SHARD: Fine-Grained Kernel Specialization with Context-Aware Hardening


Muhammad Abubakar, Adil Ahmad, Pedro Fonseca, and Dongyan Xu, Purdue University


With growing hardware complexity and ever-evolving user requirements, the kernel is increasingly bloated which increases its attack surface. Despite its large size, for specific applications and workloads, only a small subset of the kernel code is actually required. Kernel specialization approaches exploit this observation to either harden the kernel or restrict access to its code (debloating) on a per-application basis. However, existing approaches suffer from coarse specialization granularity and lack strict enforcement which limits their effectiveness.

This paper presents SHARD, a practical framework to enforce fine-grain kernel specialization. SHARD specializes at both the application and system call levels to significantly restrict the kernel code exposed to attackers. Furthermore, SHARD introduces context-aware hardening to dynamically enable code hardening during suspicious execution contexts. SHARD implements an instance of a context-aware hardening scheme using control-flow integrity (CFI), which provides near-native performance for non-hardened executions and strong security guarantees. Our analysis of the kernel attack surface reduction with SHARD as well as concrete attacks shows that SHARD exposes 181× less kernel code than the native kernel, an order of magnitude better than existing work, and prevents 90% of the evaluated attacks. Our evaluation shows that the average performance overhead ofSHARD on real-world applications is moderate — 10% to 36% on NGINX, 3% to 10% on Redis, and 0% to 2.7% on the SPEC CPU 2006 benchmarks.

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@inproceedings {263878,
title = {{SHARD}: Fine-Grained Kernel Specialization with Context-Aware Hardening},
booktitle = {30th {USENIX} Security Symposium ({USENIX} Security 21)},
year = {2021},
address = {Vancouver, B.C.},
url = {},
publisher = {{USENIX} Association},
month = aug,