(2022) Minefield: A Software-only Protection for SGX Enclaves against DVFS Attacks.
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Abstract
Modern CPUs adapt clock frequencies and voltage levels to workloads to reduce energy consumption and heat dissipation. This mechanism, dynamic voltage and frequency scaling (DVFS), is controlled from privileged software but affects all execution modes, including SGX. Prior work showed that manipulating voltage or frequency can fault instructions and thereby subvert SGX enclaves. Consequently, Intel disabled the overclocking mailbox (OCM) required for software undervolting, also preventing benign use for energy saving. In this paper, we propose Minefield, the first software-level defense against DVFS attacks. The idea of Minefield is not to prevent DVFS faults but to deflect faults to trap instructions and handle them before they lead to harmful behavior. As groundwork for Minefield, we systematically analyze DVFS attacks and observe a timing gap of at least 57.8 us between every OCM transition, leading to random faults over at least 57000 cycles. Minefield places highly fault-susceptible trap instructions in the victim code during compilation. Like redundancy countermeasures, Minefield is scalable and enables enclave developers to choose a security parameter between 0% and almost 100%, yielding a fine-grained security-performance trade-off. Our evaluation shows a density of 0.75, i.e., one trap after every 1-2 instruction, mitigates all known DVFS attacks in 99% on Intel SGX, incurring an overhead of 148.4% on protected enclaves. However, Minefield has no performance effect on the remaining system. Thus, Minefield is a better solution than hardware- or microcode-based patches disabling the OCM interface.
Item Type: | Conference or Workshop Item (A Paper) (Paper) |
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Divisions: | Michael Schwarz (MS) |
Conference: | USENIX-Security Usenix Security Symposium |
Depositing User: | Michael Schwarz |
Date Deposited: | 23 Feb 2022 12:45 |
Last Modified: | 23 Feb 2022 12:45 |
Primary Research Area: | NRA3: Threat Detection and Defenses |
URI: | https://publications.cispa.saarland/id/eprint/3575 |
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