Alternatives to Tayt

Etheno, the Ethereum-testing Swiss Army Knife. It's a JSON RPC wrapper, analysis tool multiplexer and test integration tool. It removes the complexity in setting up analysis tools such as Echidna for large, multi-contract project. Etheno is a great tool for smart contract developers to test their contracts. Etheno is a great tool for Ethereum client developers to test their implementations. Etheno is a JSON RPC Server that can multiplex requests to one or several clients. API for filtering, modifying and filtering JSON RPC calls. Sending JSON RPC to multiple Ethereum clients allows differential testing. Deploy and interact with multiple networks simultaneously. Integration with test frameworks such as Ganache and Truffle. Run a local network test with just one command. Use our Docker container pre-built to quickly install Etheno. Etheno is a flexible tool that can be used many different ways. There are therefore a number of command-line arguments.

The hevm is a special implementation of the Ethereum Virtual Machine, which was created for the purpose of symbolic execution, unit-testing, and debugging smart contracts. It was developed by DappHub, and integrates particularly well with the DappHub toolsuite. The hevm program can run smart contracts symbolically, run unit testing, interactively debug Solidity contracts while showing their source code, or run any EVM code. Calculations can be performed by using a local test harness state or retrieved on demand from live networks via RPC calls. Run a symbolic implementation against the parameters to search for assertion violations. You can also add specific arguments to the function signature, while leaving others abstract. Hevm uses a eager approach for symbol execution, which means that it will try to explore all branches of a program first.

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.

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.

Jazzer is an in-process, coverage-guided fuzzer developed by Code Intelligence for the JVM platform. It is based on libFuzzer and brings many of its instrumentation-powered mutation features to the JVM. Docker can be used to test Jazzer's autofuzz, which generates arguments for a Java function and reports unexpected errors and detected security issues. You can also run a standalone Jazzer binaries that starts its JVM configured for fuzzling using GitHub release archives.

Solidity Fuzzing boilerplate is a repository of templates designed to make it easier to fuzze components in Solidity projects. This includes libraries. Write your tests once and use them for both Echidna's and Foundry’s fuzzing. Etheno can be used to deploy components that are incompatible Solidity versions into a Ganache instance. Use HEVM’s FFI cheat codes to generate complex fuzzing outputs or to compare the outputs with non EVM executables when doing differential fuzzing. You can publish your fuzzing experiment without worrying about licensing if you extend the shell script to include specific files. If you do not intend to use shell commands in your Solidity contracts, turn off FFI. FFI is a slow solution and should only ever be used as a temporary workaround. It can be used to test against things that are hard to implement in Solidity but already exist in other programming languages. Be sure to check the commands being executed before executing tests on a project with FFI enabled.

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.

LibFuzzer, a coverage-guided evolutionary fuzzing tool, is a fuzzing engine that works in the background. LibFuzzer links with the library being tested and feeds fuzzed data to the library through a specific fuzzing target function. The fuzzer tracks the code coverage and generates mutations based on the input data to maximize it. SanitizerCoverage, an instrumentation of LLVM, provides code coverage information. LibFuzzer will still be fully supported, in that important bugs are fixed. To use libFuzzer with a library, you must first implement a fuzz-target. This is a function which accepts an array and performs something interesting using the API being tested. This fuzz target is not dependent on libFuzzer, so it can be used with other fuzzing engine like AFL or Radamsa.

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.

Honggfuzz, a software fuzzer focusing on security, is available. Supports evolutionary feedback-driven fuzzing (SW and Hardware-based) based on code cover. Honggfuzz is multi-processed and multi-threaded. You don't need to run multiple instances of your fuzzer as it can unlock all of your CPU cores. The file corpus will be automatically shared and improved among all fuzzed process. When persistent fuzzing is used, it's lightning fast. A simple/empty LLVMFuzzerTestOneInput function can be tested with up to 1mo iteration per second on a relatively modern CPU. Honggfuzz has a track record of discovering security bugs. The only vulnerability (to date) in OpenSSL that received the critical score was discovered by Honggfuzz. It will report hijacked/ignored crashes signals (intercepted by a fuzzed application and potentially hidden).

API Fuzzer is a tool that allows you to test API attributes by using pentesting techniques. It also lists vulnerabilities. API Fuzzer gem takes an API request and returns all vulnerabilities that are possible. IDOR, API rate limiting vulnerabilities, open redirect flaws, information disclosure flaws and info leakage via headers.

Awesome Fuzzing contains a list of fuzzing materials, including books, free and paid courses, videos, tutorials, vulnerable applications, and tools to help you learn fuzzing, as well as the initial phases of exploit creation, such root cause analysis. Videos on fuzzing courses/training, videos discussing fuzzing tools, techniques, and best practices. Blogs, conference talks, tutorials, tools for fuzzing, and fuzzers to help fuzze applications that use network protocols like HTTP, SSH and SMTP. Search for exploits that have apps available to download and then reproduce the exploit using the fuzzer you choose. Set of tests to test fuzzing engines. Includes different well-known bugs. Includes a corpus of files in various formats for fuzzing multiple target targets.

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.

Wapiti is an application vulnerability scanner. Wapiti is a web application vulnerability scanner that allows you to audit your website or web application's security. It crawls the pages of the deployed web application, looking for scripts or forms where it could inject data. Wapiti, once it has a list of URLs and forms, as well as their inputs (inputs), acts like a "fuzzer" by injecting payloads into a script to see if it is vulnerable. Search for potentially harmful files on the server. Wapiti can be used to launch attacks using both GET HTTP and POST methods. It can also inject payloads into filenames. When an anomaly is detected (for example, 500 errors and timeouts), warnings are raised. Wapiti can distinguish between reflected and permanent XSS vulnerabilities. Creates vulnerability reports in different formats (HTML XML JSON TXT CSV).

Go-fuzz provides coverage-guided fuzzing for testing Go packages. Fuzzing is most useful for packages that parse binary and text inputs. It is also useful to harden systems that parse inputs that are potentially malicious (anything that is accepted over a LAN). Go Modules are now supported by go-fuzz. Please file an issue if you encounter a module problem. Data is a randomly generated input by go-fuzz. Note that it is usually invalid. The function must return 0 if no input should be added to the corpus, but the fuzzer must increase the priority. The fuzz function has to be in a package go-fuzz is able to import. This means that the code you wish to test cannot be in package main. However, fuzzing internal packages can be done.

Ffuf, a web fuzzer in Go, is fast and easy to use. You can also practice Ffuf scanning against a live host using different lessons and use-cases either locally, by using the Docker Container or against the live hosted version. Virtual host discovery is provided (without DNS records). A wordlist is required to inform Ffuf of the different inputs that should be tested. You can specify one or more wordlists in the command line. If you wish to (or if you are using multiple wordlists), you can select a custom keyword. You can provide Ffuf multiple wordlists. Just remember to configure a keyword for each one. The first word from the first list is tested against the words of the second list before moving on to test the second. All combinations are tested. There are many different ways to customize your request.

Echidna is a Haskell program designed for fuzzing/property-based testing of Ethereum smart contracts. It uses sophisticated grammar based fuzzing campaigns, based on an ABI contract, to falsify user defined predicates or Solidity statements. Echidna was designed with modularity in the mind. It can be easily expanded to include new mutations, or test specific contracts for specific cases. It generates inputs that are tailored to your code. Use optional corpus collection, mutation and guidance to find deeper bugs. Powered by Slither, to extract useful information prior to the fuzzing campaigns. Source code integration for identifying which lines have been covered after the fuzzing campaign. Interactive terminal UI with text-only output or JSON. Automatic test case minimization to speed up triage. Integration into the development workflow is seamless. Reporting of maximum gas usage during the fuzzing campaign. Support for the complex contract initialization process with Etheno, Truffle.

Our impeccable audit services will provide you with unparalleled security for your blockchains in the decentralized world. Choose from our services and put an end to your worries about losing money to hackers. Experts in the industry will analyze the code to find the vulnerabilities within your smart contract. Our experts protect your blockchain applications through security design, audit, and compliance. Our independent team is comprised of highly-skilled penetration testers who perform a comprehensive exercise to detect vulnerabilities and exploits. We are the torchbearers for making the space safer and we do this by helping with a comprehensive, systematic analysis of the product's security. The recovery of funds is just as important as a security review. Our transaction risk monitoring system allows you to track funds and boost user confidence.

The best tools are needed for hands-on security testers. You can use tools that you trust and enjoy all day. The tools that professionals trust. Burp Suite Professional is a web security tester's favorite toolkit. It can automate repetitive tasks and then dig deeper using its expertly designed manual and semi-automated testing tools. Burp Suite Professional will help you test for OWASP Top 10 vulnerabilities as well as the latest hacking techniques. Smart automation works in conjunction with expertly designed manual tools to save you time. Optimize your workflow and do more of what is best for you. Burp Scanner is able to navigate and scan JavaScript heavy single-page applications, scan APIs and prerecord complex authentication sequences. A toolkit used by professional testers. Use features such as the ability to record all you did during an engagement and the powerful search function to increase efficiency and reliability.

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.

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.

BlackArch is an ArchLinux-based Linux pentesting distro. BlackArch Fuzzer is a package that uses the fuzz testing concept.

Fuzzapi uses API Fuzzer for REST API pentesting. It also provides UI solutions to gem.

Defensics, a versatile, automated blackbox fuzzer, allows organizations to quickly and effectively identify and fix security flaws in software. Identify flaws and zero-day vulnerabilities in protocols and services. The generational fuzzer uses an intelligent, targeted approach for negative testing. Advanced protocol template and file fuzzers allow users to create their own test cases. The SDK allows experts to use the Defensics framework for their own test cases. Defensics can be run without the need for source code because it is a black-box fuzzer. Defensics allows users to secure their cyber supply chain and ensure interoperability, robustness and security of software and devices, before introducing them into IT and lab environments. Fuzzing techniques that are properly executed can be a cost-effective and efficient way to find vulnerabilities. They can cover more code paths and iterations than manual analysis.

Defensics, a comprehensive and versatile automated black box fuzzer, allows organizations to discover and remediate software security flaws efficiently and effectively. The generational fuzzer takes a targeted, intelligent approach to negative testing. Users can create their own test cases using advanced file and protocol template fuzzers. The SDK allows users to create their own test cases using the Defensics Framework. Defensics runs as a black-box fuzzer. It does not require source code. Defensics allows users to secure their cyber supply chains, ensuring interoperability, robustness and quality of software and devices, before introducing them in IT or lab environments. Defensics is compatible with any development workflow in a traditional SDL environment or a CI environment. Its API and export capabilities allow it to integrate with surrounding technology, making it a plug-and-play fuzzer.

ClusterFuzz provides a scalable fuzzing system that can be used to find security and stability issues within software. Google uses ClusterFuzz as the fuzzing engine for OSS Fuzz and to fuzz all Google Products. ClusterFuzz offers many features that allow fuzzing to be seamlessly integrated into the software development process. Fully automatic bug filing and triage for different issue trackers. Supports multiple coverages-guided fuzzing engines to achieve optimal results (with ensemble fuzzing and fuzzing strategy). Statistics to analyze fuzzer performance and crash rates. Web interface for managing and viewing crashes. Support for multiple authentication providers using Firebase. Support for black-box fuzzing, test case minimization and regression finding using bisection.

ClusterFuzz provides a scalable fuzzing system that can be used to find security and stability issues within software. Google uses ClusterFuzz as the fuzzing engine for OSS Fuzz and to fuzz all Google Products. ClusterFuzz offers many features that allow fuzzing to be seamlessly integrated into the software development process. Fully automatic bug filing and triage for different issue trackers. Supports multiple coverages-guided fuzzing engines to achieve optimal results (with ensemble fuzzing and fuzzing strategy). Statistics to analyze fuzzer performance and crash rates. Web interface for managing and viewing crashes. Support for multiple authentication providers using Firebase. Support for black-box fuzzing, test case minimization and regression finding using bisection.

Fuzz testing or fuzzing is a software testing technique, that basically consists in finding implementation bugs using malformed/semi-malformed data injection in an automated fashion. Consider an integer that stores the result of the user's selection between three questions. When the user selects one, he will have the option of choosing 0, 1, or 2. This gives us three possible cases. Integers will be stored in a static variable. If the default switch case is not implemented properly, the program can crash and cause "classical" security problems. Fuzzing is a form of automatic bug detection. Its role is to identify software implementation faults, and if possible, find them. A fuzzer program is a program which injects semi-random information into a stack or program and detects bugs. Generators are used to generate data, while debugging tools are used to identify vulnerabilities. Generators use a combination of static fuzzing vectors.

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.

Syzkaller is a kernel fuzzer that uses coverage to guide the fuzzing process. Supports FreeBSD Fuchsia gVisor Linux, NetBSD OpenBSD and Windows. Initially, syzkaller focused on Linux kernel fuzzing, but it is now being extended to other OS kernels. When syzkaller detects a crash in a VM, it will start the process to reproduce the crash. It will, by default, use 4 VMs in order to reproduce the crash. Then it will minimize the program which caused the crash. This could stop the fuzzing as all the VMs may be busy reproducing crashes. The time it takes to reproduce a crash can vary from a few seconds up to an entire hour, depending on how easily reproducible the crash is or if it cannot be reproduced at all.

Radamsa generates test cases for robustness testing, or fuzzer. It is used to test a program's ability to withstand malformed or malicious inputs. It works by reading valid data files and generating different outputs. Radamsa's main selling points are that it has found a lot of bugs in important programs, is scriptable and easy to set up. Fuzzing is a technique to find unexpected behavior within programs. The idea is to simply subject the program to different inputs and observe what happens. This process has two parts: how to get the inputs, and what to do with them. Radamsa can be used to solve the first part. The second part is usually a shell script. The testers usually have an idea of what they don't want to happen and try to verify it.

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.

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.

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.

Starknet is an unrestricted decentralized ZK Rollup that operates as an L2 network on Ethereum. Any dApp may achieve unlimited computation scale without compromising Ethereum’s composability or security. Starknet maintains the security of L1 Ethereum while achieving scale. It does this by producing STARK Proofs off-chain and verifying these proofs on-chain. Starknet Contracts allow developers to deploy any business logic. Starknet will offer composability at Ethereum's level, allowing for easy development and compounding of innovation. The STARK Prover is the engine that powers StarkEx's scalability engine. It has already demonstrated its ability to process 600K transactions on Mainnet in a single proof.

FuzzDB is a dynamic application security testing tool that helps to find application security vulnerabilities. It is the most comprehensive open dictionary for fault injection patterns, predictable resources locations, and regex to match server responses. FuzzDB provides comprehensive lists of attack primitives to test fault injection. These patterns are categorized by attack and platform type where applicable. They are known to cause issues such as OS command injections, directory listings, traversals, source disclosure, file upload bypasses, authentication bypasses, XSSs, HTTP header crlfs, SQL injections, NoSQLs injections, and more. FuzzDB, for example, catalogs 56 patterns which can be interpreted as null bytes and contains lists of frequently used methods and name/value pairs that trigger the debug mode.

CI Fuzz provides robust and secure code, with 100% test coverage. Use CI Fuzz on the command line, or in your favorite IDE to generate thousands of automated test cases. CI Fuzz analyzes the code as it runs. It's like a unit-test, but with AI to cover all paths in the code. Say goodbye to false positives and theoretical issues. Find real issues and get all the information you need to reproduce them quickly and fix them. Test your code with maximum coverage and automatically detect typical security bugs such as injections and remote executions. Deliver the highest-quality software by being fully covered. CI Fuzz allows you to perform real-time code analyses. Unit tests at the next level. It uses AI to generate thousands of test cases and comprehensive code coverage. Maximize pipeline speed without compromising software integrity.

Fuzzing can be a powerful way to find software bugs. The idea is simple: generate a large amount of randomly malformed data for the software to parse, and then see what happens. If the program crashes, then something is wrong. It is surprising how easy it is to find bugs in widely used software, even though fuzzing is an established strategy. Memory access errors will be the most common errors found when fuzzing C/C++ software. The core problem, while they may differ in details, is usually the same: the software reads or write to the wrong memory location. Modern Linux or BSD systems ship with a number of basic tools which display and parse files. Most of these tools, in their current state are not suitable for untrusted data. On the other hand we have powerful tools today that allow us find and analyze these bug.

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.

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.

Advanced fuzzing solution that combines guided and symbolic execution. This technology is patented by CMU. Mayhem, an advanced fuzz testing solution, dramatically reduces manual testing with autonomous defect detection. You can deliver reliable, secure software in a shorter timeframe, at a lower cost, and with less effort. Mayhem's unique advantage lies in its ability to continuously acquire intelligence about its targets. Mayhem's knowledge increases and it expands its analysis. This allows it to maximize its code coverage. All vulnerabilities reported are exploitable and confirmed risks. Mayhem provides detailed system-level information such as backtraces, memory logs and register state to assist in remediation efforts. This helps speed up issue diagnosis and fixes. Mayhem uses target feedback to automatically generate test cases -- no need for manual testing. Mayhem provides access to all its test cases, making regression testing easy and continuous.

StarkNet's L2 money-market protocol, zkLend, is built on StarkNet. It combines zk-rollup scaleability, superior transaction speeds, cost savings, and Ethereum's security. The protocol offers two solutions: a permissioned and compliance-focused service for institutional clients and a permissionless service to DeFi users. This is all without sacrificing decentralization. zkLend believes that true decentralized finance must be built upon chains that are not only fast, cheap, and scalable, but also secure, decentralized, and secure. zkLend was built on the belief that Ethereum is the best decentralized solution. zk is also the only technology that can scale without compromising the unique properties that make Ethereum so special. StarkNet's L2 money-market protocol, zkLend, is built on StarkNet. It combines zk-rollup scaleability, superior transaction speeds, cost savings, and Ethereum's security. Users will be able deposit their assets into a reserve fund, which will provide liquidity to all money markets.

Afl-unicorn allows you to fuzz any binary code that can be emulated using Unicorn Engine. Afl-unicorn can fuzz any binary that can be emulated by Unicorn Engine. Unicorn Mode implements the block-edge instrumentation normally done by AFL's QEMU Mode into Unicorn Engine. AFL will basically use block coverage data from any emulated code to drive its input. The idea revolves around a Unicorn test harness that is constructed correctly. The Unicorn-based testing harness loads the target binary code, sets the initial state and loads data mutated by AFL. The test harness emulates the binary code of the target and, if a crash or an error occurs, it will send a signal. AFL will perform all its usual tasks, but is actually fuzzing the emulated binary code. It was only tested on Ubuntu 16.04 LTS but should work with any OS that can run both AFL and Unicorn.

SithSwap, the next-generation AMM on StarkNet, features instant volatile and stabile swaps, ultra-low slippage, almost-zero fees and the full safety of Ethereum. SithSwap is a flexible and efficient liquidity exchange protocol that allows users and builders to leverage a highly composable architecture, offering deep, sustainable and adaptable liquidity. SithSwap offers a rich incentive system that includes escrows and staking pools. It also supports gauges, bribes and native $SITH emission tokens. These can be escrowed in order to unlock exclusive benefits, such as the ability to govern protocol parameters including future emissions rewards. SithSwap provides StarkNet protocols with the liquidity infrastructure they need to launch, bootstrap, and grow in a sustainable & decentralized way. The SithSwap AMM conforms to the timeless Uniswap Smart Contracts architecture, popularized through its intuitive Pair features and Router APIs.

10K Swap, an open-source AMM protocol built on Starknet Mainnet, aims to improve performance of the AMM Protocol. 10K Swap uses a rollup feature to reduce friction and fees, and improve liquidity within the L2 environment. This could ultimately help to advance DeFi adoption. StarkNet Mainnet's 10K Swap AMM is the first open source AMM to be deployed.

Orbiter Finance, a decentralized layer 2 bridge, enables asset transfers between different blockchain networks in a fast and secure manner. It was designed with scalability, interoperability, and speed in mind. It connects Layer 2 solutions such as Optimism Arbitrum zkSync and StarkNet. Users can seamlessly move assets across these networks at low fees and with minimal transaction latency. Orbiter Finance uses zero-knowledge cryptographic techniques and other advanced cryptographic methods to ensure a high degree of security while maintaining an intuitive interface. It is positioned for the growing demand of efficient cross-chain transaction, making it a major player in the evolving Ethereum Layer 2 ecosystem and beyond.

Retreaver, a cloud-based call centre solution, tracks, tags and routes calls to specific departments or agents. We help marketers, agencies, and brands analyze inbound calls, manage caller profiles and dynamically route calls. We also track conversions, analyze ROI, and generate reports. You can personalize customer experiences, reduce call times, and improve customer satisfaction by leveraging powerful CRM databases and real-time tracking. Retreaver allows you to create your own call data properties using custom tags. These tags are associated with a number or a phone call, giving you greater control over your campaigns. We can pass these savings to you by optimizing our operating expenses. We do not have any monthly contracts and offer competitive pricing for our clients. Contact us for a quote.

It works with.NET Framework for Windows and.NET Core for all supported platforms. Coverlet supports coverage for deterministic builds. Unfortunately, the solution is not ideal and requires a workaround. Depending on your platform, the following addins can be used to visualize coverlet output in Visual Studio. Coverlet integrates with the build system for code coverage after tests. It is as easy as setting the CollectCoverage property true to enable code coverage. The path to the assembly containing the unit tests is what invokes the coverlet tool. Also, specify the test runner and arguments to pass to it using the --targetargs and --targetargs options. If you provide the arguments, the invocation of test runner must not result in a recompilation or deletion of the unit test assembly.

The public blockchain, which derives its value through activities created by members of the ecosystem to solve real world economic problems. BlockRef and Expiration transaction fields allow users to set the time at which transactions are processed or expired, if they are not included in a block. Flexible transaction fee delegation schemes (Multiparty Payment and Designated gas Payer) allow for a freemium model within an application that allows users to be onboarded without any friction. Multi-function atomic transactions enable developers to batch pay, add multiple calls from different contract functions to one transaction, and determine their order. To ensure that the execution order meets business needs, you can set dependencies. Transactions that specify a dependency won't be executed until the transaction is processed.