KASLR-MT: kernel address space layout randomization for multi-tenant cloud systems

Abstract

[EN] Cloud computing has completely changed our lives. This technology dramatically impacted on how we play, work and live. It has been widely adopted in many sectors mainly because it reduces the cost of performing tasks in a flexible, scalable and reliable way. To provide a secure cloud computing architecture, the highest possible level of protection must be applied. Unfortunately, the cloud computing paradigm introduces new scenarios where security protection techniques are weakened or disabled to obtain a better performance and resources exploitation. Kernel ASLR (KASLR) is a widely adopted protection technique present in all modern operating systems. KASLR is a very effective technique that thwarts unknown attacks but unfortunately its randomness have a significant impact on memory deduplication savings. Both techniques are very desired by the industry, the first one because of the high level of security that it provides and the latter to obtain better performance and resources exploitation. In this paper, we propose KASLR-MT, a new Linux kernel randomization approach compatible with memory deduplication. We identify why the most widely and effective technique used to mitigate attacks at kernel level, KASLR, fails to provide protection and shareability at the same time. We analyze the current Linux kernel randomization and how it affects to the shared memory of each kernel region. Then, based on the analysis, we propose KASLR-MT, the first effective and practical Kernel ASLR memory protection that maximizes the memory deduplication savings rate while providing a strong security. Our tests reveal that KASLR-MT is not intrusive, very scalable and provides strong protection without sacrificing the shareability. (C) 2019 Elsevier Inc. All rights reserved.