Bsides Exeter 2026

In today’s interconnected world, achieving complete software security is extremely challenging, as vulnerabilities continue to provide opportunities for exploitation. Buffer overflow attacks remain among the most enduring and impactful forms of software exploitation, enabling adversaries to manipulate program execution and gain unauthorized access. Although significant countermeasures such as stack canaries, Address Space Layout Randomisation (ASLR), and non-executable memory protections have been developed, buffer overflows persist due to legacy codebases, low-level programming practices, and the constant evolution of attack strategies. Recent advances in Generative Artificial Intelligence (Gen-AI) introduce a new dimension to defensive cybersecurity research and offers potential as a countermeasure through enhanced vulnerability detection, automated secure code generation, and the ability to adapt defensively to dynamic attack patterns. This project specifically evaluate the dangers of buffer overflows attacks and effectiveness of Gen-AI models in guarding against them, with a focus on recent vulnerabilities such as CVE-2025-6660 and CVE-2025-6191.