Abstract-We identify a timing channel in the floating point instructions of modern x86 processors: the running time of floating point addition and multiplication instructions can vary by two orders of magnitude depending on their operands. We develop a benchmark measuring the timing variability of floating point operations and report on its results. We use floating point data timing variability to demonstrate practical attacks on the security of the Firefox browser (versions 23 through 27) and the Fuzz differentially private database. Finally, we initiate the study of mitigations to floating point data timing channels with libfixedtimefixedpoint, a new fixed-point, constant-time math library.Modern floating point standards and implementations are sophisticated, complex, and subtle, a fact that has not been sufficiently recognized by the security community. More work is needed to assess the implications of the use of floating point instructions in security-relevant software.
We argue that five recent software and hardware developments -the AES-NI instructions, multicore processors with per-core caches, complex modern software, sophisticated prefetchers, and physically tagged caches -combine to make it substantially more difficult to mount data-cache side-channel attacks on AES than previously realized. We propose ways in which some of the challenges posed by these developments might be overcome. We also consider scenarios where sidechannel attacks are attractive, and whether our proposed workarounds might be applicable to these scenarios.
We present three techniques for extracting entropy during boot on embedded devices. Our first technique times the execution of code blocks early in the Linux kernel boot process. It is simple to implement and has a negligible runtime overhead, but, on many of the devices we test, gathers hundreds of bits of entropy. Our second and third techniques, which run in the bootloader, use hardware features-DRAM decay behavior and PLL locking latency, respectively-and are therefore less portable and less generally applicable, but their behavior is easier to explain based on physically unpredictable processes. We implement and measure the effectiveness of our techniques on ARM-, MIPS-, and AVR32-based systems-on-a-chip from a variety of vendors.
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