Zelun Kong (University of Texas at Dallas), Minkyung Park (University of Texas at Dallas), Le Guan (University of Georgia), Ning Zhang (Washington University in St. Louis), Chung Hwan Kim (University of Texas at Dallas)

As reliance on embedded systems grows in critical domains such as healthcare, industrial automation, and unmanned vehicles, securing the data on micro-controller units (MCUs) becomes increasingly crucial. These systems face significant challenges related to computational power and energy constraints, complicating efforts to maintain the confidentiality and integrity of sensitive data. Previous methods have utilized compartmentalization techniques to protect this sensitive data, yet they remain vulnerable to breaches by strong adversaries exploiting privileged software.

In this paper, we introduce TZ-DATASHIELD, a novel LLVM compiler tool that enhances ARM TrustZone with sensitive data flow (SDF) compartmentalization, offering robust protection against strong adversaries in MCU-based systems. We address three primary challenges: the limitations of existing compartment units, inadequate isolation within the Trusted Execution Environment (TEE), and the exposure of shared data to potential attacks. TZ-DATASHIELD addresses these challenges by implementing a fine-grained compartmentalization approach that focuses on sensitive data flow, ensuring data confidentiality and integrity, and developing a novel intra-TEE isolation mechanism that validates compartment access to TEE resources at runtime. Our prototype enables firmware developers to annotate source code to generate TrustZone-ready firmware images automatically. Our evaluation using real-world MCU applications demonstrates that TZ-DATASHIELD achieves up to 80.8% compartment memory and 88.6% ROP gadget reductions within the TEE address space. It incurs an average runtime overhead of 14.7% with CFI and DFI enforcement, and 7.6% without these measures.

View More Papers

MALintent: Coverage Guided Intent Fuzzing Framework for Android

Ammar Askar (Georgia Institute of Technology), Fabian Fleischer (Georgia Institute of Technology), Christopher Kruegel (University of California, Santa Barbara), Giovanni Vigna (University of California, Santa Barbara), Taesoo Kim (Georgia Institute of Technology)

Read More

Oreo: Protecting ASLR Against Microarchitectural Attacks

Shixin Song (Massachusetts Institute of Technology), Joseph Zhang (Massachusetts Institute of Technology), Mengjia Yan (Massachusetts Institute of Technology)

Read More

LeoCommon – A Ground Station Observatory Network for LEO...

Eric Jedermann, Martin Böh (University of Kaiserslautern), Martin Strohmeier (armasuisse Science & Technology), Vincent Lenders (Cyber-Defence Campus, armasuisse Science & Technology), Jens Schmitt (University of Kaiserslautern)

Read More

Delay-allowed Differentially Private Data Stream Release

Xiaochen Li (University of Virginia), Zhan Qin (Zhejiang University), Kui Ren (Zhejiang University), Chen Gong (University of Virginia), Shuya Feng (University of Connecticut), Yuan Hong (University of Connecticut), Tianhao Wang (University of Virginia)

Read More