Abstract—This study presents an advanced framework for analyzing security vulnerabilities in binary code through en- hanced modeling of data dependencies. Traditional methods often struggle to clearly identify the origin of critical issues such as memory corruption and buffer overflows. To address this limitation, the proposed approach introduces a structured and scalable mechanism that improves visibility into complex program behaviors. By leveraging data flow insights, the frame- work simplifies vulnerability tracing and strengthens analytical accuracy. The solution is designed to support cybersecurity professionals and ethical hackers by enabling more efficient detection and interpretation of software flaws. Ultimately, this work contributes toward building more resilient and secure software systems in modern computing environments.
