Best Fuzz Testing Tools for Linux - Page 2

BFuzz is an input-based fuzzer tool that takes HTML as an input, opens up your browser with a new instance, and passes multiple test cases generated by domato which is present in the recurve folder of BFuzz, more over BFuzz is an automation that performs the same task repeatedly and it doesn't mangle any test cases. Running BFuzz will ask for the option of whether to fuzz Chrome or Firefox, however, this will open Firefox from recurve and create the logs on the terminal. BFuzz is a small script that enables you to open the browser and run test cases. The test cases in recurve are generated by the domato generator and contain the main script. It contains additional helper code for DOM fuzzing.

Atheris is a coverage-guided Python fuzzing engine. It supports fuzzing of Python code, but also native extensions written for CPython. Atheris is based on libFuzzer. When fuzzing native code, Atheris can be used to catch extra bugs. Atheris supports Linux (32- and 64-bit) and Mac OS X, with Python versions 3.6-3.10. It comes with a built-in libFuzzer, which is fine for fuzzing Python code. If you plan to fuzz native extensions, you may need to build from source to ensure the libFuzzer version in Atheris matches your Clang version. Atheris relies on libFuzzer, which is distributed with Clang. Apple Clang doesn't come with libFuzzer, so you'll need to install a new version of LLVM. Atheris is based on a coverage-guided mutation-based fuzzer (LibFuzzer). This has the advantage of not requiring any grammar definition for generating inputs, making its setup easier. The disadvantage is that it will be harder for the fuzzer to generate inputs for code that parses complex data types.

Our platform uses various security techniques, including coverage-guided and feedback-based fuzz testing, to automatically generate millions of test cases that trigger hard-to-find bugs deep within your application. This white-box approach protects against edge cases and speeds up development. Advanced fuzzing engines generate inputs that maximize code coverage. Powerful bug detectors check for errors during code execution. Uncover true vulnerabilities only. Get the input and stack trace as proof, so you can reliably reproduce errors every time. AI white-box testing uses data from all previous test runs to continuously learn the inner-workings of your application, triggering security-critical bugs with increasingly high precision.

Fuzz testing or fuzzing is a software testing technique, that basically consists in finding implementation bugs using malformed/semi-malformed data injection in an automated fashion. Let’s consider an integer in a program, which stores the result of a user’s choice between 3 questions. When the user picks one, the choice will be 0, 1, or 2, which makes three practical cases. Integers are stored as a static size variable. If the default switch case hasn’t been implemented securely, the program may crash and lead to “classical” security issues. Fuzzing is the art of automatic bug finding, and its role is to find software implementation faults and identify them if possible. A fuzzer is a program that automatically injects semi-random data into a program/stack and detects bugs. The data-generation part is made of generators, and vulnerability identification relies on debugging tools. Generators usually use combinations of static fuzzing vectors.

CI Fuzz ensures robust and secure code with test coverage up to 100%. Use CI Fuzz from the command line or in the IDE of choice to generate thousands of test cases automatically. CI Fuzz analyzes code as it runs, just like a unit test, but with AI support to efficiently cover all paths through the code. Uncover real bugs in real-time and say goodbye to theoretical issues and false positives. Find real issues with all the information needed to quickly reproduce and fix them. Test your code with maximum code coverage and automatically detect typical security-relevant bugs like injections and remote code executions automatically in one go. Get fully covered to deliver the highest quality software. Conduct real-time code analysis with CI Fuzz. Take unit tests to the next level. It employs AI for comprehensive code path coverage and the automatic generation of thousands of test cases. Maximize pipeline performance that doesn't compromise software integrity.

Defensics Fuzz Testing is a comprehensive, versatile, automated black box fuzzer that enables organizations to efficiently and effectively discover and remediate security weaknesses in software. The generational fuzzer takes an intelligent, targeted approach to negative testing. Advanced file and protocol template fuzzers enable users to build their own test cases. The SDK allows expert users to use the Defensics framework to develop their own test cases. Defensics is a black box fuzzer, meaning it doesn’t require source code to run. With Defensics, users can secure their cyber supply chain to ensure the interoperability, robustness, quality, and security of software and devices before introducing them into IT or lab environments. Defensics fits nearly any development workflow, whether in a traditional SDL or CI environment. Its API and data export capabilities also enable it to integrate with surrounding technologies, making it a true plug-and-play fuzzer.

BlackArch is a Linux pentesting distribution based on ArchLinux. BlackArch Fuzzer provides packages that use the fuzz testing principle.

ClusterFuzz is a scalable fuzzing infrastructure that finds security and stability issues in software. Google uses ClusterFuzz to fuzz all Google products and as the fuzzing backend for OSS-Fuzz. ClusterFuzz provides many features to seamlessly integrate fuzzing into a software project’s development process. Fully automatic bug filing, triage, and closing for various issue trackers. Supports multiple coverages guided fuzzing engines for optimal results (with ensemble fuzzing and fuzzing strategies). Statistics for analyzing fuzzer performance, and crash rates. Easy to use web interface for management and viewing crashes. Support for various authentication providers using Firebase. Support for black-box fuzzing, test case minimization, and regression finding through bisection.