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

American Fuzzy Lop and American Fuzzy Lop++

American Fuzzy Lop (AFL) represents a piece of history – though its code base has not been updated for 2 years, it was open sourced a while ago. For this reason, a group of brave hackers decided to fork it and develop AFL++, which offers very advanced features with respect to the original version and has taken over AFL within the open source community.

Advantages of AFL and AFL++ versus my own fuzzer

Michael Zalewski (@lcamtuf) developed American Fuzzy Lop (also a breed of rabbits) while working at Google. AFL is used by Google to test its software for code coverage and bug finding. AFL is a program that incorporates the best fuzzing practices and evolutive algorithms. An evolutive algorithm allows mutating the input according to a reward function, which is normally based on the program experience (i.e., the output of the previous execution). Rewriting such software from scratch would surely be very hard given its maturity. Nonetheless...