Trustworthy Systems

Systematic prevention of on-core timing channels by full temporal partitioning


Nils Wistoff, Moritz Schneider, Frank Gürkaynak, Gernot Heiser and Luca Benini

    School of Computer Science and Engineering
    Sydney 2052, Australia


Microarchitectural timing channels enable unwanted information flow across security boundaries, violating fundamental security assumptions. They leverage timing variations of several state-holding microarchitectural components and have been demonstrated across instruction set architectures and hardware implementations. Analogously to memory protection, Ge et al. have proposed time protection for preventing information leakage via timing channels. They also showed that time protection calls for hardware support. This work leverages the open and extensible RISC-V instruction set architecture (ISA) to introduce the temporal fence instruction fence.t, which provides the required mechanisms by clearing vulnerable microarchitectural state and guaranteeing a history-independent context-switch latency. We propose and discuss three different implementations of fence.t and implement them on an experimental version of the seL4 microkernel and CVA6, an open-source, in-order, application class, 64-bit RISC-V core. We find that a complete, systematic, ISA-supported erasure of all non-architectural core components is the most effective implementation while featuring a low implementation effort, a minimal performance overhead of approximately 2%, and negligible hardware costs.

BibTeX Entry

    author           = {Wistoff, Nils and Schneider, Moritz and G\"{u}rkaynak, Frank and Heiser, Gernot and Benini, Luca},
    journal          = {IEEE Transactions on Computers},
    keywords         = {time protection, micro-architectural timing channels, seL4, covert channels},
    month            = xyz,
    note             = {To appear},
    number           = {xyz},
    pages            = {xyz},
    paperurl         = {},
    title            = {Systematic Prevention of On-Core Timing Channels by Full Temporal Partitioning},
    volume           = {xyz},
    year             = {2023}