Multi-fuzzer techniques for automated vulnerabilities assessments

Choy, Ein Jun (2025) Multi-fuzzer techniques for automated vulnerabilities assessments. Final Year Project, UTAR.

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Abstract

In the contemporary cybersecurity landscape, effectively safeguarding software from vulnerabilities is critically important. This project introduces an innovative approach to automated vulnerability assessment through a sophisticated multi-fuzzer system designed to enhance the security of at-risk software applications. The primary objective is to provide an efficient and user-friendly solution for identifying and analyzing security vulnerabilities via a dynamic front-end chatbot interface. Users can seamlessly upload their software applications, which are subsequently subjected to a series of diverse fuzzing tools within an automated framework. The system employs a range of fuzzing tools, such as AFL++ and Honggfuzz, ensuring a comprehensive and systematic evaluation of software interfaces and their responses to various potential threats. By automating the fuzzing process, this project facilitates a more efficient and thorough assessment of security weaknesses than traditional manual testing methods. The automated framework generates detailed CVEs on discovered vulnerabilities and potential exploitation scenarios, significantly enhancing the security posture of the evaluated applications. The results of this project demonstrate the system's capability to automatically detect and document vulnerabilities across different software environments, providing a comparative analysis of the effectiveness and limitations of various fuzzing techniques. This analysis offers valuable insights into the roles these techniques play in software security, highlighting the importance of using a multi-fuzzer approach to achieve a more resilient vulnerability assessment. Ultimately, this project underscores the critical role of automation in vulnerability assessment and reinforces the value of employing diverse fuzzing methods as essential tools in advancing cybersecurity practices. The findings contribute to the development of more effective security measures and serve as a foundational resource for improving software security in future applications.

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