KernelSnitch: Side Channel-Attacks on Kernel Data Structures

The sharing of hardware elements, such as caches, is known to introduce microarchitectural side-channel leakage.One approach to eliminate this leakage is to not share hardware elements across security domains.However, even under the assumption of leakage-free hardware, it is unclear whether other critical system components, like the operating system, introduce software-caused side-channel leakage.In this paper, we present a novel generic software side-channel attack, KernelSnitch, targeting kernel data structures such as hash tables and trees.These structures are commonly used to store both kernel and user information, e.g., metadata for userspace locks.KernelSnitch exploits that these data structures are variable in size, ranging from an empty state to a theoretically arbitrary amount of elements.Accessing these structures requires a variable amount of time depending on the number of elements, i.e., the occupancy level.This variance constitutes a timing side channel, observable from user space by an unprivileged, isolated attacker.While the timing differences are very low compared to the syscall runtime, we demonstrate and evaluate methods to amplify these timing differences reliably.In three case studies, we show that KernelSnitch allows unprivileged and isolated attackers to leak sensitive information from the kernel and activities in other processes.First, we demonstrate covert channels with transmission rates up to 580 kbit/s.Second, we perform a kernel heap pointer leak in less than 65 s by exploiting the specific indexing that Linux is using in hash tables.Third, we demonstrate a website fingerprinting attack, achieving an F1 score of more than 89 %, showing that activity in other user programs can be observed using KernelSnitch.Finally, we discuss mitigations for our hardware-agnostic attacks.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.14722/ndss.2025.240223
Citations
4
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
9.92

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article

KernelSnitch: Side Channel-Attacks on Kernel Data Structures

Stefan Mangard, Daniel Gruss, Jonas Juffinger, Lukas Maar et al.
4 citations
Adversarial Robustness in Machine Learning
9.92
article

KernelSnitch: Side Channel-Attacks on Kernel Data Structures

Stefan Mangard, Daniel Gruss, Jonas Juffinger, Lukas Maar, Thomas Steinbauer
article en
4 citations

Abstract

The sharing of hardware elements, such as caches, is known to introduce microarchitectural side-channel leakage.One approach to eliminate this leakage is to not share hardware elements across security domains.However, even under the assumption of leakage-free hardware, it is unclear whether other critical system components, like the operating system, introduce software-caused side-channel leakage.In this paper, we present a novel generic software side-channel attack, KernelSnitch, targeting kernel data structures such as hash tables and trees.These structures are commonly used to store both kernel and user information, e.g., metadata for userspace locks.KernelSnitch exploits that these data structures are variable in size, ranging from an empty state to a theoretically arbitrary amount of elements.Accessing these structures requires a variable amount of time depending on the number of elements, i.e., the occupancy level.This variance constitutes a timing side channel, observable from user space by an unprivileged, isolated attacker.While the timing differences are very low compared to the syscall runtime, we demonstrate and evaluate methods to amplify these timing differences reliably.In three case studies, we show that KernelSnitch allows unprivileged and isolated attackers to leak sensitive information from the kernel and activities in other processes.First, we demonstrate covert channels with transmission rates up to 580 kbit/s.Second, we perform a kernel heap pointer leak in less than 65 s by exploiting the specific indexing that Linux is using in hash tables.Third, we demonstrate a website fingerprinting attack, achieving an F1 score of more than 89 %, showing that activity in other user programs can be observed using KernelSnitch.Finally, we discuss mitigations for our hardware-agnostic attacks.

Graz University of Technology (AT)
Austrian Science Fund, Österreichische Forschungsförderungsgesellschaft
Openalex Percentile: Top 2%
Adversarial Robustness in Machine Learning
9.92
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