| Title | Invisible Probe: Timing Attacks with PCIe Congestion Side-channel |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Tan, Mingtian, Wan, Junpeng, Zhou, Zhe, Li, Zhou |
| Conference Name | 2021 IEEE Symposium on Security and Privacy (SP) |
| Date Published | may |
| Keywords | composability, delays, graphics processing units, Human Behavior, machine learning, Metrics, PCIe, performance evaluation, privacy, Protocols, pubcrawl, resilience, Resiliency, side-channel, Switches, virtualization privacy |
| Abstract | PCIe (Peripheral Component Interconnect express) protocol is the de facto protocol to bridge CPU and peripheral devices like GPU, NIC, and SSD drive. There is an increasing demand to install more peripheral devices on a single machine, but the PCIe interfaces offered by Intel CPUs are fixed. To resolve such contention, PCIe switch, PCH (Platform Controller Hub), or virtualization cards are installed on the machine to allow multiple devices to share a PCIe interface. Congestion happens when the collective PCIe traffic from the devices overwhelm the PCIe link capacity, and transmission delay is then introduced.In this work, we found the PCIe delay not only harms device performance but also leaks sensitive information about a user who uses the machine. In particular, as user's activities might trigger data movement over PCIe (e.g., between CPU and GPU), by measuring PCIe congestion, an adversary accessing another device can infer the victim's secret indirectly. Therefore, the delay resulted from I/O congestion can be exploited as a side-channel. We demonstrate the threat from PCIe congestion through 2 attack scenarios and 4 victim settings. Specifically, an attacker can learn the workload of a GPU in a remote server by probing a RDMA NIC that shares the same PCIe switch and measuring the delays. Based on the measurement, the attacker is able to know the keystroke timings of the victim, what webpage is rendered on the GPU, and what machine-learning model is running on the GPU. Besides, when the victim is using a low-speed device, e.g., an Ethernet NIC, an attacker controlling an NVMe SSD can launch a similar attack when they share a PCH or virtualization card. The evaluation result shows our attack can achieve high accuracy (e.g., 96.31% accuracy in inferring webpage visited by a victim). |
| DOI | 10.1109/SP40001.2021.00059 |
| Citation Key | tan_invisible_2021 |
Invisible Probe: Timing Attacks with PCIe Congestion Side-channel