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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
ToothPicker serves as an innovative in-process, coverage-guided fuzzer specifically designed for iOS, focusing on the Bluetooth daemon and various Bluetooth protocols. Utilizing FRIDA as its foundation, this tool can be tailored to function on any platform compatible with FRIDA. The repository also features an over-the-air fuzzer that showcases an example implementation for fuzzing Apple's MagicPairing protocol through InternalBlue. Furthermore, it includes the ReplayCrashFile script, which aids in confirming any crashes identified by the in-process fuzzer. This simple fuzzer operates by flipping bits and bytes in inactive connections, lacking coverage or injection, yet it serves effectively as a demonstration and is stateful. It requires only Python and Frida to operate, eliminating the need for additional modules or installations. Built upon the frizzer codebase, it's advisable to establish a virtual Python environment for optimal performance with frizzer. Notably, with the introduction of the iPhone XR/Xs, the PAC (Pointer Authentication Code) feature has been implemented. This advancement underscores the necessity for continuous adaptation of fuzzing tools like ToothPicker to keep pace with evolving iOS security measures.
API Access
Has API
No
API Access
Has API
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
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
Yes
iPad App
Yes
Android App
No
Windows
No
Mac
Yes
Linux
No
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
Ffuf
Website
github.com/ffuf/ffuf
Vendor Details
Company Name
Secure Mobile Networking Lab
Website
github.com/seemoo-lab/toothpicker