Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
American fuzzy lop is a security-focused fuzzer that utilizes a unique form of compile-time instrumentation along with genetic algorithms to automatically generate effective test cases that can uncover new internal states within the targeted binary. This approach significantly enhances the functional coverage of the code being fuzzed. Additionally, the compact and synthesized test cases produced by the tool can serve as a valuable resource for initiating other, more demanding testing processes in the future. Unlike many other instrumented fuzzers, afl-fuzz is engineered for practicality, boasting a minimal performance overhead while employing a diverse array of effective fuzzing techniques and strategies for minimizing effort. It requires almost no setup and can effortlessly manage complicated, real-world scenarios, such as those found in common image parsing or file compression libraries. As an instrumentation-guided genetic fuzzer, it excels at generating complex file semantics applicable to a wide variety of challenging targets, making it a versatile choice for security testing. Its ability to adapt to different environments further enhances its appeal for developers seeking robust solutions.
Description
Identify similar phishing domains that could be leveraged by attackers against your organization. Investigate the potential issues users may face when attempting to type your domain name accurately. Look for fraudulent domains that adversaries might exploit for malicious purposes, as this can help in identifying typosquatters, phishing schemes, scams, and instances of brand impersonation. This information serves as a valuable resource for enhanced targeted threat intelligence. The process of DNS fuzzing automates the detection of potentially harmful domains aimed at your organization by creating a vast array of variations from a specified domain name and checking if any of these variations are active. Furthermore, it can produce fuzzy hashes of web pages to identify ongoing phishing attempts, instances of brand impersonation, and additional threats, thereby providing a more comprehensive security measure. By utilizing this tool, organizations can significantly bolster their defenses against evolving cyber threats.
API Access
Has API
No
API Access
Has API
No
Integrations
C
Yes
C++
Yes
ClusterFuzz
Yes
FreeBSD
Yes
Go
Yes
Google ClusterFuzz
Yes
Java
Yes
NetBSD
Yes
OCaml
Yes
Objective-C
Yes
Integrations
C
No
C++
No
ClusterFuzz
No
FreeBSD
No
Go
No
Google ClusterFuzz
No
Java
No
NetBSD
No
OCaml
No
Objective-C
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
No
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
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
Country
United States
Website
github.com/google/AFL
Vendor Details
Company Name
dnstwist
Website
dnstwist.it/