The RoboInsight (formerly AutoInsight) project leverages advanced techniques in program analysis, control-system modeling, and software hardening to develop a novel framework to enhance the safety and security of modern autonomous robotic systems, including robotic arms, mobile robots, and autonomous vehicles.

Recent News

• Feb. 26, 2025: TZ-DATASHIELD conference talk at NDSS 2025
• Feb. 20, 2025: TZ-DATASHIELD code is publicly available
• Oct. 31, 2024: TZ-DATASHIELD paper is accepted to NDSS 2025
• Nov. 7, 2023: RoboDbg poster is accepted to ACSAC 2023
• Sep. 25, 2023: DriveFuzz code is publicly available
• Nov. 9, 2022: DriveFuzz is featured in a news article
• Aug. 26, 2022: DriveFuzz paper is accepted to CCS 2022
• Sep. 28, 2021: AutoInsight poster is presented at Fed Supernova 2021

Available Work

• TZ-DATASHIELD: a compartmentalization framework that leverages ARM TrustZone to protect sensitive data on microcontrollers (paper, code)
• DriveFuzz: a feedback-driven fuzzing framework for autonomous driving systems (paper, extended paper, website, code)

Acknowledgments

This project is supported in part by the National Science Foundation, Electronics and Telecommunications Research Institute, National Center for Transportation Cybersecurity and Resiliency (a U.S. Department of Transportation National University Transportation Center, headquartered at Clemson University in Clemson, South Carolina), Agency for Defense Development, Texas A&M Engineering Experiment Station on behalf of its SecureAmerica Institute, University of Texas at Dallas Office of Research through the NFRS program, and Texas Advanced Computing Center.