S3 Lab - Software & Systems Security Laboratory
RetroV logo


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.

Available Work

  • Mayday: A post-mortem analysis framework for robotic aerial vehicle accidents (paper)
  • RVFuzzer: A fuzzing tool to find control-semantic bugs in robotic vehicles (paper)
  • Minion: A framework for enforcing memory isolation on real-time microcontrollers (paper)

Ongoing Work

  • SensorFuzz
  • RVProf
  • xHammer


Cindy Chang
Cindy Chang
Masters student
Junghwan (John) Rhee
Junghwan (John) Rhee
External collaborator


From Control Model to Program: Investigating Robotic Aerial Vehicle Accidents with Mayday
Taegyu Kim, Chung Hwan Kim, Altay Ozen, Fan Fei, Zhan Tu, Xiangyu Zhang, Xinyan Deng, Dave (Jing) Tian, and Dongyan Xu
In Security 2020 [ pdf :: slides :: bibtex ]
RVFuzzer: Finding Input Validation Bugs in Robotic Vehicles through Control-Guided Testing
Taegyu Kim, Chung Hwan Kim, Junghwan (John) Rhee, Fan Fei, Zhan Tu, Gregory Walkup, Xiangyu Zhang, Xinyan Deng, and Dongyan Xu
In Security 2019 [ pdf :: slides :: bibtex ]
Securing Real-Time Microcontroller Systems through Customized Memory View Switching
Chung Hwan Kim, Taegyu Kim, Hongjun Choi, Zhongshu Gu, Xiangyu Zhang, and Dongyan Xu
In NDSS 2018 [ pdf :: slides :: bibtex ]