Book Image

Fuzzing Against the Machine

By : Antonio Nappa, Eduardo Blázquez
Book Image

Fuzzing Against the Machine

By: Antonio Nappa, Eduardo Blázquez

Overview of this book

Emulation and fuzzing are among the many techniques that can be used to improve cybersecurity; however, utilizing these efficiently can be tricky. Fuzzing Against the Machine is your hands-on guide to understanding how these powerful tools and techniques work. Using a variety of real-world use cases and practical examples, this book helps you grasp the fundamental concepts of fuzzing and emulation along with advanced vulnerability research, providing you with the tools and skills needed to find security flaws in your software. The book begins by introducing you to two open source fuzzer engines: QEMU, which allows you to run software for whatever architecture you can think of, and American fuzzy lop (AFL) and its improved version AFL++. You’ll learn to combine these powerful tools to create your own emulation and fuzzing environment and then use it to discover vulnerabilities in various systems, such as iOS, Android, and Samsung's Mobile Baseband software, Shannon. After reading the introductions and setting up your environment, you’ll be able to dive into whichever chapter you want, although the topics gradually become more advanced as the book progresses. By the end of this book, you’ll have gained the skills, knowledge, and practice required to find flaws in any firmware by emulating and fuzzing it with QEMU and several fuzzing engines.
Table of Contents (18 chapters)
Part 1: Foundations
Part 2: Emulation and Fuzzing
Part 3: Advanced Concepts
Chapter 12: Conclusion and Final Remarks


In this chapter, we learned the changes that the Sloth project makes to qemu-user in order to apply fuzzing to a function in a native library. You have also included into your set of skills another library specific for fuzzing (libFuzzer), and have seem how you can integrate it into qemu-user. There are some major limitations in the Sloth project since, at the moment, it does not yet support the fuzzing of JNI used for the communication between the Java and the native libraries of an Android application. But we agree that the engine behind the ART is very complex, and going through the ART to exploit JNI code would be harder than what has been presented here (it wouldn’t even fit in one chapter of a book). In any case, we think that a project like this opens your mind and teaches you about libFuzzer, an alternative to fuzzers such as AFL or AFL++.

In the next chapter, we will conclude this book with some final remarks and extra acknowledgments.