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Description

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.

Description

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.