Best Fuzz Testing Tools for Linux

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Peach is a SmartFuzzer that is capable of performing both generation and mutation-based fuzzing. Peach requires the creation of Peach Pit files that define the structure, type information, and relationships in the data to be fuzzed. It additionally allows for the configuration of a fuzzing run including selecting a data transport (publisher), logging interface, etc. Peach has been under active development since 2004 and is in its third major version. Fuzzing continues to be the fastest way to find security issues and test for bugs. Effective hardware fuzzing with Peach will introduce students to the fundamentals of device fuzzing. Peach was designed to fuzz any type of data consumer from servers to embedded devices. Researchers, corporations, and governments already use Peach to find vulnerabilities in hardware. This course will focus on using Peach to target embedded devices and collect information from the device in the event of a crash.

Etheno is an Ethereum-testing, JSON RPC multiplexer, analysis tool wrapper, and test integration tool. It eliminates the complexity of setting up analysis tools like Echidna on large, multi-contract projects. If you are a smart contract developer, you should use Etheno to test your contracts. If you are an Ethereum client developer, you should use Etheno to perform differential testing on your implementation. Etheno runs a JSON RPC server that can multiplex calls to one or more clients. API for filtering and modifying JSON RPC calls. Enables differential testing by sending JSON RPC sequences to multiple Ethereum clients. Deploy to and interact with multiple networks at the same time. Integration with test frameworks like Ganache and Truffle. Run a local test network with a single command. Use our prebuilt Docker container to quickly install and try Etheno. Etheno can be used in many different ways and therefore, has numerous command-line argument combinations.

Solidity Fuzzing Boilerplate is a template repository intended to ease fuzzing components of Solidity projects, especially libraries. Write tests once and run them with both Echidna and Foundry's fuzzing. Fuzz components that use incompatible Solidity versions by deploying those into a Ganache instance via Etheno. Use HEVM's FFI cheat code to generate complex fuzzing inputs or to compare outputs with non-EVM executables while doing differential fuzzing. Publish your fuzzing experiments without worrying about licensing by extending the shell script to download specific files. Turn off FFI if you don't intend to make use of shell commands from your Solidity contracts. Note that FFI is slow and should only be used as a workaround. It can be useful for testing against things that are difficult to implement within Solidity and already exist in other languages. Before executing tests of a project that has FFI enabled, be sure to check what commands are actually being executed.

The hevm project is an implementation of the Ethereum Virtual Machine (EVM) made specifically for symbolic execution, unit testing, and debugging of smart contracts. It is developed by DappHub and integrates especially well with the DappHub tool suite. The hevm command line program can symbolically execute smart contracts, run unit tests, interactively debug contracts while showing the Solidity source, or run arbitrary EVM code. Computations can be performed using a local state set up in a testing harness or fetched on demand from live networks using RPC calls. Run a symbolic execution against the given parameters, searching for assertion violations. One can also specialize specific arguments to a function signature while leaving others abstract. hevm uses an eager approach for symbolic execution, meaning that it will first attempt to explore all branches of the program.

Tayt is a StarkNet smart contract fuzzer. We recommend using a Python virtual environment. When starting you will see the properties to be checked and the external functions used to generate a sequence of transactions. Eventually, if a property is violated a call sequence will be presented with the order of functions to be called, the respective arguments passed, the caller address, and the events emitted. With Tayt, you can test a contract that deploys other contracts.

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OSS-Fuzz offers continuous fuzzing for open source software. Fuzz testing is a well-known technique for uncovering programming errors in software. Many of these detectable errors, like buffer overflow, can have serious security implications. Google has found thousands of security vulnerabilities and stability bugs by deploying guided in-process fuzzing of Chrome components, and we now want to share that service with the open source community. OSS-Fuzz aims to make common open source software more secure and stable by combining modern fuzzing techniques with scalable, distributed execution. Projects that do not qualify for OSS-Fuzz can run their own instances of ClusterFuzz or ClusterFuzzLite. Currently, OSS-Fuzz supports C/C++, Rust, Go, Python, and Java/JVM code. Other languages supported by LLVM may work too. OSS-Fuzz supports fuzzing x86_64 and i386 builds.

Awesome Fuzzing is a list of fuzzing resources including books, courses, both free and paid, videos, tools, tutorials, and vulnerable applications to practice in order to learn fuzzing and initial phases of exploit development like root cause analysis. Courses/training videos on fuzzing, videos talking about fuzzing techniques, tools, and best practices. Conference talks and tutorials, blogs, tools that help in fuzzing applications, and fuzzers that help in fuzzing applications that use network-based protocols like HTTP, SSH, SMTP, etc. Search and pick the exploits, that have respective apps available for download, and reproduce the exploit by using the fuzzer of your choice. Set of tests for fuzzing engines. Includes different well-known bugs. A corpus, including various file formats for fuzzing multiple targets in the fuzzing literature.

Fuzzing is a powerful strategy to find bugs in software. The idea is quite simple, which is to generate a large number of randomly malformed inputs for the software to parse and see what happens. If the program crashes then something is likely wrong. While fuzzing is a well-known strategy, it is surprisingly easy to find bugs, often with security implications, in widely used software. Memory access errors are the errors most likely to be exposed when fuzzing software that is written in C/C++. While they differ in the details, the core problem is often the same, the software reads or writes to the wrong memory locations. A modern Linux or BSD system ships a large number of basic tools that do some kind of file displaying and parsing. In their current state, most of these tools are not suitable for untrusted inputs. On the other hand, we have powerful tools these days that allow us to find and analyze these bugs.

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.

American fuzzy lop is a security-oriented fuzzer that employs a novel type of compile-time instrumentation and genetic algorithms to automatically discover clean, interesting test cases that trigger new internal states in the targeted binary. This substantially improves the functional coverage for the fuzzed code. The compact synthesized corpora produced by the tool are also useful for seeding other, more labor or resource-intensive testing regimes down the road. Compared to other instrumented fuzzers, afl-fuzz is designed to be practical, it has a modest performance overhead, uses a variety of highly effective fuzzing strategies and effort minimization tricks, requires essentially no configuration, and seamlessly handles complex, real-world use cases, say, common image parsing or file compression libraries. It's an instrumentation-guided genetic fuzzer capable of synthesizing complex file semantics in a wide range of non-trivial targets.