The Shear project creates a secure environment for the least-authority execution of over-privileged applications that may be exploited by adversaries to launch privileged attacks.

The RetroFirm project aims to enhance the security of existing embedded devices by creating retrospective methods for firmware analysis and vulnerability detection.

The PeriShield project analyzes the security of various types of peripheral devices and it develops cutting-edge tools to detect/prevent malicious peripherals.

The AI Vault project designs and develops new trusted execution environments tailored to run artificial intelligence and machine learning programs confidentially on modern AI platforms end-to-end, including cloud, edge, and embedded devices.

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.

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.

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.