We focused on improving the reliability and security of software systems through the development and refinement of fuzz testing methods. This research spans multiple application areas, including embedded systems, web applications, network protocols, and cryptographic code, demonstrating the broad applicability and importance of fuzzing as a vulnerability detection technique. A main theme linking the activities is the recognition of the limitations of current fuzzing techniques and the search for innovative approaches to overcome them. We identified areas where conventional methods fall short, whether due to the complexity of modern systems, the inadequacy of coverage-based metrics, or the challenges associated with generating meaningful inputs for complex, structured programs. In response, we explored a variety of improvements, including the integration of adaptive mechanisms, hybrid analysis techniques, and novel data formats to improve input generation, coverage, and vulnerability detection. We have placed an emphasis on improving the evaluation practices of fuzzing tools: we have recognized that the lack of standardized methods has made it difficult to meaningfully compare fuzzers or reproduce results in the past. By proposing a more rigorous, systematic framework for evaluating the effectiveness of fuzzing, this line of research aims to establish standardized benchmarks and methods that improve the reproducibility and credibility of experimental results. In addition, our research highlights the importance of applying fuzzing techniques to increasingly specialized and complex environments. This includes using fuzzing to detect concurrency issues at the binary level, investigating server-side vulnerabilities in web applications, investigating vulnerabilities in shader translation mechanisms, and network applications. This diversity of applications demonstrates both the adaptability of fuzzing as a testing approach and the need for continuous refinement to meet evolving technological challenges. Ultimately, the overarching theme of this project is the effort to extend fuzzing beyond its traditional boundaries and make it more adaptable, efficient, and reliable in various domains. The work aims to bridge the gap between theoretical advances and practical applicability, providing tools and methods that are not only effective, but also reproducible and widely accessible. Through collaboration with other research groups, we aim to create a more coherent, integrated framework for applying fuzzing techniques to real-world security problems. Three distinguished paper awards have been awarded to the project results so far (IEEE Symposium on Security and Privacy 2023 + 2024 and USENIX Security 2025).
