HyBP: Hybrid Isolation-Randomization Secure Branch Predictor
Lutan Zhao, Peinan Li, Rui Hou, Michael C. Huang, Xuehai Qian, Lixin Zhang, Dan Meng
摘要
Recently exposed vulnerabilities reveal the necessity to improve the security of branch predictors. Branch predictors record history about the execution of different processes, and such information from different processes are stored in the same structure and thus accessible to each other. This leaves the attackers with the opportunities for malicious training and malicious perception. Physical or logical isolation mechanisms such as using dedicated tables and flushing during context-switch can provide security but incur non-trivial costs in space and/or execution time. Randomization mechanisms incurs the performance cost in a different way: those with higher securities add latency to the critical path of the pipeline, while the simpler alternatives leave vulnerabilities to more sophisticated attacks.
This paper proposes HyBP, a practical hybrid protection and effective mechanism for building secure branch predictors. The design applies the physical isolation and randomization in the right component to achieve the best of both worlds. We propose to protect the smaller tables with physically isolation based on (thread, privilege) combination; and protect the large tables with randomization. Surprisingly, the physical isolation also significantly enhances the security of the last-level tables by naturally filtering out accesses, reducing the information flow to these bigger tables. As a result, key changes can happen less frequently and be performed conveniently at context switches. Moreover, we propose a latency hiding design for a strong cipher by precomputing the "code book" with a validated, cryptographically strong cipher. Overall, our design incurs a performance penalty of 0.5% compared to 5.1% of physical isolation under the default context switching interval in Linux.
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引用它的顶会 Paper5
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它引用的顶会 Paper6
- Spectre Attacks: Exploiting Speculative ExecutionPaul Kocher, Jann Horn, Anders Fogh, Daniel Genkin 等S&P 2019 · 被引用 2,435 次
- Inferring Fine-grained Control Flow Inside SGX Enclaves with Branch ShadowingSangho Lee, Ming-Wei Shih, Prasun Gera, Taesoo Kim 等USENIX Security 2017 · 被引用 536 次
- ScatterCache: Thwarting Cache Attacks via Cache Set RandomizationMario Werner, Thomas Unterluggauer, Lukas Giner, Michael Schwarz 等USENIX Security 2019 · 被引用 221 次
- Systematic Analysis of Randomization-based Protected Cache ArchitecturesAntoon Purnal, Lukas Giner, Daniel Gruss, Ingrid VerbauwhedeS&P 2021 · 被引用 93 次
- Exploring Branch Predictors for Constructing Transient Execution TrojansTao Zhang, Kenneth Koltermann, Dmitry EvtyushkinASPLOS 2020 · 被引用 32 次
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