Speaker

Insu Yun (윤인수)

Title

The Dark and Bright Side of AI in Cybersecurity

Abstract

LLM agents extend beyond simple natural language interaction; they are intelligent systems capable of autonomously executing tasks through tool usage, code execution, and interaction with external systems. Recent advancements have enabled these agents to evolve rapidly, positioning them as viable alternatives or complements to traditional software analysis techniques, including static analysis, vulnerability detection, and code security auditing. However, this evolution presents a dual-use challenge. On the one hand, LLM agents offer significant potential for automating repetitive tasks traditionally performed by security professionals, thereby improving the efficiency and speed of vulnerability identification and mitigation. On the other hand, from an adversarial perspective, these agents may also be leveraged as powerful tools, and inadequately designed systems can introduce new attack surfaces. In this presentation, we examine both the opportunities and risks associated with LLM-based security automation. Furthermore, we aim to discuss how AI-driven approaches are expected to reshape the cybersecurity landscape.

Bio

Insu Yun is an associate professor at KAIST, currently leading Hacking Lab. He is interested in system security in general, especially, binary analysis, automatic vulnerability detection, and automatic exploit generation. His work has been published to the major computer conferences such as IEEE Security & Privacy, USENIX Security, and USENIX OSDI. Particularly, his research won the best paper award from USENIX Security and OSDI in 2018, and he also won DARPA AIxCC with Team Atlanta. In addition to research, he has been participating in several hacking competitions as a hacking expert. In particular, he won Pwn2Own 2020 by compromising Apple Safari and won DEFCON CTF in 2015 and 2018, which is the world hacking competition. Prior to joining KAIST, he received his Ph.D. degree in Computer Science from Georgia Tech in 2020.

Language

English (Offline)