标准操作系统抽象的信息流控制

Max Krohn, A. Yip, Micah Z. Brodsky, Natan Cliffer, F. Kaashoek, E. Kohler, R. Morris
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引用次数: 503

摘要

分散信息流控制(DIFC)是一种安全方法,它允许应用程序编写者控制数据如何在应用程序的各个部分和外部世界之间流动。当应用于隐私时,DIFC允许不受信任的软件使用私有数据进行计算,而受信任的安全代码控制该数据的发布。当应用于完整性时,DIFC允许受信任的代码保护不受信任的软件免受意外的恶意输入。在任何一种情况下,只有受信任代码中的错误(往往是小而孤立的)才会导致安全违规。我们提出了Flume,一个新的DIFC模型,它适用于操作系统进程和标准操作系统抽象(例如,管道和文件描述符)的粒度。Flume的设计是为了简化机制,简化DIFC在现有应用程序中的使用,并允许传统过程和DIFC感知过程之间的安全交互。Flume作为用户级参考监视器在linux上运行。受Flume限制的进程不能直接执行大多数系统调用;取而代之的是,一个介入层用引用监视器的ipc代替系统调用,它执行数据流策略并代表进程执行安全操作。我们将一个复杂的web应用程序(MoinMoin Wiki)移植到Flume,只修改了2%的原始代码。性能测量显示,读工作负载降低了43%,写工作负载降低了34%,这主要是由于Flume的用户级实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Information flow control for standard OS abstractions
Decentralized Information Flow Control (DIFC) is an approach to security that allows application writers to control how data flows between the pieces of an application and the outside world. As applied to privacy, DIFC allows untrusted software to compute with private data while trusted security code controls the release of that data. As applied to integrity, DIFC allows trusted code to protect untrusted software from unexpected malicious inputs. In either case, only bugs in the trusted code, which tends to be small and isolated, can lead to security violations. We present Flume, a new DIFC model that applies at the granularity of operating system processes and standard OS abstractions (e.g., pipes and file descriptors). Flume was designed for simplicity of mechanism, to ease DIFC's use in existing applications, and to allow safe interaction between conventional and DIFC-aware processes. Flume runs as a user-level reference monitor onLinux. A process confined by Flume cannot perform most system calls directly; instead, an interposition layer replaces system calls with IPCto the reference monitor, which enforces data flowpolicies and performs safe operations on the process's behalf. We ported a complex web application (MoinMoin Wiki) to Flume, changingonly 2% of the original code. Performance measurements show a 43% slowdown on read workloadsand a 34% slowdown on write workloads, which aremostly due to Flume's user-level implementation.
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