Alternatives to syzkaller

ClusterFuzz is an advanced fuzzing platform designed to identify security vulnerabilities and stability problems within software applications. Utilized by Google for all its products, it also serves as the fuzzing backend for OSS-Fuzz. This infrastructure offers a plethora of features that facilitate the integration of fuzzing into the development lifecycle of software projects. It includes fully automated processes for bug filing, triage, and resolution across different issue trackers. Moreover, it supports various coverage-guided fuzzing engines to achieve optimal outcomes through techniques like ensemble fuzzing and diverse fuzzing strategies. The platform provides detailed statistics for evaluating fuzzer efficiency and tracking crash rates. Its user-friendly web interface simplifies management tasks and crash examinations, while it also accommodates multiple authentication providers via Firebase. Additionally, ClusterFuzz supports black-box fuzzing, minimizes test cases, and employs regression identification through bisection techniques, making it a comprehensive solution for software testing. The versatility and robustness of ClusterFuzz truly enhance the software development process.

Bugfender serves as a remote logger, crash reporting tool, and platform for in-app user feedback specifically designed for application developers. It captures every action within the app, even in instances where it doesn’t crash, allowing developers to replicate and address issues more efficiently, thus enhancing customer support. With a strong emphasis on user privacy, Bugfender operates efficiently in terms of battery and network usage, maintaining logging capabilities even when the device is offline. This tool empowers developers to identify and rectify bugs before users become aware of them. By logging bugs across all devices and delivering the results in mere seconds, Bugfender allows for prompt fixes, ensuring that users never encounter an error message. This proactive approach helps in achieving 5-Star Ratings. Beyond merely tracking crashes and bugs, Bugfender gathers comprehensive data essential for creating a detailed understanding of user behavior, which is vital for obtaining those coveted five-star ratings. Additionally, it facilitates exceptional customer service by enabling developers to focus on individual users, thereby offering tailored support to enhance user satisfaction and loyalty. Ultimately, Bugfender transforms the way developers interact with their applications and users, fostering a more resilient and user-friendly product.

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.

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.

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.

Awesome Fuzzing serves as a comprehensive compilation of resources for those interested in the field of fuzzing, encompassing an array of materials such as books, both free and paid courses, videos, tools, tutorials, and vulnerable applications ideal for hands-on practice to enhance one's understanding of fuzzing and the early stages of exploit development, including root cause analysis. It features instructional videos focused on fuzzing methodologies, essential tools, and recommended practices, alongside conference presentations, tutorials, and blogs dedicated to the subject. Additionally, it includes software tools that facilitate fuzzing of applications, particularly those utilizing network protocols like HTTP, SSH, and SMTP. Users are encouraged to search for and select exploits linked to downloadable applications, where they can then recreate the exploits with their preferred fuzzer. The resource also encompasses a range of tests tailored for fuzzing engines, highlighting various well-known vulnerabilities and providing a corpus of diverse file formats to enable fuzzing across multiple targets found in the existing fuzzing literature. Ultimately, this collection aims to empower learners with the necessary knowledge and skills to effectively engage with fuzzing techniques and develop their expertise in security testing.

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.

Our platform uses a variety of security techniques, including feedback-based fuzz testing and coverage-guided fuzz testing, in order to generate millions upon millions of test cases that trigger difficult-to-find bugs deep in your application. This white-box approach helps to prevent edge cases and speed up development. Advanced fuzzing engines produce inputs that maximize code coverage. Powerful bug detectors check for errors during code execution. Only uncover true vulnerabilities. You will need the stack trace and input to prove that you can reproduce errors reliably every time. AI white-box testing is based on data from all previous tests and can continuously learn the inner workings of your application. This allows you to trigger security-critical bugs with increasing precision.

Atheris is a Python fuzzing engine guided by coverage, designed to test both Python code and native extensions developed for CPython. It is built on the foundation of libFuzzer, providing an effective method for identifying additional bugs when fuzzing native code. Atheris is compatible with Linux (both 32- and 64-bit) and Mac OS X, supporting Python versions ranging from 3.6 to 3.10. Featuring an integrated libFuzzer, it is well-suited for fuzzing Python applications, but when targeting native extensions, users may need to compile from source to ensure compatibility between the libFuzzer version in Atheris and their Clang installation. Since Atheris depends on libFuzzer, which is a component of Clang, users of Apple Clang will need to install a different version of LLVM, as the default does not include libFuzzer. The implementation of Atheris as a coverage-guided, mutation-based fuzzer (LibFuzzer) simplifies the setup process by eliminating the need for input grammar definition. However, this approach can complicate the generation of inputs for code that processes intricate data structures. Consequently, while Atheris offers ease of use in many scenarios, it may face challenges when dealing with more complex parsing requirements.

Fuzz testing, commonly referred to as fuzzing, is a technique used in software testing that aims to discover implementation errors by injecting malformed or semi-malformed data in an automated way. For example, consider a scenario involving an integer variable within a program that captures a user's selection among three questions; the user's choice can be represented by the integers 0, 1, or 2, resulting in three distinct cases. Since integers are typically stored as fixed-size variables, a failure to implement the default switch case securely could lead to program crashes and various traditional security vulnerabilities. Fuzzing serves as an automated method for uncovering software implementation issues, enabling the identification of bugs when they occur. A fuzzer is a specialized tool designed to automatically inject semi-random data into the program stack, aiding in the detection of anomalies. The process of generating this data involves the use of generators, while the identification of vulnerabilities often depends on debugging tools that can analyze the program's behavior under the influence of the injected data. These generators typically utilize a mixture of established static fuzzing vectors to enhance the testing process, ultimately contributing to more robust software development practices.

CI Fuzz guarantees that your code is both robust and secure, achieving test coverage levels as high as 100%. You can utilize CI Fuzz through the command line or within your preferred integrated development environment (IDE) to automatically generate a vast number of test cases. Similar to a unit test, CI Fuzz analyzes code during execution, leveraging AI to ensure every code path is effectively covered. This tool helps you identify genuine bugs in real-time, eliminating the need to deal with hypothetical problems and erroneous positives. It provides all the necessary details to help you swiftly reproduce and resolve actual issues. By maximizing your code coverage, CI Fuzz also automatically identifies common security vulnerabilities, such as injection flaws and remote code execution risks, all in a single process. Ensure your software is of the highest quality by achieving comprehensive test coverage. With CI Fuzz, you can elevate your unit testing practices, as it harnesses AI for thorough code path analysis and the seamless creation of numerous test cases. Ultimately, it enhances your pipeline's efficiency without sacrificing the integrity of the software being produced. This makes CI Fuzz an essential tool for any developer aiming to improve code quality and security.

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.

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.

With Bugsee, you can access videos, network activity, and logs that pinpoint the source of bugs and crashes in your live applications, eliminating the hassle of trying to reproduce sporadic issues. This tool ensures that all essential data is readily available, providing vital traces from your app to identify the root causes of unexpected behavior. You can view recordings of user interactions, backend communications, and system states that contributed to the issues. Additionally, Bugsee offers statistics on recurring crashes while also allowing you to analyze trends categorized by device type, operating system version, and time. You'll receive detailed information, including the specific filename, method, and line number associated with the crash. Furthermore, you can examine the states of all other threads active during the incident and review all HTTP and HTTPS requests and responses—complete with headers and body content—between your app and the server. The platform even allows you to replay all console logs from your app, perfectly synchronized with the video and network data, giving you a comprehensive view of the situation. This functionality ultimately empowers developers to enhance their applications with greater efficiency and precision.

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.

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.

BlackArch is a penetration testing distribution that builds upon ArchLinux. The BlackArch Fuzzer offers a variety of packages designed to utilize the principles of fuzz testing effectively. This toolset is particularly beneficial for security researchers and developers looking to identify vulnerabilities in their applications.

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.

SystemRescue is a Linux-based toolkit designed for system recovery, available as a bootable medium for managing or fixing your system and data following a crash. Its primary goal is to simplify administrative tasks on your machine, including the creation and modification of hard disk partitions. The toolkit includes a variety of Linux system utilities, such as GParted, fsarchiver, and essential tools for filesystem management, along with basic programs like editors, midnight commander, and network utilities. It is compatible with both Linux and Windows operating systems, making it suitable for use on desktop computers and servers alike. This rescue environment does not require installation, as it can be directly booted from a CD/DVD or USB flash drive, though installation on a hard drive is also an option if desired. Additionally, the kernel is compatible with all major file systems, including ext4, xfs, btrfs, vfat, and ntfs, along with support for network file systems such as Samba and NFS, ensuring a comprehensive recovery solution. The versatility and ease of use of SystemRescue make it an invaluable tool for anyone needing to manage or recover their systems effectively.

The latest release of DragonFly, version 6.2.2, introduces several enhancements, including hardware compatibility for type-2 hypervisors utilizing NVMM, an upgraded amdgpu driver, and the experimental feature of remote-mounting HAMMER2 volumes, alongside a variety of other updates. As a member of the BSD family of operating systems, DragonFly shares its roots with Linux and other BSD variants, adhering to the foundational principles and APIs of UNIX while also diverging in terms of development direction from FreeBSD, NetBSD, and OpenBSD. This divergence allows DragonFly to pursue unique innovations, such as its sophisticated HAMMER filesystem, which offers high performance, built-in mirroring, and historical access capabilities. Additionally, one of the standout features is the implementation of virtual kernels, enabling the execution of a complete kernel as a user process, which facilitates resource management, kernel development, and debugging in an accelerated environment. These features collectively position DragonFly as a distinctive option within its operating system category, appealing to users seeking alternatives beyond conventional solutions.

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.

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.

UserX is a cutting-edge UX analytics platform that provides growth-oriented insights for mobile app teams. Our comprehensive suite of tools, including session recordings, heatmaps, and conversion funnels, empowers companies to gain a deep understanding of user behavior and make data-driven decisions. Session recordings allow you to gain valuable insights into user behavior by closely monitoring and analyzing their interactions with your app. Heatmaps provide a comprehensive understanding of user interactions on every application screen, enabling you to identify the interface elements that are most appealing to users, as well as those that are being overlooked or ignored. Conversion funnels help you determine the stage in the application funnel where users are dropping off and identify the reasons for churn. Crash replays enable you to identify and reproduce technical errors, allowing for quicker resolution of technical bugs. With fast and comprehensive analytics of UserX, companies can easily identify areas of improvement and unlock new growth opportunities. The platform is designed to help brands understand user behavior, track essential metrics, and optimize app's performance.

Boofuzz represents a continuation and enhancement of the established Sulley fuzzing framework. In addition to a variety of bug fixes, Boofuzz emphasizes extensibility and flexibility. Mirroring Sulley, it integrates essential features of a fuzzer, such as rapid data generation, instrumentation, failure detection, and the ability to reset targets after a failure, along with the capability to log test data effectively. It offers a more streamlined installation process and accommodates diverse communication mediums. Furthermore, it includes built-in capabilities for serial fuzzing, as well as support for Ethernet, IP-layer, and UDP broadcasting. The improvements in data recording are notable, providing consistency, clarity, and thoroughness in the results. Users benefit from the ability to export test results in CSV format and enjoy extensible instrumentation and failure detection options. Boofuzz operates as a Python library that facilitates the creation of fuzzer scripts, and setting it up within a virtual environment is highly advisable for optimal performance and organization. This attention to detail and user experience makes Boofuzz a powerful tool for security testing.

Echidna is a Haskell-based tool created for fuzzing and property-based testing of Ethereum smart contracts. It employs advanced grammar-driven fuzzing strategies that leverage a contract's ABI to challenge user-defined predicates or Solidity assertions. Designed with a focus on modularity, Echidna allows for easy extensions to incorporate new mutations or to target specific contracts under particular conditions. The tool generates inputs that are specifically adapted to your existing codebase, and it offers optional features for corpus collection, mutation, and coverage guidance to uncover more elusive bugs. It utilizes Slither to extract critical information prior to launching the fuzzing process, ensuring a more effective campaign. With source code integration, Echidna can pinpoint which lines of code are exercised during testing, and it provides an interactive terminal UI along with text-only or JSON output formats. Additionally, it includes automatic test case minimization for efficient triage and integrates seamlessly into the development workflow. The tool also reports maximum gas usage during fuzzing activities and supports complex contract initialization through Etheno and Truffle, enhancing its usability for developers. Ultimately, Echidna stands out as a robust solution for ensuring the reliability and security of Ethereum smart contracts.

Luciq is an advanced mobile observability platform powered by AI, tailored for app developers and enterprises, enabling them to effectively monitor, diagnose, and enhance mobile applications with ease. This comprehensive solution integrates bug reporting, crash analytics, session replay, and performance monitoring within a single SDK that accommodates Android, iOS, web, and hybrid applications. Users can collect extensive device logs, network traces, annotated screenshots, videos, and user feedback, while machine learning automatically correlates events and errors to prioritize issues based on their impact. By offering developers insights into user sessions where problems occurred, they can replicate defects through replay and expedite issue resolution via integrations with tools like JIRA, Slack, Zapier, and Zendesk. Luciq's “Agentic Mobile Observability” methodology not only highlights the most pressing issues but also identifies potential root causes and suggests remediation strategies, empowering teams to boost their efficiency, enhance application stability, and improve the overall user experience. Ultimately, this platform transforms the way teams approach mobile app development and maintenance, ensuring they stay ahead of potential challenges.

Enhance your app's stability and resolve issues more swiftly. Firebase Crashlytics allows you to monitor, prioritize, and address stability problems that compromise your app's quality in real time. By reducing the time spent on crash triage and troubleshooting, you can focus more on developing features that will impress your users. Crashlytics expertly consolidates a multitude of crashes into a concise list of issues, offering insights into the reasons behind each crash and the events that preceded it. This detailed information enables you to quickly identify the underlying causes of crashes. Furthermore, crashes are ranked based on their impact on real users, guiding you in effectively prioritizing bug fixes. You will receive instant notifications about new issues, regressions, and emerging problems that may need urgent attention, regardless of your location. In addition, Firebase Crashlytics integrates smoothly with popular bug tracking and project management tools such as Slack and Jira, allowing for efficient collaboration and workflow management. This comprehensive approach not only streamlines the debugging process but also ensures your app remains robust and user-friendly.

Lotan™ equips your organization with a distinctive ability to identify attacks at an earlier stage and with enhanced assurance. Given the vulnerability of exploits against contemporary countermeasures and the diversity of environments, application crashes frequently occur. Lotan scrutinizes these crashes to identify the underlying attack and facilitate an effective response. It gathers crash data through a straightforward registry modification on Windows or via a lightweight userland application for Linux systems. Furthermore, a RESTful API enables seamless sharing of evidence and insights with your existing Threat Defense and SIEM systems. This API delivers transparency into every aspect of Lotan's operational process, supplying comprehensive details essential for a swift and informed response to threats. By significantly improving the precision, frequency, and speed of threat detection, Lotan hampers adversaries' ability to operate unnoticed within your network, ultimately reinforcing your enterprise's security posture. Additionally, the combination of these features ensures a more resilient defense strategy against evolving cyber threats.

CrashPlan is a cloud-based backup and cyber resilience platform designed to protect business-critical data across endpoints, servers, and SaaS applications (Microsoft 365, and Google Workspace), CrashPlan safeguards critical data against threats such as accidental deletion, ransomware, and system failure. Built with proactive threat detection and automated governance, CrashPlan ensures continuous access and compliance. Whether you back up to our cloud, your Azure instance, a local destination, or a third-party cloud, CrashPlan restores your data and your peace of mind. Features Automatic Data Protection Complete security & compliance Unlimited Versioning Point-in-Time Recovery Benefits Beyond Backup

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.

Every minute, a multitude of autonomously generated tests is executed to identify vulnerabilities and facilitate swift remediation. Mayhem eliminates uncertainty surrounding untested code by autonomously creating test suites that yield practical outcomes. There is no requirement to recompile the code, as Mayhem operates seamlessly with dockerized images. Its self-learning machine learning technology continuously executes thousands of tests each second, searching for crashes and defects, allowing developers to concentrate on enhancing features. Background continuous testing detects new defects and expands code coverage effectively. For each defect identified, Mayhem provides a detailed reproduction and backtrace, prioritizing them according to your risk assessment. Users can view all results, organized and prioritized based on immediate needs for fixes. Mayhem integrates effortlessly into your existing development tools and build pipeline, granting developers access to actionable insights regardless of the programming language or tools utilized by the team. This adaptability ensures that teams can maintain their workflow without disruption while enhancing their code quality.

Capture videos of user sessions to effortlessly replicate JavaScript errors without needing to request information from the end user. Explore a demonstration in our online sandbox. RootCause equips you with an extensive array of tools to effectively monitor and replicate errors in websites and applications. With the integrated recording feature, you’ll have a precise account of user actions. Once an error is recorded, simply navigate to the web-based Replay Studio to replay the session and recreate the error with just one click. This functionality eliminates the need to sift through call stacks or extensive logs to track down bugs. Additionally, by utilizing the Feedback button, you can gather user reports regarding visual, usability, or rendering issues, enhancing your ability to improve the user experience. This comprehensive approach not only streamlines error management but also strengthens communication with users regarding potential issues.

BFuzz is a tool designed for input-based fuzzing that utilizes HTML as its source input, launching a new instance of your browser to execute various test cases created by the domato generator located in the recurve directory. In addition, BFuzz automates the process by repeatedly performing the same operations without altering any of the test cases. When you run BFuzz, it prompts you to choose between fuzzing Chrome or Firefox; however, it specifically opens Firefox from the recurve directory and generates logs in the terminal. This lightweight script facilitates the opening of a browser and the execution of test cases, which are systematically generated by the domato tool and include the main scripting functionality. Furthermore, the script incorporates supplementary helper code that is essential for effective DOM fuzzing, enhancing the overall testing process. Its streamlined design makes it an efficient choice for developers looking to perform thorough web application testing.

API Fuzzer is a tool designed to perform fuzz-testing on attributes by employing prevalent penetration testing methods while identifying potential vulnerabilities. By taking an API request as its input, the API Fuzzer gem effectively outputs a list of possible vulnerabilities inherent in the API, which may include risks such as cross-site scripting, SQL injection, blind SQL injection, XML external entity vulnerabilities, insecure direct object references (IDOR), issues with API rate limiting, open redirect vulnerabilities, information disclosure flaws, information leakage through headers, and cross-site request forgery vulnerabilities. This comprehensive evaluation helps developers enhance the security of their APIs by pinpointing critical areas that require attention and remediation.

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.

Bluefish is a robust text editor designed specifically for programmers and web developers, offering a wide array of tools for creating websites, scripts, and various coding languages. It accommodates numerous programming and markup languages, making it versatile for different coding needs. For a detailed look at its features, users can explore the overview, browse through screenshots, or download the editor directly. As an open-source project, Bluefish is distributed under the GNU GPL license, promoting free use and distribution. This multi-platform application is compatible with a variety of desktop operating systems such as Linux, FreeBSD, MacOS-X, Windows, OpenBSD, and Solaris. The latest version, Bluefish 2.2.12, is a minor maintenance update that introduces some new features, including a crucial fix for a crash that occurred during simple searches. Enhancements have also been made in Python 3 compatibility, and the detection of encoding in Python files has seen improvements. Additionally, the triple-click function now allows users to select entire lines easily, and on Mac OSX, Bluefish has been optimized to better handle new permission features. Furthermore, issues with displaying the correct language in the Bluefish user interface for certain languages on OSX have been resolved. With these updates, users can expect a smoother and more efficient coding experience.

Defensics Fuzz Testing is a robust and flexible automated black box fuzzer that helps organizations efficiently identify and address vulnerabilities in their software. This generational fuzzer employs a smart, focused methodology for negative testing, allowing users to create custom test cases through advanced file and protocol templates. Additionally, the software development kit (SDK) empowers proficient users to leverage the Defensics framework to craft their own unique test scenarios. Being a black box fuzzer means that Defensics operates without the need for source code, which adds to its accessibility. By utilizing Defensics, organizations can enhance the security of their cyber supply chain, ensuring that their software and devices are interoperable, resilient, high-quality, and secure prior to deployment in IT or laboratory settings. This versatile tool seamlessly integrates into various development workflows, including both traditional Software Development Life Cycle (SDL) and Continuous Integration (CI) environments. Furthermore, its API and data export functions facilitate smooth integration with other technologies, establishing it as a truly plug-and-play solution for fuzz testing. As a result, Defensics not only enhances security but also streamlines the overall software development process.

Revolutionize the approach to crash reporting by automating every aspect, from the moment of the incident to the distribution of online reports, ultimately leading to cost reductions and a more user-friendly experience for citizens. eCrash offers a solution that simplifies and streamlines the management of crash reports. By automating the entire reporting process, it provides a secure electronic data management system that enables agencies to deliver crash report information more quickly, thereby improving traffic safety and enhancing the overall well-being of the communities they serve. This comprehensive online tool makes data entry, storage, access, and distribution straightforward and efficient. By removing the need for manual processing, it saves valuable time and reduces the volume of walk-in and mail report requests, allowing staff to concentrate on higher-priority responsibilities. Additionally, agencies can maintain and increase report revenue through effortless online fee collection, ensuring they continue to receive their standard fees. By improving the management of crash reports, law enforcement investigations can be bolstered, and public service can be significantly enhanced for a better experience for all citizens involved. This innovative approach not only modernizes the system but also fosters trust and transparency between agencies and the communities they serve.

Mayhem is an innovative fuzz testing platform that integrates guided fuzzing with symbolic execution, leveraging a patented technology developed at CMU. This sophisticated solution significantly minimizes the need for manual testing by autonomously detecting and validating defects in software. By facilitating the delivery of safe, secure, and reliable software, it reduces the time, cost, and effort typically required. One of Mayhem's standout features is its capability to gather intelligence about its targets over time; as its understanding evolves, it enhances its analysis and maximizes overall code coverage. Every vulnerability identified is an exploitable and confirmed risk, enabling teams to prioritize their efforts effectively. Furthermore, Mayhem aids in remediation by providing comprehensive system-level insights, including backtraces, memory logs, and register states, which expedite the diagnosis and resolution of issues. Its ability to generate custom test cases in real-time, based on target feedback, eliminates the need for any manual test case creation. Additionally, Mayhem ensures that all generated test cases are readily accessible, making regression testing not only effortless but also a continuous and integral part of the development process. This seamless integration of automated testing and intelligent feedback sets Mayhem apart in the realm of software quality assurance.

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.

Firejail is a SUID application designed to enhance security by isolating untrusted programs through the use of Linux namespaces and seccomp-bpf. This tool enables a process and its child processes to maintain a distinct view of shared kernel resources, including the network stack, process table, and mount table. Developed in C with minimal dependencies, Firejail is compatible with any Linux system that operates on version 3.x of the kernel or later. Its sandboxing solution is efficient, resulting in minimal overhead. Users benefit from its simplicity, as there are no intricate configuration files to manage, no open socket connections, and no background daemons to contend with. All security mechanisms are built directly into the Linux kernel, making them accessible on any Linux machine. This combination of features makes Firejail an appealing choice for those looking to bolster their system's defenses against potential threats.

The Windows operating system's Wifi hosted network feature enables users to create a wifi hotspot, granting any wifi-enabled device access to the network and internet connection of the computer. However, users have encountered issues with HostedNetworkStarter crashing when attempting to initiate the hosted network, specifically encountering a failure within hnetcfg.dll as it tries to disable existing Internet Connection Sharing (ICS) settings to configure ICS for the new hotspot. It remains uncertain whether the issue stems from a misconfiguration by HostedNetworkStarter or if it is a bug within the ICS component of Windows 10. Notably, several users have reported that this problem emerged following the installation of the Windows 10 Anniversary Update, suggesting a potential link between the update and the crashing issue. This situation leaves users seeking reliable solutions for establishing a functional wifi hotspot without disruption.

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.

To begin capturing crashes that may occur in your Android or iOS applications, you must first integrate the EasyQA Software Development Kit into your app's code. You can access the SDK download and detailed instructions for connecting it to your project by visiting the Integrations page in the EasyQA Test Management Tool. Once the SDK is integrated, remember to use the provided token and initialize it within the application class of your project. After completing this step, you can create your app's build and upload it to the Test Objects section in EasyQA, allowing your application to start reporting crashes to the service. With the EasyQA SDK in place and your app uploaded to Test Objects, you will be able to monitor your app's crash reports through our website. Simply install the app on any Android or iOS device to initiate testing. If a crash occurs, restart the app and click the Upload button to send the crash report. It's important to regularly check the crash reports to ensure your application runs smoothly.

APIFuzzer analyzes your API specifications and systematically tests the fields to ensure your application can handle modified parameters, all without the need for programming. It allows you to import API definitions from either local files or remote URLs, supporting both JSON and YAML formats. Every HTTP method is accommodated, and it can fuzz the request body, query strings, path parameters, and request headers. Utilizing random mutations, it also integrates seamlessly with continuous integration systems. The tool can produce test reports in JUnit XML format and has the capability to send requests to alternative URLs. It supports HTTP basic authentication through configuration settings and stores reports of any failed tests in JSON format within a designated folder, thus ensuring that all results are easily accessible for review. Additionally, this enhances your ability to identify vulnerabilities and improve the reliability of your API.