Best Fuzz Testing Tools in Japan

Hands-on security testers need the best tools for the job. Tools you have faith in, and enjoy using all day long. The tools that other professionals trust. Burp Suite Professional is the web security tester's toolkit of choice. Use it to automate repetitive testing tasks, then dig deeper with its expert-designed manual and semi-automated security testing tools. Burp Suite Professional can help you to test for OWASP top 10 vulnerabilities, as well as the very latest hacking techniques. Smart automation works in concert with expert-designed manual tools, to save you time. Optimize your workflow, and do more of what you do best. Burp Scanner can navigate and scan JavaScript-heavy single-page applications (SPAs), scan APIs, and enable the prerecording of complex authentication sequences. A toolkit designed and used by professional testers. Utilize features like the ability to record everything you did on an engagement and a powerful search function to improve efficiency and reliability.

LibFuzzer is an in-process, coverage-guided, evolutionary fuzzing engine. LibFuzzer is linked with the library under test, and feeds fuzzed inputs to the library via a specific fuzzing entry point (or target function); the fuzzer then tracks which areas of the code are reached, and generates mutations on the corpus of input data in order to maximize the code coverage. The code coverage information for libFuzzer is provided by LLVM’s SanitizerCoverage instrumentation. LibFuzzer is still fully supported in that important bugs will get fixed. The first step in using libFuzzer on a library is to implement a fuzz target, a function that accepts an array of bytes and does something interesting with these bytes using the API under test. Note that this fuzz target does not depend on libFuzzer in any way so it is possible and even desirable to use it with other fuzzing engines like AFL and/or Radamsa.

Honggfuzz is a security-oriented software fuzzer. Supports evolutionary, feedback-driven fuzzing based on code coverage (SW and HW-based). It’s multi-process and multi-threaded, there’s no need to run multiple copies of your fuzzer, as Honggfuzz can unlock the potential of all your available CPU cores with a single running instance. The file corpus is automatically shared and improved between all fuzzed processes. It’s blazingly fast when the persistent fuzzing mode is used. A simple/empty LLVMFuzzerTestOneInput function can be tested with up to 1mo iteration per second on a relatively modern CPU. Has a solid track record of uncovered security bugs, the only (to date) vulnerability in OpenSSL with the critical score mark was discovered by Honggfuzz. As opposed to other fuzzers, it will discover and report hijacked/ignored signals from crashes (intercepted and potentially hidden by a fuzzed program).

Fuzzapi is a tool used for REST API pentesting and uses API Fuzzer and provides UI solutions for gem.

Boofuzz is a fork of and the successor to the venerable Sulley fuzzing framework. Besides numerous bug fixes, Boofuzz aims for extensibility. Like Sulley, Boofuzzincorporates all the critical elements of a fuzzer like easy and quick data generation, instrumentation and failure detection, target reset after failure, and recording of test data. Much easier install experience and support for arbitrary communications mediums. Built-in support for serial fuzzing, ethernet- and IP-layer, UDP broadcast. Better recording of test data, consistent, thorough, and clear. Test result CSV export and extensible instrumentation/failure detection. Boofuzz installs as a Python library used to build fuzzer scripts. It is strongly recommended to set up Boofuzz in a virtual environment.

Thousands of autonomously generated tests run every minute to pinpoint vulnerabilities and guide rapid remediation. Mayhem takes the guesswork out of untested code by autonomously generating test suites that produce actionable results. No need to recompile the code, since Mayhem works with dockerized images. Self-learning ML continually runs thousands of tests per second probing for crashes and defects, so developers can focus on features. Continuous testing runs in the background to surface new defects and increase code coverage. Mayhem delivers a copy/paste reproduction and backtrace for every defect, then prioritizes them based on your risk. See all the results, duplicated and prioritized by what you need to fix now. Mayhem fits into your existing build pipeline and development tools, putting actionable results at your developers' fingertips. No matter what language or tools your team uses.

APIFuzzer reads your API description and step-by-step fuzzes the fields to validate if your application can cope with the fuzzed parameters, and it does not require coding. Parse API definition from a local file or remote URL. JSON and YAML file format support. All HTTP methods are supported. Fuzzing of the request body, query string, path parameter, and request header is supported. Relies on random mutations and supports CI integration. Generate JUnit XML test report format. Send a request to an alternative URL. Support HTTP basic auth from the configuration. Save the report of the failed test in JSON format into the pre-configured folder.

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.

API Fuzzer allows to fuzz-request attributes using common pentesting techniques and lists vulnerabilities. API Fuzzer gem accepts an API request as input and returns vulnerabilities possible in the API. Cross-site scripting vulnerability, SQL injection, blind SQL injection, XML external entity vulnerability, IDOR, API rate limiting, open redirect vulnerabilities, information disclosure flaws, info leakage through headers, and cross-site request forgery vulnerability.

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.

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.