Angelic Checking within Static Driver Verifier: Towards high-precision defects without (modeling) cost

Shuvendu K. Lahiri, A. Lal, S. Gopinath, Alexander Nutz, V. Levin, Rahul Kumar, Nate Deisinger, J. Lichtenberg, Chetan Bansal
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引用次数: 3

Abstract

Microsoft's Static Driver Verifier (SDV) pioneered the use of software model checking for ensuring that device drivers correctly use operating system (OS) APIs. However, the verification methodology has been difficult to extend in order to support either (a) new classes of drivers for which SDV does not already have a harness and stubs, or (b) memory-corruption properties. Any attempt to apply SDV out-of-the-box results in either false alarms due to the lack of environment modeling, or scalability issues when finding deeply nested bugs in the presence of a very large number of memory accesses. In this paper, we describe our experience designing and shipping a new class of checks known as angelic checks through SDV with the aid of angelic verification (AV) [1] technology, over a period of 4 years. AV pairs a precise inter-procedural assertion checker with automatic inference of likely specifications for the environment. AV helps compensate for the lack of environment modeling and regains scalability by making it possible to find deeply nested bugs, even for complex memory-corruption properties. These new rules have together found over a hundred confirmed defects during internal deployment at Microsoft, including several previously unknown high-impact potential security vulnerabilities. AV considerably increases the reach of SDV, both in terms of drivers as well as rules that it can support effectively.
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静态驱动验证器内的天使检查:无(建模)成本的高精度缺陷
微软的静态驱动程序验证器(SDV)率先使用软件模型检查来确保设备驱动程序正确使用操作系统(OS) api。然而,验证方法很难扩展,以支持(a)新的驱动类,其中SDV尚未具有线束和存根,或(b)内存损坏属性。任何应用开箱即用的SDV的尝试都会导致由于缺乏环境建模而产生的错误警报,或者在发现存在大量内存访问的深度嵌套错误时出现可伸缩性问题。在本文中,我们描述了我们在4年的时间里,借助天使验证(AV)[1]技术,通过SDV设计和交付一种称为天使检查的新型支票的经验。AV将精确的过程间断言检查器与对环境可能规范的自动推断配对。AV有助于弥补环境建模的不足,并通过发现深度嵌套的错误(即使是复杂的内存损坏属性)来恢复可伸缩性。在微软内部部署期间,这些新规则一共发现了100多个已确认的缺陷,包括几个以前未知的高影响潜在安全漏洞。自动驾驶汽车大大增加了自动驾驶汽车的覆盖范围,无论是在驾驶员方面,还是在它可以有效支持的规则方面。
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