skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Cloud FPGA Cartography using PCIe Contention
Public cloud infrastructures allow for easy, on-demand access to FPGA resources. However, the low-level, direct access to the FPGA hardware exposes the infrastructure providers to new types of attacks. Prior work has shown that it is possible to uniquely identify the underlying hardware by creating fingerprints of the different FPGA instances that users rent from a cloud provider, but such work was not able to actually map the cloud FPGA infrastructure itself. Meanwhile, this paper demonstrates that it is possible to reverse-engineer the co-location of FPGA boards inside a cloud FPGA server using PCIe contention. Specifically, this work deduces the Non-Uniform Memory Access (NUMA) locality of FPGA boards within a server by analyzing their mutual PCIe contention during simultaneous use of the PCIe bus. In addition, experiments conducted in data centers located in several geographic regions and repeated at different times are used to calculate the probability that cloud providers allocate FPGA boards co-located in the same server to a user. This paper thus shows that it is possible to map cloud FPGA infrastructures, and learn how FPGA instances are physically co-located within a server. Consequently, this paper also highlights the importance of mitigating these novel avenues for reverse-engineering and mapping of cloud FPGA setups, as they can reveal insights about the cloud infrastructure itself, or assist other single- and multi-tenant attacks.  more » « less
Award ID(s):
1901901
PAR ID:
10225317
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
International Symposium on Field-Programmable Custom Computing Machines (FCCM)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. The availability of FPGAs in cloud data centers offers rapid, on-demand access to reconfigurable hardware compute resources that users can adapt to their own needs. However, the low-level access to the FPGA hardware and associated resources such as the PCIe bus, SSD drives, or DRAM modules also opens up threats of malicious attackers uploading designs that are able to infer information about other users or about the cloud infrastructure itself. In particular, this work presents a new, fast PCIe-contention-based channel that is able to transmit data between FPGA-accelerated virtual machines by modulating the PCIe bus usage. This channel further works with different operating systems, and achieves bandwidths reaching 20 kbps with 99% accuracy. This is the first cross-FPGA covert channel demonstrated on commercial clouds, and has a bandwidth which is over 2000 × larger than prior voltage- or temperature-based cross-board attacks. This paper further demonstrates that the PCIe receivers are able to not just receive covert transmissions, but can also perform fine-grained monitoring of the PCIe bus, including detecting when co-located VMs are initialized, even prior to their associated FPGAs being used. Moreover, the proposed mechanism can be used to infer the activities of other users, or even slow down the programming of the co-located FPGAs as well as other data transfers between the host and the FPGA. Beyond leaking information across different virtual machines, the ability to monitor the PCIe bandwidth over hours or days can be used to estimate the data center utilization and map the behavior of the other users. The paper also introduces further novel threats in FPGA-accelerated instances, including contention due to network traffic, contention due to shared NVMe SSDs, as well as thermal monitoring to identify FPGA co-location using the DRAM modules attached to the FPGA boards. This is the first work to demonstrate that it is possible to break the separation of privilege in FPGA-accelerated cloud environments, and highlights that defenses for public clouds using FPGAs need to consider PCIe, SSD, and DRAM resources as part of the attack surface that should be protected. 
    more » « less
  2. In recent years, multiple public cloud FPGA providers have emerged,increasing interest in FPGA acceleration of cryptographic, bioinformatic, financial, and machine learning algorithms. To help understand the security of the cloud FPGA infrastructures, this paper focuses on a fundamental question of understanding what an adversary can learn about the cloud FPGA infrastructure itself, without attacking it or damaging it. In particular, this work explores how unique features of FPGAs can be exploited to instantiate Physical Unclonable Functions (PUFs) that can distinguish between otherwise-identical FPGA boards. This paper specifically introduces the first method for identifying cloud FPGA instances by extracting a unique and stable FPGA fingerprint based on PUFs measured from the FPGA boards’ DRAM modules. Experiments conducted on the Amazon Web Services (AWS) cloud reveal the probability of renting the same physical board more than once. Moreover, the experimental results show that hardware is not shared amongf1.2xlarge,f1.4xlarge, andf1.16xlargeinstance types. As the approach used does not violate any restrictions currently placed by Amazon,this paper also presents a set of defense mechanisms that can be added to existing countermeasures to mitigate users’ attempts to fingerprint cloud FPGA infrastructures. 
    more » « less
  3. Field-Programmable Gate Arrays (FPGAs) are ver-satile, reconfigurable integrated circuits that can be used ashardware accelerators to process highly-sensitive data. Leakingthis data and associated cryptographic keys, however, can un-dermine a system’s security. To prevent potentially unintentionalinteractions that could break separation of privilege betweendifferent data center tenants, FPGAs in cloud environments arecurrently dedicated on a per-user basis. Nevertheless, while theFPGAs themselves are not shared among different users, otherparts of the data center infrastructure are. This paper specificallyshows for the first time that powering FPGAs, CPUs, and GPUsthrough the same power supply unit (PSU) can be exploitedin FPGA-to-FPGA, CPU-to-FPGA, and GPU-to-FPGA covertchannels between independent boards. These covert channelscan operate remotely, without the need for physical access to,or modifications of, the boards. To demonstrate the attacks, thispaper uses a novel combination of “sensing” and “stressing” ringoscillators as receivers on the sink FPGA. Further, ring oscillatorsare used as transmitters on the source FPGA. The transmittingand receiving circuits are used to determine the presence of theleakage on off-the-shelf Xilinx boards containing Artix 7 andKintex 7 FPGA chips. Experiments are conducted with PSUs bytwo vendors, as well as CPUs and GPUs of different generations.Moreover, different sizes and types of ring oscillators are alsotested. In addition, this work discusses potential countermeasuresto mitigate the impact of the cross-board leakage. The results ofthis paper highlight the dangers of shared power supply unitsin local and cloud FPGAs, and therefore a fundamental need tore-think FPGA security for shared infrastructures. 
    more » « less
  4. In recent decades, due to the emerging requirements of computation acceleration, cloud FPGAs have become popular in public clouds. Major cloud service providers, e.g. AWS and Microsoft Azure have provided FPGA computing resources in their infrastructure and have enabled users to design and deploy their own accelerators on these FPGAs. Multi-tenancy FPGAs, where multiple users can share the same FPGA fabric with certain types of isolation to improve resource efficiency, have already been proved feasible. However, this also raises security concerns. Various types of side-channel attacks targeting multi-tenancy FPGAs have been proposed and validated. The awareness of security vulnerabilities in the cloud has motivated cloud providers to take action to enhance the security of their cloud environments. In FPGA security research papers, researchers always perform attacks under the assumption that attackers successfully co-locate with victims and are aware of the existence of victims on the same FPGA board. However, the way to reach this point, i.e., how attack- ers secretly obtain information regarding accelerators on the same fabric, is constantly ignored despite the fact that it is non-trivial and important for attackers. In this paper, we present a novel finger- printing attack to gain the types of co-located FPGA accelerators. We utilize a seemingly non-malicious benchmark accelerator to sniff the communication link and collect performance traces of the FPGA-host communication link. By analyzing these traces, we are able to achieve high classification accuracy for fingerprinting co-located accelerators, which proves that attackers can use our method to perform cloud FPGA accelerator fingerprinting with a high success rate. As far as we know, this is the first paper targeting multi-tenant FPGA accelerator fingerprinting with the communica- tion side-channel. 
    more » « less
  5. Cloud computing has become crucial for the commercial world due to its computational capacity, storage capabilities, scalability, software integration, and billing convenience. Initially, clouds were relatively homogeneous, but now diverse machine configurations in heterogeneous clouds are recognized for their improved application performance and energy efficiency. This shift is driven by the integration of various hardware to accommodate diverse user applications. However, alongside these advancements, security threats like micro-architectural attacks are increasing concerns for cloud providers and users. Studies like Repttack and Cloak & Co-locate highlight the vulnerability of heterogeneous clouds to co-location attacks, where attacker and victim instances are placed together. The ease of these attacks isn’t solely linked to heterogeneity but also correlates with how heterogeneous the target systems are. Despite this, no numerical metrics exist to quantify cloud heterogeneity. This article introduces the Heterogeneity Score (HeteroScore) to evaluate server setups and instances. HeteroScore significantly correlates with co-location attack security. The article also proposes strategies to balance diversity and security. This study pioneers the quantitative analysis connecting cloud heterogeneity and infrastructure security. 
    more » « less