Compare Code Intelligence vs. Mayhem Code Security vs. Sulley

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.

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.

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.