A Survey on Thwarting Memory Corruption in RISC-V

Author:

Brohet Marco1ORCID,Regazzoni Francesco2ORCID

Affiliation:

1. University of Amsterdam, The Netherlands

2. University of Amsterdam, The Netherlands and Università della Svizzera italiana, Switzerland

Abstract

With embedded devices becoming more pervasive and entrenched in society, it is paramount to keep these systems secure. A threat plaguing these systems consists of software vulnerabilities that cause memory corruption, potentially allowing an attacker to breach the device. Software-based countermeasures exist, but suffer from high overhead. In this survey, we investigate whether this could be mitigated using dedicated hardware. Driven by the advancements of open hardware, we focus on implementations for RISC-V, a novel and open architecture tailored for customization. We distinguish between methods validating memory accesses beforehand, obfuscating information necessary for an attack, and detecting memory values corrupted earlier. We compare on qualitative metrics, such as the security coverage and level of transparency, and performance in both software and hardware. Although current implementations do not easily allow for a fair comparison as their evaluation methodologies vary widely, we show that current implementations are suitable to minimize the runtime overhead with a relatively small area overhead. Nevertheless, we identified that further research is still required to mitigate more fine-grained attacks such as intra-object overflows, to integrate into more sophisticated protected execution environments towards resilient systems that are automatically recoverable, and to move towards more harmonized evaluation.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Reference83 articles.

1. Control-flow integrity

2. Arm Limited. 2019. Armv8.5-A Memory Tagging Extensions. Arm Limited, Technical Report. Retrieved from https://developer.arm.com/-/media/ArmDeveloperCommunity/PDF/Arm_Memory_Tagging_Extension_Whitepaper.pdf

3. Krste Asanović and David A. Patterson. 2014. Instruction Sets Should Be Free: The Case for RISC-V. Technical Report UCB/EECS-2014-146. EECS Department, University of California, Berkeley. Retrieved from https://www2.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS-2014-146.html

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3