Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Boofuzz represents a continuation and enhancement of the established Sulley fuzzing framework. In addition to a variety of bug fixes, Boofuzz emphasizes extensibility and flexibility. Mirroring Sulley, it integrates essential features of a fuzzer, such as rapid data generation, instrumentation, failure detection, and the ability to reset targets after a failure, along with the capability to log test data effectively. It offers a more streamlined installation process and accommodates diverse communication mediums. Furthermore, it includes built-in capabilities for serial fuzzing, as well as support for Ethernet, IP-layer, and UDP broadcasting. The improvements in data recording are notable, providing consistency, clarity, and thoroughness in the results. Users benefit from the ability to export test results in CSV format and enjoy extensible instrumentation and failure detection options. Boofuzz operates as a Python library that facilitates the creation of fuzzer scripts, and setting it up within a virtual environment is highly advisable for optimal performance and organization. This attention to detail and user experience makes Boofuzz a powerful tool for security testing.
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.
API Access
Has API
API Access
Has API
Integrations
Atheris
C
C++
ClusterFuzz
Fuzzbuzz
Google ClusterFuzz
Google Sheets
Jazzer
Microsoft Excel
Python
Integrations
Atheris
C
C++
ClusterFuzz
Fuzzbuzz
Google ClusterFuzz
Google Sheets
Jazzer
Microsoft Excel
Python
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
Boofuzz
Website
boofuzz.readthedocs.io/en/stable/
Vendor Details
Company Name
LLVM Project
Founded
2003
Website
llvm.org/docs/LibFuzzer.html