4 citations · 6 across the 13 of their papers we have counts for
9 papers · 1 filter
SCRIPTIOC-BENCH: A Benchmark for Recognizing Actionable Threat Intelligence from Script-Based Malware using LLMs
Hanna Kim, Jian Cui, Minkyoo Song +4
Script-based malware remains a prevalent attack technique. These scripts often contain indicators of compromise (IOCs) that provide actionable threat intelligence. However, statica…
Graphs Don't Stay Secret: Practical Subgraph Reconstruction Attacks on Defended Graph RAG
Minkyoo Song, Jaehan Kim, Myungchul Kang +3
Graph-based retrieval-augmented generation (Graph RAG) is increasingly deployed to support LLM applications by augmenting user queries with structured knowledge retrieved from a kn…
: Politically Controversial Content Generation via Jailbreaking Attacks on GPT-based Text-to-Image Models
Wonwoo Choi, Minjae Seo, Minkyoo Song +3
The rapid evolution of text-to-image (T2I) models has enabled high-fidelity visual synthesis on a global scale. However, these advancements have introduced significant security ris…
BEACON: Automatic Container Policy Generation using Environment-aware Dynamic Analysis
Haney Kang, Eduard Marin, Myoungsung You +3
This paper introduces BeaCon, a novel tool for the automated generation of adjustable container security policies. Unlike prior approaches, BeaCon leverages dynamic analysis to sim…
SecTracer: A Framework for Uncovering the Root Causes of Network Intrusions via Security Provenance
Seunghyeon Lee, Hyunmin Seo, Hwanjo Heo +3
Modern enterprise networks comprise diverse and heterogeneous systems that support a wide range of services, making it challenging for administrators to track and analyze sophistic…
Ambusher: Exploring the Security of Distributed SDN Controllers Through Protocol State Fuzzing
Jinwoo Kim, Minjae Seo, Eduard Marin +3
Distributed SDN (Software-Defined Networking) controllers have rapidly become an integral element of Wide Area Networks (WAN), particularly within SD-WAN, providing scalability and…
Defending MoE LLMs against Harmful Fine-Tuning via Safety Routing Alignment
Jaehan Kim, Minkyoo Song, Seungwon Shin +1
Recent large language models (LLMs) have increasingly adopted the Mixture-of-Experts (MoE) architecture for efficiency. MoE-based LLMs heavily depend on a superficial safety mechan…
AutoPatch: Multi-Agent Framework for Patching Real-World CVE Vulnerabilities
Minjae Seo, Wonwoo Choi, Myoungsung You +1
Large Language Models (LLMs) have emerged as promising tools in software development, enabling automated code generation and analysis. However, their knowledge is limited to a fixe…
MUFFLER: Secure Tor Traffic Obfuscation with Dynamic Connection Shuffling and Splitting
Minjae Seo, Myoungsung You, Jaehan Kim +3
Tor, a widely utilized privacy network, enables anonymous communication but is vulnerable to flow correlation attacks that deanonymize users by correlating traffic patterns from To…