시스템 보안

As LLM and agentic systems are entrusted with sensitive data and consequential actions, they expose new attack surfaces. Drawing on our experience protecting computation within trusted execution environments, using hardware security features such as ARM TrustZone alongside micro-hypervisor and compiler-based techniques, we design defenses that secure these systems, safeguarding their data, models, and decision-making against emerging threats. Our prior work developed system-level techniques that automatically discover latent vulnerabilities in low-level software, including OS kernels, device drivers, and the hardware/OS boundary. Building on this foundation, we are now designing agentic systems that autonomously reason about and discover vulnerabilities, pushing automated vulnerability discovery beyond the reach of fixed heuristics. Reverse engineering is labor-intensive, and existing automation often relies on brittle heuristics. Building on our work applying machine learning to binary analysis, we are developing agentic systems that emulate the iterative reasoning of skilled reverse engineers to tackle core reverse-engineering challenges. Welcome to Secure Systems Lab (SSLab) in the Department of Computer Science and Engineering in College of Computing at Yonsei University. We are a group of people who are genuinely interested in improving the security of modern computer systems, and we are currently deeply invested in LLM and agentic systems. Our research spans both ends of this challenge: hardening these systems against new threats and harnessing them to tackle long-standing security and privacy problems, drawing on a combination of systems and machine learning techniques.