Ragnar: Exploring Volatile-Channel Vulnerabilities on RDMA NIC

With the surge in data computation, Remote Direct Memory Access (RDMA) becomes crucial to offering low-latency and highthroughput communication for data centers, but it faces new security threats. This paper presents RAGNAR, a comprehensive suite of hardware-contention-based volatile-channel attacks leveraging the underexplored security vulnerabilities in RDMA hardware. Through comprehensive microbenchmark reverse engineering, we analyze RDMA NICs at multiple granularity levels and then construct covert-channel attacks, achieving 3.2x the bandwidth of state-of-the-art RDMA-targeted attacks on CX-5. We apply side-channel attacks on real-world distributed databases and disaggregated memory, where we successfully fingerprint operations and recover sensitive address data with 95.6% accuracy.

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Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1109/dac63849.2025.11133127
Primary Topic
Security and Verification in Computing
Type
article
Field-Weighted Citation Impact
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article

Ragnar: Exploring Volatile-Channel Vulnerabilities on RDMA NIC

Yu‐Chen Fan, Teng Ma, Yunpeng Xu, Shuwen Deng
Security and Verification in Computing
article

Ragnar: Exploring Volatile-Channel Vulnerabilities on RDMA NIC

Yu‐Chen Fan, Teng Ma, Yunpeng Xu, Shuwen Deng
article en

Abstract

With the surge in data computation, Remote Direct Memory Access (RDMA) becomes crucial to offering low-latency and highthroughput communication for data centers, but it faces new security threats. This paper presents RAGNAR, a comprehensive suite of hardware-contention-based volatile-channel attacks leveraging the underexplored security vulnerabilities in RDMA hardware. Through comprehensive microbenchmark reverse engineering, we analyze RDMA NICs at multiple granularity levels and then construct covert-channel attacks, achieving 3.2x the bandwidth of state-of-the-art RDMA-targeted attacks on CX-5. We apply side-channel attacks on real-world distributed databases and disaggregated memory, where we successfully fingerprint operations and recover sensitive address data with 95.6% accuracy.

Alibaba Group (United States) (US), Tsinghua University (CN)
Openalex Percentile: Top 87%
Security and Verification in Computing
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