WUSTL

COMPUTER SECURITY & PRIVACY LABORATORY CSPL PUBLICATION OVERVIEW
Cyber-physical Reasoning Foundation

Project Description

The very fabric of modern society is interwoven with computing, from managing critical infrastructures like transportation systems and power grids to predicting climate changes and tracking epidemics. This omnipresence of computing, supporting the very backbone of the most fundamental operations of society and nation, has transformed the threat landscape by interconnecting both cyber and physical domains with an increasing number of vulnerabilities discovered daily.

This project will develop the theoretical foundations as well as the system implementations to automatically and efficiently discover, prioritize (exploit), and mitigate vulnerabilities in cyber-physical systems. The proposed reasoning system scientifically formulates hacking into formal verification problems on a common abstraction that allows for analysis of essential physical properties of cyber-physical systems, such as real-time availability and control integrity. The theory to bridge the semantic gap among different layers for reasoning will lay the foundation for new algorithms and system designs for safety and security protection. Building on the insights from an in-depth understanding of the vulnerabilities, this project also aims to leverage physics principles to detect, prevent, and operate through adversarial attacks. Such physical-world informed defense will bring a paradigm shift into how adversarial attacks are modeled upon and defended against. Lastly, a cyber-physical battlefield testbed will be developed not only to provide the evaluation environment but also to serve as a platform for community and stakeholder engagement.

The proposed research activities tackle the increasingly pressing societal challenge of cyber-physical system security through the lens of automation. To do so, it tackles several long-standing research problems by reasoning via a common abstraction grounded on cyber-physical control loop and driven by physics principles. The interdisciplinary nature of the approach necessities collaborative research connecting communities from computer security, real-time computing, and control theory, as well as artificial intelligence and machine learning. If successful, the proposed project will be the seed for a new scientific community from different backgrounds under the common goal of securing the nation's most dependent critical cyber-physical assets.

The reasoning capability will fundamentally transform the timescale in both offensive maneuvers and defensive actions, introducing a revolutionary shift in battlefield dynamics and giving the US Army a strategic advantage. Beyond military application, the scientific approach to automatically and efficiently protect critical assets also has profound implications for national security and the lives and livelihood of the citizens.
Publication
A Unified Hardware Performance Profiling Infrastructure to Measure and Manage Uncertainty
A. Li, M. Sudvarg, Z. Li, S. Baruah, C. Gill, N. Zhang
USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2025
[PDF]
Resilient Federated Learning on Embedded Devices with Constrained Network Connectivity
Z. Li, H. Liu, A. Li, C. Chan, Y. Vorobeychik, W. Yeoh, W. Lou, N. Zhang
Design Automation Conference (DAC), 2025
[PDF]
An In-Depth Investigation of Data Collection in LLM App Ecosystems
Y. Wu, E. Jaff, K. Yang, N. Zhang, U. Iqbal
Internet Measurement Conference (IMC), 2025
[PDF]
Preference Poisoning Attacks on Reward Model Learning
J. Wu, J. Wang, C. Xiao, C. Wang, N. Zhang, Y. Vorobeychik
IEEE Symposium on Security and Privacy (Oakland), 2025
[PDF]
IsolateGPT: An Execution Isolation Architecture for LLM-Based Agentic Systems
Y. Wu, F. Roesner, T. Kohno, N. Zhang, U. Iqbal
Network and Distributed System Security Symposium (NDSS), 2025
[PDF] [GitHub]
Software Availability Protection in Cyber-Physical Systems
A. Li, J. Wang, N. Zhang
USENIX Security Symposium (Security), 2025
[PDF] [Zenodo]
Secure Information Embedding in Forensic 3D Fingerprinting
C. Wang, J. Wang, M. Zhou, V. Pham, S. Hao, C. Zhou, N. Zhang, N. Raviv
USENIX Security Symposium (Security), 2025
[PDF] [GitHub]
PhySense: Defending Physically Realizable Attacks for Autonomous Systems via Consistency Reasoning
Z Yu, A. Li, R. Wen, Y. Chen, N. Zhang
ACM SIGSAC Conference on Computer and Communications Security (CCS), 2024
[PDF] [Zenodo]
Partial Context-Sensitive Pointer Integrity for Real-time Embedded Systems
Y. Wang, K. Mack, T. Chantem, S. Baruah, N. Zhang and B. Ward
IEEE Real-Time Systems Symposium (RTSS), 2024
[PDF] [GitHub]
An Empirical Study of Performance Interference: Timing Violation Patterns and Impacts
A. Li, J. Wang, S. Baruah, B. Sinopoli, N. Zhang
IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 2024
[PDF] [GitHub]
Opportunistic Data Flow Integrity for Real-time CPS Using Worst Case Execution Time Reservation
Y. Wang, A. Li, J. Wang, S. Baruah, N. Zhang
USENIX Security Symposium (Security), 2024
[PDF] [GitHub]
Please Tell Me More: Privacy Impact of Explainability through the Lens of Membership Inference Attack
H. Liu, Y. Wu, Z. Yu, N. Zhang
IEEE Symposium on Security and Privacy (Oakland), 2024
[PDF]
Don't Listen To Me: Understanding and Exploring Jailbreak Prompts of Large Language Models
Z. Yu, X. Liu, S. Liang, Z. Cameron, C. Xiao, N. Zhang
USENIX Security Symposium (Security), 2024
[PDF] [GitHub] [Site Link]
AntiFake: Using Adversarial Audio to Prevent Unauthorized Speech Synthesis
Z. Yu, S. Zhai, N. Zhang
ACM Conference on Computer and Communications Security (CCS), 2023
[PDF] [GitHub] [Site Link]
AvaGPU: Secure and Timely GPU Execution in Cyber-physical Systems
J. Wang, Y. Wang, N. Zhang
ACM Conference on Computer and Communications Security (CCS), 2023
[PDF] [GitHub]
CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models
Z. Yu, Y. Wu, N. Zhang, C. Wang, Y. Vorobeychik, C. Xiao
International Conference on Machine Learning (ICML), 2023
[PDF] [GitHub]
SlowLiDAR: Increasing the Latency of LiDAR-Based Detection Using Adversarial Examples
H. Liu, Y. Wu, Z. Yu, Y. Vorobeychik, N. Zhang
IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023
[PDF] [GitHub]
RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation With Natural Prompts
H. Liu, Y. Wu, S. Zhai, B. Yuan, N. Zhang
IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023
[PDF] [GitHub]
XCheck: Verifying Integrity of 3D Printed Patient-Specific Devices via Computing Tomography
Z. Yu, Y. Chang, S. Zhai, N. Deily, T. Ju, X. Wang, U. Jammalamadaka, N. Zhang
USENIX Security Symposium (Security), 2023
[PDF] [GitHub]
ARI: Attestation of Real-time Mission Execution Integrity
J. Wang, Y. Wang, A. Li, Y. Xiao, R. Zhang, W. Lou, Y. T. Hou, N. Zhang
USENIX Security Symposium (Security), 2023
[PDF] [GitHub]
SMACK: Semantically Meaningful Adversarial Audio Attack
Z. Yu, Y. Chang, N. Zhang, C. Xiao
USENIX Security Symposium (Security), 2023
[PDF] [GitHub]
IP Protection in TinyML
J. Wang, Y. Wu, H. Liu, B. Yuan, R. Chamberlain, N. Zhang
ACM/IEEE Design Automation Conference (DAC), 2023
[PDF] [GitHub]
PolyRhythm: Adaptive Tuning of a Multi-Channel Attack Template for Timing Interference
A. Li, M. Sudvarg, H. Liu, Z. Yu, C. Gill, N. Zhang
IEEE Real-Time Systems Symposium (RTSS), 2022
[PDF] [GitHub]
Understanding and Mitigating the Impact of Software Execution Timing in SLAM
A. Li, H. Liu, J. Wang, N. Zhang
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022
[PDF] [GitHub]
When Evil Calls: Targeted Adversarial Voice over IP Network
H. Liu, Z. Yu, M. Zha, X. Wang, W. Yeoh, Y. Vorobeychik, N. Zhang
ACM Conference on Computer and Communications Security (CCS), 2022
[PDF] [GitHub]
HeatDeCam: Detecting Hidden Spy Cameras via Thermal Emissions
Z. Yu, Z. Li, Y. Chang, S. Fong, J. Liu, N. Zhang
ACM Conference on Computer and Communications Security (CCS), 2022
[PDF] [GitHub]
Work-in-Progress: Measuring Security Protection in Real-time Embedded Firmware
Y. Wu, Y. Wang, S. Zhai, Z. Li, A. Li, J. Wang, N. Zhang
IEEE Real-Time Systems Symposium (RTSS), 2022
[PDF] [GitHub]
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features
L. Tong, B. Li, C. Hajaj, C. Xiao, N. Zhang, Y. Vorobeychik
USENIX Security Symposium (Security), 2019
[PDF] [GitHub]

Acknowledgement

This project is supported by ARO under award W911NF-24-1-0155.