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

Installing TriforceAFL for ARM

In order to perform fuzzing with TriforceAFL on the ARM architecture, we need to make certain changes in the Docker image provided by MoFlow, which can be found at We will create a folder called armfuzz to store the zImage and kallsyms files. Once the necessary changes are implemented, we can run the following command to start Docker with TriforceAFL:

docker run --rm -it -v $(pwd)/armfuzz:/krn moflow/afl-triforce /bin/bash

Now, we need to apply specific changes. First, we will update the repository of TriforceAFL to obtain the capabilities for fuzzing syscalls from the ARM architecture. Since the image starts in the TriforceLinuxSyscallFuzzer folder, we can execute the following commands:

cd /TriforceAFL # move to the TriforceAFL folder
git pull # update branch to the last version
make clean # clean current compiled binaries
make $(nprocs) # compile newer binaries

With these changes, we have...