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InvisiSpec: Making Speculative Execution Invisible in the Cache Hierarchy

Authors: Mengjia Yan 0001; Jiho Choi; Dimitrios Skarlatos 0002; Adam Morrison 0001; Christopher W. Fletcher; Josep Torrellas;

InvisiSpec: Making Speculative Execution Invisible in the Cache Hierarchy

Abstract

Hardware speculation offers a major surface for micro-architectural covert and side channel attacks. Unfortunately, defending against speculative execution attacks is challenging. The reason is that speculations destined to be squashed execute incorrect instructions, outside the scope of what programmers and compilers reason about. Further, any change to micro-architectural state made by speculative execution can leak information. In this paper, we propose InvisiSpec, a novel strategy to defend against hardware speculation attacks in multiprocessors by making speculation invisible in the data cache hierarchy. InvisiSpec blocks micro-architectural covert and side channels through the multiprocessor data cache hierarchy due to speculative loads. In InvisiSpec, unsafe speculative loads read data into a speculative buffer, without modifying the cache hierarchy. When the loads become safe, InvisiSpec makes them visible to the rest of the system. InvisiSpec identifies loads that might have violated memory consistency and, at this time, forces them to perform a validation step. We propose two InvisiSpec designs: one to defend against Spectre-like attacks and another to defend against futuristic attacks, where any speculative load may pose a threat. Our simulations with 23 SPEC and 10 PARSEC workloads show that InvisiSpec is effective. Under TSO, using fences to defend against Spectre attacks slows down execution by 74% relative to a conventional, insecure processor; InvisiSpec reduces the execution slowdown to only 21%. Using fences to defend against futuristic attacks slows down execution by 208%; InvisiSpec reduces the slowdown to 72%.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
130
Top 1%
Top 1%
Top 1%
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