Best Fuzz Testing Tools for Python

Peach is an SmartFuzzer capable of both mutation-based and generation-based fuzzing. Peach requires that Peach Pit files be created to define the structure, type and relationship information in the data being fuzzed. It also allows the configuration of a run, including selecting a data publisher (transporter), logging API, etc. Peach is in its third version and has been actively developed since 2004. Fuzzing is the fastest method to test for bugs and find security issues. Peach's effective hardware fuzzing will introduce students to device fuzzing fundamentals. Peach can be used to fuzz any type of data consumer, from embedded devices to servers. Researchers, corporations and governments use Peach already to find vulnerabilities in hardware. This course will cover how to use Peach to collect information from embedded devices in the event of an accident.

Tayt is the StarkNet smart contracts fuzzer. We recommend using a Python Virtual Environment. You will see the properties that need to be checked, and the external functions that are used to generate the sequence of transactions. If a property is violated, a call-sequence will be displayed with the order in which functions are to be called, arguments passed, caller address and events emitted. Tayt allows you to test a contract which deploys other contracts.

OSS-Fuzz provides continuous fuzzing to open source software. Fuzz testing is an established technique for detecting programming errors in software. Many of these detectable mistakes, such as buffer overflow, have serious security implications. Google has discovered thousands of security flaws and stability bugs through guided in-process fuzzing of Chrome components. We now want to share this service with the open-source community. OSS-Fuzz aims at making open source software more stable and secure by combining modern fuzzing with scalable, distributive execution. ClusterFuzzLite or ClusterFuzz is available for projects that do not qualify to use OSS-Fuzz. OSS-Fuzz currently supports C/C++ code, Rust code, Go code, Python code, and Java/JVM. Other languages supported by LLVM could also work. OSS-Fuzz can fuzz both x86_64 builds and i386 versions.

American fuzzy lop, a security-oriented fuzzer, uses a novel form of compile-time tooling and genetic algorithms to discover clean test cases that trigger internal states within the binary. This improves the functional coverage of the fuzzed codes. The compact corpora generated by the tool can also be used to seed other, more resource-intensive or labor-intensive testing regimes in the future. Afl-fuzz, in comparison to other instrumented fuzzers, is designed to be practical. It has a modest overhead, uses highly effective fuzzing techniques and effort minimization tricks. It requires little configuration and handles complex real-world use-cases, such as common image parsing and file compression libraries. It's an instrumentation-guided genetic fuzzer capable of synthesizing complex file semantics in a wide range of non-trivial targets.

Boofuzz forks and succeeds the venerable Sulley fuzzing framework. Boofuzz is a fork of the venerable Sulley fuzzing framework. It aims to be extensible, in addition to numerous bug fixes. Boofuzz, like Sulley, incorporates all of the critical elements that make up a fuzzer, such as easy and quick data creation, instrumentation and detection of failures, target reset after failure and recording of test results. Installation is much easier and supports arbitrary communication mediums. Support for serial fuzzing and UDP broadcast. Consistent, thorough and clear recording of test data. Test result CSV export and extensible instrumentation/failure detection. Boofuzz is installed as a Python Library used to create fuzzer scripts. It is highly recommended that Boofuzz be installed in a virtual environment.

ToothPicker, a coverage-guided in-process fuzzer is available for iOS. It was developed specifically to target iOS's Bluetooth Daemon and analyze various Bluetooth Protocols on iOS. It can be adapted for any platform that uses FRIDA as it was built using FRIDA. This repository includes an over the air fuzzer that uses InternalBlue to fuzz Apple’s MagicPairing Protocol. It also contains the ReplayCrashFile, a script that can verify crashes found by the in-process fuzzer. This is a simple fuzzer which only flips bits of bytes from inactive connections. No injection or coverage, but a nice demo. No modules or installation required. ToothPicker was built on frizzer's codebase. It is recommended that you set up a Python virtual environment for frizzer. PAC was introduced with the iPhone XR/Xs.

The Fuzzbuzz testing workflow is very similar with other CI/CD test workflows. Fuzz testing is different from other testing workflows in that it requires multiple jobs to be run simultaneously. This results in some extra steps. Fuzzbuzz provides a fuzz-testing platform. We make it easy for developers to add fuzz testing to their code, and run them within CI/CD. This helps them find critical bugs and vulnerabilities prior to production. Fuzzbuzz integrates seamlessly into your environment. It follows you from the terminal through to CI/CD. Use your own terminal, IDE, or build tool to write a fuzztest in your environment. Fuzzbuzz will run your fuzz tests automatically against your latest code changes when you push to CI/CD. You can be notified via Slack, GitHub or email when bugs are discovered. Regressions are caught as new changes and previous runs are automatically compared. Fuzzbuzz builds and instruments code as soon as changes are detected.

BFuzz uses an input-based fuzzer that accepts HTML as input, opens a new browser instance and runs multiple test cases created by domato, which is located in the recurve directory of BFuzz. BFuzz also automates the same tasks repeatedly without affecting any test cases. BFuzz asks you to choose whether to fuzz Firefox or Chrome. However, it will open Firefox using recurve, and create logs in the terminal. BFuzz allows you to open a browser and run testcases. The test cases generated by domato contain the main script. It contains additional code for DOM fuzzing.

Sulley is an extensible fuzzing engine, and fuzz testing framework. Sulley (IMHO), surpasses the capabilities of many previously published fuzzing techniques, both commercial and public domain. The framework's goal is to simplify data representation, data transmission, and instrumentation. A pure-Python, fully automated and unattended framework for fuzzing. Sulley has not only impressive data generation, but has gone a step further to include many other important aspects that a modern fuzzer should provide. Sulley keeps meticulous records and monitors the network. Sulley monitors and instruments the target's health, capable of reverting back to a known-good state using multiple methods. Sulley tracks, categorizes and detects faults. Sulley can fuzz simultaneously, increasing test speed. Sulley can automatically identify which unique sequence of test cases triggers a fault.

APIFuzzer is a tool that reads your API description, and fuzzes each field step-by-step to determine if your application will be able to handle the fuzzed parameter. It does not require any coding. Parse API definitions from a remote URL or local file. Support for JSON and YAML files. All HTTP methods can be used. Support for fuzzing the request body, path parameter, query string and request header. Supports CI integration and relies on random mutations. Create JUnit XML format for test reports. Send a request using an alternative URL. Support HTTP basic authentication from the configuration. Save the JSON formatted report of the failed tests into the preconfigured folder.

Atheris is an engine for Python fuzzing that uses coverage-guided fuzzing. It supports fuzzing Python code as well as native extensions written in CPython. Atheris is based off libFuzzer. Atheris is a tool that can be used for fuzzing native code to find additional bugs. Atheris supports Linux 32- and 64-bit and Mac OS X with Python versions 3.6-3.10. It comes with an integrated libFuzzer that is suitable for fuzzing Python code. If you want to fuzz native extensions you may have to build Atheris from source in order to match the libFuzzer versions. Atheris relies upon libFuzzer which is distributed along with Clang. Apple Clang does not come with libFuzzer. You'll have to install a different version of LLVM. Atheris is based upon a coverage-guided, mutation-based fuzzer called LibFuzzer. This has the benefit of not requiring a grammar definition to generate inputs. It makes its setup easier. The disadvantage is that the fuzzer will have a harder time generating inputs for complex data types.

Wfuzz is a framework for automating web application security assessments. It could help you secure web applications by finding web application vulnerabilities and exploiting them. You can also run the Wfuzz image from Docker. Wfuzz works on the simple principle that it replaces all references to the fuzz keyword by the value of the payload. In Wfuzz, a payload is a data source. This simple concept allows for any input to be injected into any field of an HTTP Request, allowing for complex web security attacks to be performed in different web application components, such as parameters and authentication, forms, directories/files or headers. Plugins are used to support Wfuzz's vulnerability scanner for web applications. Wfuzz's modular structure makes it easy to contribute, even for the newest Python programmers. The process of creating plugins is easy and takes only a few moments.

Thousands of tests are generated automatically every minute in order to identify vulnerabilities and guide rapid remediation. Mayhem automates the generation of test suites to produce actionable results. Mayhem uses dockerized images, so there is no need to recompile code. Self-learning ML continuously runs thousands of tests every second, probing for defects and crashes. Developers can then focus on features. Continuous testing is run in the background, highlighting new defects and increasing code coverage. Mayhem provides a copy/paste replication and backtrace of every defect. It then prioritizes these based on the risk. All results are duplicated, and sorted by urgency. Mayhem integrates with your existing build pipelines and development tools to provide developers with actionable results. No matter what tools or language your team uses.