arXiv subjects

Taehyun Noh

Publications and source records attributed to Taehyun Noh.

3 recordsLinked to original sources

arXiv2026

ARM MTE Performance in Practice (Extended Version)

We present the first comprehensive analysis of ARM MTE hardware performance on four different microarchitectures: ARM Big (A7x), Little (A5x), and Performance (Cortex-X) cores on the Google Pixel 8 and Pixel 9, and on Ampere Computing's AmpereOne CPU core. We also include preliminary analysis of MTE on Apple's M5 chip. We investigate performance in MTE's primary application -- probabilistic memory safety -- on both SPEC CPU benchmarks and in server workloads such as RocksDB, Nginx, PostgreSQL, and Memcached. While MTE often exhibits modest overheads, we also see performance slowdowns up to 6.64x on certain benchmarks. We identify the microarchitectural cause of these overheads and where they can be addressed in future processors. We then analyze MTE's performance for more specialized security applications such as memory tracing, time-of-check time-of-use prevention, sandboxing, and CFI. In some of these cases, MTE offers significant advantages today, while the benefits for other cases are negligible or will depend on future hardware. Finally, we explore where prior work characterizing MTE performance has either been incomplete or incorrect due to methodological or experimental errors.

cs.CR↗

arXiv2023

Capacity: Cryptographically-Enforced In-Process Capabilities for Modern ARM Architectures (Extended Version)

In-process compartmentalization and access control have been actively explored to provide in-place and efficient isolation of in-process security domains. Many works have proposed compartmentalization schemes that leverage hardware features, most notably using the new page-based memory isolation feature called Protection Keys for Userspace (PKU) on x86. Unfortunately, the modern ARM architecture does not have an equivalent feature. Instead, newer ARM architectures introduced Pointer Authentication (PA) and Memory Tagging Extension (MTE), adapting the reference validation model for memory safety and runtime exploit mitigation. We argue that those features have been underexplored in the context of compartmentalization and that they can be retrofitted to implement a capability-based in-process access control scheme. This paper presents Capacity, a novel hardware-assisted intra-process access control design that embraces capability-based security principles. Capacity coherently incorporates the new hardware security features on ARM that already exhibit inherent characteristics of capability. It supports the life-cycle protection of the domain's sensitive objects -- starting from their import from the file system to their place in memory. With intra-process domains authenticated with unique PA keys, Capacity transforms file descriptors and memory pointers into cryptographically-authenticated references and completely mediates reference usage with its program instrumentation framework and an efficient system call monitor. We evaluate our Capacity-enabled NGINX web server prototype and other common applications in which sensitive resources are isolated into different domains. Our evaluation shows that Capacity incurs a low-performance overhead of approximately 17% for the single-threaded and 13.54% for the multi-threaded webserver.

cs.CR↗

arXiv2022

Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective

As machine learning (ML) technologies and applications are rapidly changing many computing domains, security issues associated with ML are also emerging. In the domain of systems security, many endeavors have been made to ensure ML model and data confidentiality. ML computations are often inevitably performed in untrusted environments and entail complex multi-party security requirements. Hence, researchers have leveraged the Trusted Execution Environments (TEEs) to build confidential ML computation systems. We conduct a systematic and comprehensive survey by classifying attack vectors and mitigation in confidential ML computation in untrusted environments, analyzing the complex security requirements in multi-party scenarios, and summarizing engineering challenges in confidential ML implementation. Lastly, we suggest future research directions based on our study.

cs.CR↗