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Description
Ffuf is a high-speed web fuzzer developed in Go that allows users to conduct scans on live hosts through various lessons and scenarios, which can be executed either locally via a Docker container or through an online hosted version. It offers virtual host discovery capabilities that operate independently of DNS records. To effectively utilize Ffuf, users need to provide a wordlist containing the inputs they want to test. You can specify one or multiple wordlists directly in the command line, and if you are using more than one, it's important to assign a custom keyword to manage them correctly. Ffuf processes the first entry of the initial wordlist against all entries in the subsequent wordlist, then moves on to the second entry of the first wordlist, repeating this process until all combinations have been tested. This method ensures thorough coverage of potential inputs, and there are numerous options available for further customizing the requests made during the fuzzing process. By leveraging these features, users can optimize their web vulnerability assessments effectively.
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
Docker
Go
JSON
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
Ffuf
Website
github.com/ffuf/ffuf
Vendor Details
Company Name
Battelle
Website
github.com/Battelle/afl-unicorn