通过布局随机化的本地内存

R. Jagadeesan, Corin Pitcher, J. Rathke, J. Riely
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引用次数: 61

摘要

在计算机安全中,随机化是一种将不可预测性引入软件基础结构的工具。在本文中,我们研究了使用随机化来实现局部内存的保密性和完整性保证。我们遵循阿巴迪和普劳金提出的方法。我们考虑在两种环境中执行一种理想语言。在严格环境下,对手不能访问用户程序的局部变量。在松散的环境中,对手可能会尝试猜测分配的内存位置,从而以很小的概率访问用户程序的本地内存。我们使用两个新颖的演算:lambda-mu-hashref和lambda-mu-proberef来模拟这些环境。我们对Abadi-Plot kin程序的贡献是通过动态内存分配、一级和高阶引用以及调用/cc风格控制来丰富编程语言。一方面,这些增强允许我们直接对更大的系统强化原则类进行建模。另一方面,由于我们丰富的语言允许对改变程序控制流的攻击进行自然和直接的编码,因此对手的类别也得到了增强。我们的主要技术成果是将lambda-mu-hashref完全抽象地转换为lambda-mu-proberef。因此,在随机布局的情况下,对手无法从猜测用户程序的局部引用中获得新的力量。我们的数值界限与Abadi和Plot kin的相似,因此,额外的编程语言特征不会导致通过随机化保护所需资源的增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Local Memory via Layout Randomization
Randomization is used in computer security as a tool to introduce unpredictability into the software infrastructure. In this paper, we study the use of randomization to achieve the secrecy and integrity guarantees for local memory. We follow the approach set out by Abadi and Plot kin. We consider the execution of an idealized language in two environments. In the strict environment, opponents cannot access local variables of the user program. In the lax environment, opponents may attempt to guess allocated memory locations and thus, with small probability, gain access the local memory of the user program. We model these environments using two novel calculi: lambda-mu-hashref and lambda-mu-proberef. Our contribution to the Abadi-Plot kin program is to enrich the programming language with dynamic memory allocation, first class and higher order references and call/cc-style control. On the one hand, these enhancements allow us to directly model a larger class of system hardening principles. On the other hand, the class of opponents is also enhanced since our enriched language permits natural and direct encoding of attacks that alter the control flow of programs. Our main technical result is a fully abstract translation (up to probability) of lambda-mu-hashref into lambda-mu-proberef. Thus, in the presence of randomized layouts, the opponent gains no new power from being able to guess local references of the user program. Our numerical bounds are similar to those of Abadi and Plot kin, thus, the extra programming language features do not cause a concomitant increase in the resources required for protection via randomization.
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