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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 No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

.NET Yes 
GitLab Yes 
Python Yes 
Visual Studio Yes 
XML Yes 

Integrations

.NET No 
GitLab No 
Python No 
Visual Studio No 
XML No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App Yes 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

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

Product Features

Product Features

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