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S3 Lab - Software & Systems Security Laboratory
South Engineering and Computer Science Building

about us

We conduct research and build practical tools to analyze the security of diverse software and develop dependable systems using program analysis, operating systems and cyber-physical systems techniques.

If you are interested in working with us, please look at this page.

S3 Lab is part of the Cyber Security Research and Education Institute and the Department of Computer Science at the University of Texas at Dallas.

recent news (see more)

A new grant awarded from Sandia National Laboratories. Thanks SNL for support!
T-Slices paper is accepted to CODASPY 2023.
Chung Hwan Kim is a program committee member of VehicleSec 2023.
Chung Hwan Kim is a program committee member of ICDCS 2023.

highlighted projects (see more)

AutoInsight AutoInsight

The AutoInsight project applies advanced techniques in software security and control systems to build a new security analysis platform for autonomous driving systems.

RetroV RetroV

Robotic vehicles (as known as drones) are facing various threats of cyber-physical attacks that exploit their security vulnerabilities. RetroV develops automated analysis tools to find such vulnerabilities in existing robotic vehicle systems retrospectively and retrofits their design against advanced cyber-physical attacks.

Trusted Things Trusted Things

The Trusted Things project develops new software systems to enable secure IoT leveraging trusted execution environment techniques.

AI Vault AI Vault

The AI Vault project designs and develops a new trusted execution environment tailored to run artificial intelligence and machine learning programs on modern AI platforms (e.g., cloud and embedded devices) while providing strong data confidentiality and high efficiency.


The CLUE project develops an infrastructure to detect and diagnose system anomalies in enterprise and cloud systems. These anomalies include stealthy malware and other types of hidden system anomalies. CLUE provides a diverse set of tools to find and understand such anomalies with minimal disruption to the target system.

research areas

courses and seminar (see more)

recent publications (see more)

DriveFuzz: Discovering Autonomous Driving Bugs through Driving Quality-Guided Fuzzing
Seulbae Kim, Major Liu, Junghwan , Yuseok Jeon, Yonghwi Kwon, and Chung Hwan Kim
In CCS 2022 [ pdf :: slides :: bibtex ]
PASAN: Detecting Peripheral Access Concurrency Bugs within Bare-metal Embedded Applications
Taegyu Kim, Vireshwar Kumar, Junghwan "John" Rhee, Jizhou Chen, Kyungtae Kim, Chung Hwan Kim, Dongyan Xu, and Dave (Jing) Tian
In Security 2021 [ pdf :: slides :: bibtex ]