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Description
Peach is an advanced SmartFuzzer that excels in both generation and mutation-based fuzzing techniques. It necessitates the creation of Peach Pit files, which outline the data's structure, type information, and interrelations for effective fuzzing. In addition, Peach provides customizable configurations for a fuzzing session, such as selecting a data transport (publisher) and logging interface. Since its inception in 2004, Peach has undergone continuous development and is currently in its third major iteration. Fuzzing remains one of the quickest methods to uncover security vulnerabilities and identify bugs in software. By utilizing Peach for hardware fuzzing, students will gain insights into the essential principles of device fuzzing. Designed to address any data consumer, Peach can be applied to servers as well as embedded devices. A wide array of users, including researchers, companies, and government agencies, leverage Peach to detect hardware vulnerabilities. This course will specifically concentrate on employing Peach to target embedded devices while also gathering valuable information in case of a device crash, thus enhancing the understanding of fuzzing techniques in practical scenarios.
Description
AFL-Unicorn provides the capability to fuzz any binary that can be emulated using the Unicorn Engine, allowing you to target specific code segments for testing. If you can emulate the desired code with the Unicorn Engine, you can effectively use AFL-Unicorn for fuzzing purposes. The Unicorn Mode incorporates block-edge instrumentation similar to what AFL's QEMU mode employs, enabling AFL to gather block coverage information from the emulated code snippets to drive its input generation process. The key to this functionality lies in the careful setup of a Unicorn-based test harness, which is responsible for loading the target code, initializing the state, and incorporating data mutated by AFL from its disk storage. After establishing these parameters, the test harness emulates the binary code of the target, and upon encountering a crash or error, triggers a signal to indicate the issue. While this framework has primarily been tested on Ubuntu 16.04 LTS, it is designed to be compatible with any operating system that can run both AFL and Unicorn without issues. With this setup, developers can enhance their fuzzing efforts and improve their binary analysis workflows significantly.
API Access
Has API
API Access
Has API
Integrations
.NET
GitLab
Python
Visual Studio
XML
Pricing Details
Free
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Peach Tech
Founded
2004
Country
United States
Website
peachtech.gitlab.io/peach-fuzzer-community/
Vendor Details
Company Name
Battelle
Website
github.com/Battelle/afl-unicorn