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Description
LibFuzzer serves as an in-process, coverage-guided engine for evolutionary fuzzing. By being linked directly with the library under examination, it injects fuzzed inputs through a designated entry point, or target function, allowing it to monitor the code paths that are executed while creating variations of the input data to enhance code coverage. The coverage data is obtained through LLVM’s SanitizerCoverage instrumentation, ensuring that users have detailed insights into the testing process. Notably, LibFuzzer continues to receive support, with critical bugs addressed as they arise. To begin utilizing LibFuzzer with a library, one must first create a fuzz target—this function receives a byte array and interacts with the API being tested in a meaningful way. Importantly, this fuzz target operates independently of LibFuzzer, which facilitates its use alongside other fuzzing tools such as AFL or Radamsa, thereby providing versatility in testing strategies. Furthermore, the ability to leverage multiple fuzzing engines can lead to more robust testing outcomes and clearer insights into the library's vulnerabilities.
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
API Access
Has API
Integrations
Atheris
C
C++
ClusterFuzz
Fuzzbuzz
Google ClusterFuzz
Jazzer
Integrations
Atheris
C
C++
ClusterFuzz
Fuzzbuzz
Google ClusterFuzz
Jazzer
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
LLVM Project
Founded
2003
Website
llvm.org/docs/LibFuzzer.html
Vendor Details
Company Name
dnstwist
Website
dnstwist.it/