Direct Memory Access (DMA) introduces a security vulnerability as peripherals are given direct access to system memory, exposing privileged data to potentially malicious Input/Output (IO) devices. Modern systems are equipped with an IO Memory Management Unit (IOMMU) to mitigate such DMA attacks. The OS uses the IOMMU and IO page tables to map and unmap a designated memory region before and after the DMA operation, constraining each DMA request to the approved region. IOMMU protection comes at the cost of reduced throughput in IO-intensive workloads, mainly due to the high IOTLB invalidation latency. The Linux OS eliminates this bottleneck by deferring the IOTLB invalidation requests to a later time. This opens a vulnerability window during which a memory region is unmapped but the relevant IOTLB entry remains. In this paper, we present a proof-of-concept exploit, empirically demonstrating that a malicious DMA-capable device can use this vulnerability window to leak data used by other devices. Furthermore, we propose hardware-assisted mitigation for the deferred invalidation vulnerability by making minor changes to the existing IOMMU hardware and OS software. We implemented the proposed mitigation in the Intel IOMMU implementation in QEMU and the Linux kernel. Our security evaluation showed that our proposed mitigation successfully mitigated the deferred invalidation vulnerability and provided 12.7% higher throughput compared to the strict invalidation mode.

Timing side channels can be inadvertently introduced into processor microarchitecture during the design process, mainly due to optimizations carried out to improve processor performance. These timing side channels have been used in various attacks including transient execution attacks on recent commodity proces-sors. Hence, we need a tool to detect timing side channels during the design process. This paper presents SIGFuzz, a fuzzing-based framework for detecting microarchitectural timing side channels. A designer can use SIGFuzz to detect side channels early in the design flow and mitigate potential vulnerabilities associated with them. SIGFuzz generates a cycle-accurate microarchitectural trace for a program that executes on the target processor, it then uses two trace properties to identify side channels that would have been formed by the program. These two trace properties evaluate the effect of each instruction in the program, on the timing of its prior and later instructions, respectively. SIGFuzz also uses a statistical distribution of execution delays of instructions with the same mnemonic to flag potential side channels that manifest with different operands of an instruction. Furthermore, SIGFuzz automatically groups the detected side channels based on the microarchitectural activity trace (i.e. signature) of the instruction that triggered it. We evaluated SIGFuzz on two real-world open-source processor designs: Rocket and BOOM, and found three new side channels and two known side channels. We present a novel Spectre-style attack on BOOM based on one of the newly detected side channel.