrites.

Types of bugs[edit]

As well as testing for outright crashes, fuzz testing is used to find bugs such as assertion failures and memory leaks (when coupled with a memory debugger). The methodology is useful against large applications, where any bug affecting memory safety is likely to be a severe vulnerability.

Since fuzzing often generates invalid input it is used for testing error-handling routines, which are important for software that does not control its input. Simple fuzzing can be thought of as a way to automate negative testing.

Fuzzing can also find some types of "correctness" bugs. For example, it can be used to find incorrect-serialization bugs by complaining whenever a program's serializer emits something that the same program's parser rejects.[10] It can also find unintentional differences between two versions of a program[11] or between two implementations of the same specification.[12]


Any type of program input can be fuzzed. Interesting inputs include: file formats, network protocols, environment variables, sequences of API calls, keyboard and mouse events. Even items not normally considered "input" can be fuzzed, such as the contents of databases, shared memory, or the precise interleaving of threads.

Fuzzing programs fall into two different categories. Mutation-based fuzzers mutate existing data samples to create test data while generation-based fuzzers define new test data based on models of the input.[6] These can be employed as white-, grey-, or black-box testing.[6]

The simplest form of fuzzing technique is sending a stream of random bits to software, either as command line options, randomly mutated protocol packets, or as events. This technique of random inputs continues to be a powerful tool to find bugs in command-line applications, network protocols, and GUI-based applications and services.

Another common technique that is easy to implement is mutating existing input (e.g. files from a test suite) by flipping bits at random or moving blocks of the file around.

Other fuzzers have a detailed understanding of the format or protocol being tested. This can be based on a specification. A specification-based fuzzer involves writing the entire array of specifications into the tool, and then using model-based test generation techniques in walking through the specifications and adding anomalies in the data contents, structures, messages, and sequences. This "smart fuzzing" technique is also known as robustness testing, syntax testing, grammar testing, and (input) fault injection.[13][14][15] The protocol awareness can also be created heuristically from examples using a tool such as Sequitur.[16] These fuzzers can generate test cases from scratch, or they can mutate examples from test suites or real life. They can concentrate on valid or invalid input, with mostly-valid input tending to trigger the "deepest" error cases.

There are two limitations of protocol-based fuzzing based on protocol implementations of published specifications: 1) Testing cannot proceed until the specification is relatively mature, since a specification is a prerequisite for writing such a fuzzer; and 2) Many useful protocols are proprietary, or involve proprietary extensions to published protocols. If fuzzing is based only on published specifications, test coverage for new or proprietary protocols will be limited or nonexistent.

Fuzz testing can be combined with other testing techniques. White-box fuzzing uses symbolic execution and constraint solving.[17] Evolutionary fuzzing leverages feedback from an heuristic (E.g., code coverage in grey-box harnessing,[18] or a modeled attacker behavior in black-box) effectively automating the approach of exploratory testing.

Reproduction and isolation[edit]

Test case reduction is the process of extracting minimal test cases from an initial test case.[19][20] Test case reduction may be done manually, or using software tools, and usually involves a divide-and-conquer algorithm, wherein parts of the test are removed one by one until only the essential core of the test case remains.

So as to be able to reproduce errors, fuzzing software will often record the input data it produces, usually before applying it to the software. If the computer crashes outright, the test data is preserved. If the fuzz stream is pseudo-random number-generated, the seed value can be stored to reproduce the fuzz attempt. Once a bug is found, some fuzzing software will help to build a test case, which is used for debugging, using test case reduction tools such as Delta or Lithium.

Advantages and disadvantages[edit]

The main problem with fuzzing to find program faults is that it generally only finds very simple faults.[citation needed] The computational complexity of the software testing problem is of exponential order (, ) and every fuzzer takes shortcuts to find something interesting in a timeframe that a human cares about. A primitive fuzzer may have poor code coverage; for example, if the input includes a checksum which is not properly updated to match other random changes, only the checksum validation code will be verified. Code coverage tools are often used to estimate how "well" a fuzzer works, but these are only guidelines to fuzzer quality. Every fuzzer can be expected to find a different set of bugs.

On the other hand, bugs found using fuzz testing are sometimes severe, exploitable bugs that could be used by a real attacker.[citation needed] Discoveries have become more common as fuzz testing has become more widely known, as the same techniques and tools are now used by attackers to exploit deployed software. This is a major advantage over binary or source auditing, or even fuzzing's close cousin, fault injection, which often relies on artificial fault conditions that are difficult or impossible to exploit.

The randomness of inputs used in fuzzing is often seen as a disadvantage, as catching a boundary value condition with random inputs is highly unlikely but today most of the fuzzers solve this problem by using deterministic algorithms based on user inputs.[citation needed]

Fuzz testing enhances software security and software safety because it often finds odd oversights and defects which human testers would fail to find, and even careful human test designers would fail to create tests for.

See also[edit]


  1. ^ John Neystadt (February 2008). "Automated Penetration Testing with White-Box Fuzzing". Microsoft. Retrieved 2009-05-14. 
  2. ^ "Macintosh Stories: Monkey Lives". Folklore.org. 1999-02-22. Retrieved 2010-05-28. 
  3. ^ The "Monkey" terminology comes from the adage that "a monkey hitting keys at random on a typewriter keyboard for an infinite amount of time will eventually type out the entire works of Shakespeare". In the case of testing, the "works of Shakespeare" is a metaphor for the particular sequence of inputs that will trigger a crash (cf. the infinite monkey theorem).
  4. ^ Barton Miller (2008). "Preface". In Ari Takanen, Jared DeMott and Charlie Miller, Fuzzing for Software Security Testing and Quality Assurance, ISBN 978-1-59693-214-2
  5. ^ "Fuzz Testing of Application Reliability". University of Wisconsin-Madison. Retrieved 2009-05-14. 
  6. ^ a b c Michael Sutton; Adam Greene; Pedram Amini (2007). Fuzzing: Brute Force Vulnerability Discovery. Addison-Wesley. ISBN 0-321-44611-9. 
  7. ^ Gerald M. Weinberg. "Fuzz Testing and Fuzz History". Retrieved 2017-02-06. 
  8. ^ "crashme". CodePlex. Retrieved 2012-06-26. 
  9. ^ Justin E. Forrester; Barton P. Miller. "An Empirical Study of the Robustness of Windows NT Applications Using Random Testing". 
  10. ^ Jesse Ruderman. "Fuzzing for correctness". 
  11. ^ Jesse Ruderman. "Fuzzing TraceMonkey". 
  12. ^ Jesse Ruderman. "Some differences between JavaScript engines". 
  13. ^ "Robustness Testing Of Industrial Control Systems With Achilles" (PDF). Retrieved 2010-05-28. [dead link]
  14. ^ "Software Testing Techniques by Boris Beizer. International Thomson Computer Press; 2 Sub edition (June 1990)". Amazon.com. Retrieved 2010-05-28. 
  15. ^ "Software Fault Injection: Inoculating Programs Against Errors by Jeffrey M. Voas and Gary McGraw". John Wiley & Sons. January 28, 1998. 
  16. ^ Dan Kaminski (2006). "Black Ops 2006" (PDF). 
  17. ^ Patrice Godefroid; Adam Kiezun; Michael Y. Levin. "Grammar-based Whitebox Fuzzing" (PDF). Microsoft Research. 
  18. ^ "VDA Labs". 
  19. ^ "Test Case Reduction". 2011-07-18. 
  20. ^ "IBM Test Case Reduction Techniques". 2011-07-18. 

Further reading[edit]

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