Fuzzing: Hack, Art, and Science

bands of color in fuzzy photo- Credit: Irina Vinnikova

Communications of the ACM, January 2019, Vol. 62 No. 1, Pages 106-114
Review Articles: “Fuzzing: Hack, Art, and Science
By David Adrian, Karthikeyan Bhargavan, et al.

“Fuzzing is commonly used as a shorthand for security testing because the vast majority of its applications is for finding security vulnerabilities.”


Fuzzing, or fuzz testing, is the process of finding security vulnerabilities in input-parsing code by repeatedly testing the parser with modified, or fuzzed, inputs. Since the early 2000s, fuzzing has become a mainstream practice in assessing software security. Thousands of security vulnerabilities have been found while fuzzing all kinds of software applications for processing documents, images, sounds, videos, network packets, Web pages, among others. These applications must deal with untrusted inputs encoded in complex data formats. For example, the Microsoft Windows operating system supports over 360 file formats and includes millions of lines of code just to handle all of these.


Most of the code to process such files and packets evolved over the last 20+ years. It is large, complex, and written in C/C++ for performance reasons. If an attacker could trigger a buffer-overflow bug in one of these applications, s/he could corrupt the memory of the application and possibly hijack its execution to run malicious code (elevation-of-privilege attack), or steal internal information (information-disclosure attack), or simply crash the application (denial-of-service attack).9 Such attacks might be launched by tricking the victim into opening a single malicious document, image, or Web page. If you are reading this article on an electronic device, you are using a PDF and JPEG parser in order to see Figure 1.


Figure 1. How secure is your JPEG parser?
Figure 1. How secure is your JPEG parser?

Buffer-overflows are examples of security vulnerabilities: they are programming errors, or bugs, and typically triggered only in specific hard-to-find corner cases. In contrast, an exploit is a piece of code which triggers a security vulnerability and then takes advantage of it for malicious purposes. When exploitable, a security vulnerability is like an unintended backdoor in a software application that lets an attacker enter the victim’s device.

There are approximately three main ways to detect security vulnerabilities in software.


Static program analyzers are tools that automatically inspect code and flag unexpected code patterns. These tools are a good first line of defense against security vulnerabilities: they are fast and can flag many shallow bugs. Unfortunately, they are also prone to report false alarms and they do not catch every bug. Indeed, static analysis tools are typically unsound and incomplete in practice in order to be fast and automatic.


Manual code inspection consists in peer-reviewing code before releasing it. It is part of most software-development processes and can detect serious bugs. Penetration testing, or pen testing for short, is a form of manual code inspection where security experts review code (as well as design and architecture) with a specific focus on security. Pen testing is flexible, applicable to any software, easy to start (not much tooling required), and can reveal design flaws and coding errors that are beyond the reach of automated tools. But pen testing is labor-intensive, expensive, and does not scale well since (good) pen testers are specialized and in high demand.


Fuzzing is the third main approach for hunting software security vulnerabilities. Fuzzing repeatedly executes an application with all kinds of input variants with the goal of finding security bugs, like buffer-overflows or crashes. Fuzzing requires test automation, that is, the ability to execute tests automatically. It also requires each test to run fast (typically in a few seconds at most) and the application state to be reset after each iteration. Fuzzing is therefore more difficult to set up when testing complex distributed applications, like cloud or server applications running on multiple machines. In practice, fuzzing is usually most effective when applied to standalone applications with large complex data parsers. For each bug found, fuzzing provides one or several concrete inputs that can be used to reproduce and examine the bug. Compared to static analysis, fuzzing does not generate false alarms, but it is more computationally expensive (running for days or weeks) and it can also miss bugs.

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About the Author:

Patrice Godefroid is a partner researcher at Microsoft Research, Redmond, WA, USA.