LeakyRand: An Efficient High-fidelity Covert Channel in Fully Associative Last-level Caches with Random Eviction

Recent studies on secure last-level cache (LLC) have advocated the fully associative organization to defend against conflict-based side-channel attacks. In a fully associative LLC, an attacker cannot extract any information about the cache location of the addresses evicted due to cross-core conflict. Use of the random replacement policy further guards against any deterministic eviction patterns. However, the fully associative LLC design remains vulnerable against timing-based covert channel attacks. In this article, we present LeakyRand , a high-bandwidth covert communication mechanism that exploits the fully associative LLC with random replacement while guaranteeing an ultra-low bit error rate (BER). Our proposal is a union of three unique contributions. First, we present an efficient algorithm with strong analytical guarantees that enables the attacker to quickly occupy nearly the whole LLC with high probability, a prerequisite for achieving high bandwidth and high fidelity. Second, we present a novel covert communication protocol that allows the attacker to maintain high LLC occupancy. Third, our proposal detects error syndromes and efficiently takes corrective measures leading to an ultra-low expected BER. Exploiting a 2 MB fully associative LLC with random replacement policy to set up a covert channel, LeakyRand experiences an average BER of 10 -4 or less while offering 3.3× to 5.7× higher channel bandwidth compared to the recently proposed Stochastic Prime+Probe attack. We also demonstrate that LeakyRand can be adopted to mount high-precision fine-grain fingerprinting attacks.

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Publication Details

Journal
ACM Transactions on Embedded Computing Systems
Published
2025-08-25
DOI
https://doi.org/10.1145/3761797
Primary Topic
Internet Traffic Analysis and Secure E-voting
Type
article
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article

LeakyRand: An Efficient High-fidelity Covert Channel in Fully Associative Last-level Caches with Random Eviction

M. Chaudhuri, Yashika Verma, Debadatta Mishra
ACM Transactions on Embedded Computing Systems
Internet Traffic Analysis and Secure E-voting
article

LeakyRand: An Efficient High-fidelity Covert Channel in Fully Associative Last-level Caches with Random Eviction

M. Chaudhuri, Yashika Verma, Debadatta Mishra
article en

Abstract

Recent studies on secure last-level cache (LLC) have advocated the fully associative organization to defend against conflict-based side-channel attacks. In a fully associative LLC, an attacker cannot extract any information about the cache location of the addresses evicted due to cross-core conflict. Use of the random replacement policy further guards against any deterministic eviction patterns. However, the fully associative LLC design remains vulnerable against timing-based covert channel attacks. In this article, we present LeakyRand , a high-bandwidth covert communication mechanism that exploits the fully associative LLC with random replacement while guaranteeing an ultra-low bit error rate (BER). Our proposal is a union of three unique contributions. First, we present an efficient algorithm with strong analytical guarantees that enables the attacker to quickly occupy nearly the whole LLC with high probability, a prerequisite for achieving high bandwidth and high fidelity. Second, we present a novel covert communication protocol that allows the attacker to maintain high LLC occupancy. Third, our proposal detects error syndromes and efficiently takes corrective measures leading to an ultra-low expected BER. Exploiting a 2 MB fully associative LLC with random replacement policy to set up a covert channel, LeakyRand experiences an average BER of 10 -4 or less while offering 3.3× to 5.7× higher channel bandwidth compared to the recently proposed Stochastic Prime+Probe attack. We also demonstrate that LeakyRand can be adopted to mount high-precision fine-grain fingerprinting attacks.

ACM Transactions on Embedded Computing SystemsVol. 24(5s)
Indian Institute of Technology Kanpur (IN)
Openalex Percentile: Top 89%
Internet Traffic Analysis and Secure E-voting
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