Best Fuzz Testing Tools in Australia

Without access to source code, discover and certify security weaknesses in any product. Any protocol or hardware can be tested with beSTORM. This includes those used in IoT and process control, CANbus-compatible automotive and aerospace. Realtime fuzzing is possible without needing access to the source code. There are no cases to download. One platform, one GUI to use, with more than 250+ pre-built protocol testing modules, and the ability to create custom and proprietary ones. Identify security flaws before deployment. These are the ones that are most commonly discovered by outside actors after release. In your own testing center, certify vendor components and your applications. Software module self-learning and propriety testing. Scalability and customization for all business sizes. Automate the generation and delivery of near infinite attack vectors. Also, document any product failures. Record every pass/fail and manually engineer the exact command that caused each failure.

Effective security testers require top-notch tools that they can rely on and enjoy utilizing throughout their workday. The tools that have gained the trust of seasoned professionals. Among these, Burp Suite Professional stands out as the preferred toolkit for web security testing. This software allows users to automate tedious testing processes while also providing sophisticated manual and semi-automated security testing features for more in-depth analysis. With Burp Suite Professional, you can effectively assess vulnerabilities listed in the OWASP top 10, along with the newest hacking methods. Its intelligent automation complements expertly crafted manual tools, streamlining your workflow so you can focus on your core competencies. The Burp Scanner is adept at navigating and scanning JavaScript-heavy single-page applications (SPAs) and APIs, while also facilitating the prerecording of intricate authentication sequences. This toolkit, crafted by and for professional testers, includes valuable features such as the ability to document your actions during an engagement and a robust search function to enhance both efficiency and accuracy. Overall, Burp Suite Professional empowers security testers to elevate their testing practices and achieve superior results.

Peach is an advanced SmartFuzzer that excels in both generation and mutation-based fuzzing techniques. It necessitates the creation of Peach Pit files, which outline the data's structure, type information, and interrelations for effective fuzzing. In addition, Peach provides customizable configurations for a fuzzing session, such as selecting a data transport (publisher) and logging interface. Since its inception in 2004, Peach has undergone continuous development and is currently in its third major iteration. Fuzzing remains one of the quickest methods to uncover security vulnerabilities and identify bugs in software. By utilizing Peach for hardware fuzzing, students will gain insights into the essential principles of device fuzzing. Designed to address any data consumer, Peach can be applied to servers as well as embedded devices. A wide array of users, including researchers, companies, and government agencies, leverage Peach to detect hardware vulnerabilities. This course will specifically concentrate on employing Peach to target embedded devices while also gathering valuable information in case of a device crash, thus enhancing the understanding of fuzzing techniques in practical scenarios.

Etheno serves as a versatile tool for Ethereum testing, acting as a JSON RPC multiplexer, an analytical tool wrapper, and a mechanism for test integration. It simplifies the challenges associated with configuring analysis tools such as Echidna, particularly for extensive multi-contract projects. Smart contract developers are encouraged to leverage Etheno for thorough contract testing, while Ethereum client developers can utilize it for effective differential testing of their implementations. By operating an efficient JSON RPC server, Etheno can route calls to multiple clients seamlessly. It also offers an API that allows for the filtering and alteration of JSON RPC calls, facilitating differential testing by dispatching JSON RPC sequences across various Ethereum clients. Furthermore, Etheno allows users to deploy and engage with multiple networks simultaneously and integrates smoothly with popular testing frameworks like Ganache and Truffle. With the capability to launch a local test network using a single command, Etheno ensures ease of setup. Users can also take advantage of a prebuilt Docker container for a quick installation and trial of Etheno. Given its versatility, Etheno offers a wide array of command-line arguments, catering to diverse testing needs and preferences. This flexibility makes it an invaluable resource for anyone involved in Ethereum development.

The Solidity Fuzzing Boilerplate serves as a foundational template designed to simplify the fuzzing process for various components within Solidity projects, particularly libraries. By writing tests just once, developers can easily execute them using both Echidna and Foundry's fuzzing tools. In instances where components require different versions of Solidity, these can be deployed into a Ganache instance with the help of Etheno. To generate intricate fuzzing inputs or to conduct differential fuzzing by comparing outputs with non-EVM executables, HEVM's FFI cheat code can be utilized effectively. Additionally, you can publish the results of your fuzzing experiments without concerns about licensing issues by modifying the shell script to retrieve specific files. If you do not plan to use shell commands from your Solidity contracts, it is advisable to disable FFI since it can be slow and should primarily serve as a workaround. This functionality proves beneficial when testing against complex implementations that are challenging to replicate in Solidity but are available in other programming languages. It is essential to review the commands being executed before running tests in projects that have FFI activated, ensuring a clear understanding of the operations taking place. Always prioritize clarity in your testing approach to maintain the integrity and effectiveness of your fuzzing efforts.

The hevm project serves as a tailored implementation of the Ethereum Virtual Machine (EVM) designed for tasks like symbolic execution, unit testing, and debugging of smart contracts. Created by DappHub, it seamlessly integrates with the suite of tools offered by the same developer. The hevm command line interface enables users to symbolically execute smart contracts, conduct unit tests, debug contracts interactively while displaying the Solidity source code, or execute any arbitrary EVM code. It allows computations to be carried out using a local state established within a testing framework or retrieved from live networks through RPC calls. Users can initiate symbolic execution with specified parameters to detect assertion violations and can also customize certain function signature arguments while keeping others abstract. Notably, hevm adopts an eager approach to symbolic execution, meaning that it initially strives to investigate all branches of the program. This comprehensive method enhances the reliability and robustness of smart contract development and testing.

Tayt serves as a fuzzer specifically designed for StarkNet smart contracts. It is advisable to utilize a Python virtual environment for this purpose. Upon initiation, users will be presented with the properties that need verification alongside the external functions employed to create a series of transactions. If any property is found to be violated, a detailed call sequence will be displayed, listing the order of function invocations, the arguments provided, the address of the caller, and any events triggered. Additionally, Tayt allows users to evaluate contracts that are capable of deploying other contracts, enhancing its utility in smart contract testing. This capability makes it an essential tool for developers looking to ensure the robustness and security of their smart contract implementations.

Strengthen your blockchain systems with our exceptional audit services that guarantee unmatched security in the decentralized landscape. If you're losing sleep over the potential loss of your assets to cybercriminals, explore our range of services and alleviate your concerns. Our seasoned professionals conduct thorough examinations of your code to identify weaknesses within your smart contracts. We enhance the security of your blockchain solutions by addressing risks through a combination of security design, comprehensive assessment, audit, and compliance services. Our independent team of skilled penetration testers engages in a meticulous process to uncover vulnerabilities and system exploits. As champions of creating a safer environment for all, we provide a thorough and systematic analysis that elevates the overall security of your product. Additionally, the recovery of funds is just as crucial as ensuring a security audit. With our transaction risk monitoring system, you can monitor user funds effectively, thereby increasing user trust and confidence in your platform. By prioritizing these aspects, we aim to foster a secure future for blockchain applications.

OSS-Fuzz provides ongoing fuzz testing for open source applications, a method renowned for identifying programming flaws. Such flaws, including buffer overflow vulnerabilities, can pose significant security risks. Through the implementation of guided in-process fuzzing on Chrome components, Google has discovered thousands of security weaknesses and stability issues, and now aims to extend this beneficial service to the open source community. The primary objective of OSS-Fuzz is to enhance the security and stability of frequently used open source software by integrating advanced fuzzing methodologies with a scalable and distributed framework. For projects that are ineligible for OSS-Fuzz, there are alternatives available, such as running personal instances of ClusterFuzz or ClusterFuzzLite. At present, OSS-Fuzz is compatible with languages including C/C++, Rust, Go, Python, and Java/JVM, with the possibility of supporting additional languages that are compatible with LLVM. Furthermore, OSS-Fuzz facilitates fuzzing for both x86_64 and i386 architecture builds, ensuring a broad range of applications can benefit from this innovative testing approach. With this initiative, we hope to build a safer software ecosystem for all users.

Fuzzing serves as an effective method for identifying software bugs. Essentially, it involves generating numerous randomly crafted inputs for the software to process in order to observe the outcomes. When a program crashes, it usually indicates that there is a problem. Despite being a widely recognized approach, it is often surprisingly straightforward to uncover bugs, including those with potential security risks, in commonly used software. Memory access errors, especially prevalent in programs developed in C/C++, tend to be the most frequently identified issues during fuzzing. While the specifics may vary, the underlying problem is typically that the software accesses incorrect memory locations. Modern Linux or BSD systems come equipped with a variety of fundamental tools designed for file display and parsing; however, most of these tools are ill-equipped to handle untrusted inputs in their present forms. Conversely, we now possess advanced tools that empower developers to detect and investigate these vulnerabilities more effectively. These innovations not only enhance security but also contribute to the overall stability of software systems.

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.

American fuzzy lop is a security-focused fuzzer that utilizes a unique form of compile-time instrumentation along with genetic algorithms to automatically generate effective test cases that can uncover new internal states within the targeted binary. This approach significantly enhances the functional coverage of the code being fuzzed. Additionally, the compact and synthesized test cases produced by the tool can serve as a valuable resource for initiating other, more demanding testing processes in the future. Unlike many other instrumented fuzzers, afl-fuzz is engineered for practicality, boasting a minimal performance overhead while employing a diverse array of effective fuzzing techniques and strategies for minimizing effort. It requires almost no setup and can effortlessly manage complicated, real-world scenarios, such as those found in common image parsing or file compression libraries. As an instrumentation-guided genetic fuzzer, it excels at generating complex file semantics applicable to a wide variety of challenging targets, making it a versatile choice for security testing. Its ability to adapt to different environments further enhances its appeal for developers seeking robust solutions.

Honggfuzz is a software fuzzer focused on enhancing security through its advanced fuzzing techniques. It employs evolutionary and feedback-driven methods that rely on both software and hardware-based code coverage. This tool is designed to operate in a multi-process and multi-threaded environment, allowing users to maximize their CPU's potential without needing to launch multiple fuzzer instances. The file corpus is seamlessly shared and refined across all processes undergoing fuzzing, which greatly enhances efficiency. When persistent fuzzing mode is activated, Honggfuzz exhibits remarkable speed, capable of executing a simple or empty LLVMFuzzerTestOneInput function at an impressive rate of up to one million iterations per second on modern CPUs. It has a proven history of identifying security vulnerabilities, including the notable discovery of the only critical vulnerability in OpenSSL to date. Unlike other fuzzing tools, Honggfuzz can detect and report on hijacked or ignored signals that result from crashes, making it a valuable asset for identifying hidden issues within fuzzed programs. Its robust features make it an essential tool for security researchers aiming to uncover hidden flaws in software systems.

Ffuf is a high-speed web fuzzer developed in Go that allows users to conduct scans on live hosts through various lessons and scenarios, which can be executed either locally via a Docker container or through an online hosted version. It offers virtual host discovery capabilities that operate independently of DNS records. To effectively utilize Ffuf, users need to provide a wordlist containing the inputs they want to test. You can specify one or multiple wordlists directly in the command line, and if you are using more than one, it's important to assign a custom keyword to manage them correctly. Ffuf processes the first entry of the initial wordlist against all entries in the subsequent wordlist, then moves on to the second entry of the first wordlist, repeating this process until all combinations have been tested. This method ensures thorough coverage of potential inputs, and there are numerous options available for further customizing the requests made during the fuzzing process. By leveraging these features, users can optimize their web vulnerability assessments effectively.

ToothPicker serves as an innovative in-process, coverage-guided fuzzer specifically designed for iOS, focusing on the Bluetooth daemon and various Bluetooth protocols. Utilizing FRIDA as its foundation, this tool can be tailored to function on any platform compatible with FRIDA. The repository also features an over-the-air fuzzer that showcases an example implementation for fuzzing Apple's MagicPairing protocol through InternalBlue. Furthermore, it includes the ReplayCrashFile script, which aids in confirming any crashes identified by the in-process fuzzer. This simple fuzzer operates by flipping bits and bytes in inactive connections, lacking coverage or injection, yet it serves effectively as a demonstration and is stateful. It requires only Python and Frida to operate, eliminating the need for additional modules or installations. Built upon the frizzer codebase, it's advisable to establish a virtual Python environment for optimal performance with frizzer. Notably, with the introduction of the iPhone XR/Xs, the PAC (Pointer Authentication Code) feature has been implemented. This advancement underscores the necessity for continuous adaptation of fuzzing tools like ToothPicker to keep pace with evolving iOS security measures.

AFL-Unicorn provides the capability to fuzz any binary that can be emulated using the Unicorn Engine, allowing you to target specific code segments for testing. If you can emulate the desired code with the Unicorn Engine, you can effectively use AFL-Unicorn for fuzzing purposes. The Unicorn Mode incorporates block-edge instrumentation similar to what AFL's QEMU mode employs, enabling AFL to gather block coverage information from the emulated code snippets to drive its input generation process. The key to this functionality lies in the careful setup of a Unicorn-based test harness, which is responsible for loading the target code, initializing the state, and incorporating data mutated by AFL from its disk storage. After establishing these parameters, the test harness emulates the binary code of the target, and upon encountering a crash or error, triggers a signal to indicate the issue. While this framework has primarily been tested on Ubuntu 16.04 LTS, it is designed to be compatible with any operating system that can run both AFL and Unicorn without issues. With this setup, developers can enhance their fuzzing efforts and improve their binary analysis workflows significantly.

The Fuzzbuzz workflow closely resembles other continuous integration and continuous delivery (CI/CD) testing processes, but it stands out because it necessitates the concurrent execution of multiple jobs, adding several additional steps. As a dedicated fuzz testing platform, Fuzzbuzz simplifies the integration of fuzz tests into developers' code, enabling them to execute these tests within their CI/CD pipelines, which is essential for identifying critical bugs and security vulnerabilities before they reach production. Fuzzbuzz seamlessly blends into your existing environment, providing support from the terminal through to CI/CD. You can easily write a fuzz test using your preferred IDE, terminal, or build tools, and once you push your code changes to CI/CD, Fuzzbuzz will automatically initiate the fuzz testing process on the latest updates. You'll receive notifications about any bugs detected through various channels like Slack, GitHub, or email, ensuring you're always informed. Additionally, as new changes are introduced, regressions are automatically tested and compared against previous results, allowing for continuous monitoring of code stability. The moment a change is detected, Fuzzbuzz builds and instruments your code, ensuring that your development process remains efficient and responsive. This proactive approach helps maintain high-quality code and reduces the risk of deploying flawed software.

Sulley is a comprehensive fuzz testing framework and engine that incorporates various extensible components. In my view, it surpasses the functionality of most previously established fuzzing technologies, regardless of whether they are commercial or available in the public domain. The framework is designed to streamline not only the representation of data but also its transmission and instrumentation processes. As a fully automated fuzzing solution developed entirely in Python, Sulley operates without requiring human intervention. Beyond impressive capabilities in data generation, Sulley offers a range of essential features expected from a contemporary fuzzer. It meticulously monitors network activity and keeps detailed records for thorough analysis. Additionally, Sulley is equipped to instrument and evaluate the health of the target system, with the ability to revert to a stable state using various methods when necessary. It efficiently detects, tracks, and categorizes faults that arise during testing. Furthermore, Sulley has the capability to perform fuzzing in parallel, which dramatically enhances testing speed. It can also autonomously identify unique sequences of test cases that lead to faults, thereby improving the overall effectiveness of the testing process. This combination of features positions Sulley as a powerful tool for security testing and vulnerability detection.

Radamsa serves as a robust test case generator specifically designed for robustness testing and fuzzing, aimed at evaluating how resilient a program is against malformed and potentially harmful inputs. By analyzing sample files containing valid data, it produces a variety of uniquely altered outputs that challenge the software's stability. One of the standout features of Radamsa is its proven track record in identifying numerous bugs in significant programs, alongside its straightforward scriptability and ease of deployment. Fuzzing, a key technique in uncovering unexpected program behaviors, involves exposing the software to a wide range of input types to observe the resultant actions. This process is divided into two main components: sourcing the diverse inputs and analyzing the outcomes, with Radamsa effectively addressing the first component, while a brief shell script generally handles the latter. Testers often possess a general understanding of potential failures and aim to validate whether those concerns are warranted through this method. Ultimately, Radamsa not only simplifies the testing process but also enhances the reliability of software applications by revealing hidden vulnerabilities.

Jazzer, created by Code Intelligence, is a coverage-guided fuzzer designed for the JVM platform that operates within the process. It draws inspiration from libFuzzer, incorporating several of its advanced mutation features powered by instrumentation into the JVM environment. Users can explore Jazzer's autofuzz mode via Docker, which autonomously produces arguments for specified Java functions while also identifying and reporting any unexpected exceptions and security vulnerabilities that arise. Additionally, individuals can utilize the standalone Jazzer binary available in GitHub release archives, which initiates its own JVM specifically tailored for fuzzing tasks. This flexibility allows developers to effectively test their applications for robustness against various edge cases.

FuzzDB was developed to enhance the chances of identifying security vulnerabilities in applications through dynamic testing methods. As the first and most extensive open repository of fault injection patterns, along with predictable resource locations and regex for server response matching, it serves as an invaluable resource. This comprehensive database includes detailed lists of attack payload primitives aimed at fault injection testing. The patterns are organized by type of attack and, where applicable, by the platform, and they are known to lead to vulnerabilities such as OS command injection, directory listings, directory traversals, source code exposure, file upload bypass, authentication bypass, cross-site scripting (XSS), HTTP header CRLF injections, SQL injection, NoSQL injection, and several others. For instance, FuzzDB identifies 56 patterns that might be interpreted as a null byte, in addition to offering lists of frequently used methods and name-value pairs that can activate debugging modes. Furthermore, the resource continuously evolves as it incorporates new findings and community contributions to stay relevant against emerging threats.

ClusterFuzz serves as an expansive fuzzing framework designed to uncover security vulnerabilities and stability flaws in software applications. Employed by Google, it is utilized for testing all of its products and acts as the fuzzing engine for OSS-Fuzz. This infrastructure boasts a wide array of features that facilitate the seamless incorporation of fuzzing into the software development lifecycle. It offers fully automated processes for bug filing, triaging, and resolution across multiple issue tracking systems. The system supports a variety of coverage-guided fuzzing engines, optimizing results through ensemble fuzzing and diverse fuzzing methodologies. Additionally, it provides statistical insights for assessing fuzzer effectiveness and monitoring crash incidence rates. Users can navigate an intuitive web interface that simplifies the management of fuzzing activities and crash reviews. Furthermore, ClusterFuzz is compatible with various authentication systems via Firebase and includes capabilities for black-box fuzzing, minimizing test cases, and identifying regressions through bisection. In summary, this robust tool enhances software quality and security, making it invaluable for developers seeking to improve their applications.

Go-fuzz serves as a coverage-guided fuzzing tool designed specifically for testing Go packages, making it particularly effective for those that handle intricate inputs, whether they are textual or binary in nature. This method of testing is crucial for strengthening systems that need to process data from potentially harmful sources, such as network interactions. Recently, go-fuzz has introduced initial support for fuzzing Go Modules, inviting users to report any issues they encounter with detailed descriptions. It generates random input data, which is often invalid, and the function must return a value of 1 to indicate that the fuzzer should elevate the priority of that input in future fuzzing attempts, provided that it should not be stored in the corpus, even if it uncovers new coverage; a return value of 0 signifies the opposite, while other values are reserved for future enhancements. The fuzz function is required to reside in a package that go-fuzz can recognize, meaning the code under test cannot be located within the main package, although fuzzing of internal packages is permitted. This structured approach ensures that the testing process remains efficient and focused on identifying vulnerabilities in the code.

Wfuzz offers a powerful platform for automating the assessment of web application security, assisting users in identifying and exploiting potential vulnerabilities to enhance the safety of their web applications. Additionally, it can be executed using the official Docker image for convenience. The core functionality of Wfuzz is based on the straightforward principle of substituting any occurrence of the fuzz keyword with a specified payload, which serves as a source of data. This fundamental mechanism enables users to inject various inputs into any field within an HTTP request, facilitating intricate attacks on diverse components of web applications, including parameters, authentication mechanisms, forms, directories and files, headers, and more. Wfuzz's scanning capabilities for web application vulnerabilities are further enhanced by its plugin support, which allows for a wide range of functionalities. As a completely modular framework, Wfuzz invites even novice Python developers to contribute easily, as creating plugins is a straightforward process that requires only a few minutes to get started. By harnessing the power of Wfuzz, security professionals can significantly improve their web application defenses.

Fuzzapi is a specialized tool designed for penetration testing of REST APIs, incorporating an API Fuzzer and offering user interface solutions for developers. Its robust features make it a valuable resource for enhancing the security of API applications.