A Software Approach to Defeating Side Channels in Last-Level Caches

Программный подход к устранению побочных каналов в кэшах последнего уровня
Michael K. Reiter, Yinqian Zhang, Ziqiao Zhou
2016-10-24

CacheBarFlush-Reload attacksPrime-Probe attackscontainer isolationlast-level cache side channels
We present a software approach to mitigate access-driven side-channel attacks that leverage last-level caches (LLCs) shared across cores to leak information between security domains (e.g., tenants in a cloud). Our approach dynamically manages physical memory pages shared between security domains to disable sharing of LLC lines, thus preventing "Flush-Reload" side channels via LLCs. It also manages cacheability of memory pages to thwart cross-tenant "Prime-Probe" attacks in LLCs. We have implemented our approach as a memory management subsystem called CacheBar within the Linux kernel to intervene on such side channels across container boundaries, as containers are a common method for enforcing tenant isolation in Platform-as-a-Service (PaaS) clouds. Through formal verification, principled analysis, and empirical evaluation, we show that CacheBar achieves strong security with small performance overheads for PaaS workloads.
1
CacheBar is a Linux-kernel memory-management subsystem that mitigates LLC-based access-driven side channels across container and tenant boundaries.
2
Dynamically managing physical pages shared between security domains disables LLC-line sharing, preventing Flush-Reload attacks.
3
Formal verification, principled analysis, and empirical evaluation show strong security with small performance overheads for PaaS workloads.
4
Managing page cacheability thwarts cross-tenant Prime-Probe attacks targeting last-level caches.

last-level caches (LLCs) shared across cores between security domains, including containerized PaaS tenants

software-based mitigation of access-driven side-channel attacks through dynamic control of physical-page sharing and memory-page cacheability, with security and performance effects

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Publication Date
2016-10-24
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Authors
Michael K. Reiter
Yinqian Zhang
Ziqiao Zhou
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