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S3 Lab - Software & Systems Security Laboratory The University of Texas at Dallas
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)

FIDO paper is accepted to S&P 2026.
Chung Hwan Kim is serving on the program committee of ASIACCS 2027.
Minkyung Park is joining UNIST as an Assistant Professor in March 2026. Congratulations!
Chung Hwan Kim is serving on the program committee of EuroSys 2027.

highlighted projects (see more)

RoboInsight RoboInsight

The RoboInsight (formerly AutoInsight) project utilizes advanced techniques in software security, control system modeling, and system replay to develop a new security analysis platform for modern robotic and autonomous systems, including robotic arms, mobile robots, and autonomous vehicles.

AI Vault AI Vault

The AI Vault project designs and develops new trusted execution environments 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.

RetroV RetroV

Robotic vehicles (also 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 and retrofit 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.

CLUE CLUE

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)

IMUFUZZER: Resilience-based Discovery of Signal Injection Attacks on Robotic Aerial Vehicles
Sudharssan Mohan, Kyeongseok Yang, Zelun Kong, Yonghwi Kwon, Junghwan Rhee, Tyler Summers, Hongjun Choi, Heejo Lee, and Chung Hwan Kim
In ASE 2025 [ pdf :: slides :: code :: bibtex ]
PAVE: Information Flow Control for Privacy-preserving Online Data Processing Services
Minkyung Park, Jaeseung Choi, Hyeonmin Lee, and Taekyoung Kwon
In ASPLOS 2025 [ pdf :: bibtex ]
FreePart: Hardening Data Processing Software via Framework-based Partitioning and Isolation
Ali Ahad, Gang Wang, Chung Hwan Kim, Suman Jana, Zhiqiang Lin, and Yonghwi Kwon
In ASPLOS 2024 [ pdf :: bibtex ]
Poster: Deterministic Replay and Debugging for Robotic Systems
Md Nazmus Sakib, Seungmok Kim, Zelun Kong, Seulbae Kim, Kyu Hyung Lee, Heejo Lee, and Chung Hwan Kim
In ACSAC 2023 [ pdf :: bibtex ]