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

Adding a new CPU

We decided to use PANDA’s version of QEMU because, in the next chapter, we will see project FirmWire that emulates the firmware; we will just try to boot on the same emulator. You can check out the latest version.

Without further delay, let’s dive straight into a quick trick for adding support for a new CPU in PANDA-QEMU, which doesn’t seem to be supported initially. Specifically, the panda-re/panda/target/arm/cpu.c file contains details about ARM 32-bit architecture CPUs, including different flavors. In the case of real-time software, the ARM Cortex-R series is often preferred, with Samsung basebands running on top of the cortex-r7, for example. Upon examining the following code excerpt, we can see that only cortex-r5 is supported (indicated in bold). This structure associates an init function with each CPU model. To add support for cortex-r7, we can reuse the init function of cortex-r5 and rename it accordingly. This will help instrument the...