activity
20242026
most citedAmbusher: Exploring the Security of Distributed SDN Controllers Through Protocol State Fuzzing

4 citations · 6 across the 13 of their papers we have counts for

collaborators

13 papers

cs.CR2026

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…

cs.CR2026

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…

cs.CR2026

: 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…

cs.CR2025

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…

cs.CR2025

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…

cs.LG2025

PassREfinder-FL: Privacy-Preserving Credential Stuffing Risk Prediction via Graph-Based Federated Learning for Representing Password Reuse between Websites

Jaehan Kim, Minkyoo Song, Minjae Seo +3

Credential stuffing attacks have caused significant harm to online users who frequently reuse passwords across multiple websites. While prior research has attempted to detect users…

cs.CR2025★ 4 cited

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…

cs.CR2025★ 1 cited

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…

cs.CR2025

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…

cs.CR2025

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…

cs.CL2025★ 1 cited

Covering Cracks in Content Moderation: Delexicalized Distant Supervision for Illicit Drug Jargon Detection

Minkyoo Song, Eugene Jang, Jaehan Kim +1

In light of rising drug-related concerns and the increasing role of social media, sales and discussions of illicit drugs have become commonplace online. Social media platforms host…

cs.CL2024

Obliviate: Neutralizing Task-agnostic Backdoors within the Parameter-efficient Fine-tuning Paradigm

Jaehan Kim, Minkyoo Song, Seung Ho Na +1

Parameter-efficient fine-tuning (PEFT) has become a key training strategy for large language models. However, its reliance on fewer trainable parameters poses security risks, such…

cs.CL2024

Claim-Guided Textual Backdoor Attack for Practical Applications

Minkyoo Song, Hanna Kim, Jaehan Kim +2

Recent advances in natural language processing and the increased use of large language models have exposed new security vulnerabilities, such as backdoor attacks. Previous backdoor…