Leveraging LLMs for Interaction Point Detection and Payload Alignment in CTF challenges

Capture The Flag (CTF) competitions have become a vital platform for cybersecurity skill development, offering participants hands-on opportunities to analyze and exploit real-world vulnerabilities. Among various CTF challenge categories, PWN tasks—centered on binary vulnerability analysis and exploitation—present unique technical hurdles, particularly in the detection of user interaction points and the alignment of exploit payloads. While recent advances in Large Language Models (LLMs) have revolutionized the automated exploitation of web vulnerabilities, their application to binary exploitation remains nascent. This paper investigates the potential of LLMs, to automate two critical stages of binary CTF challenge solving: interaction point detection and payload alignment. We demonstrate how LLMs, leveraging their understanding of code semantics and security concepts, can reduce the manual workload for participants, and lower barriers to entry. Our findings highlight the promise of integrating LLMs into CTF workflows, paving the way for more efficient and accessible binary vulnerability research and exploitation.