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arXiv 2406.05403cs.CRcs.AR
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SemPat:利用基于超性质(Hyperproperty)的语义分析生成微架构攻击模式

SemPat: Using Hyperproperty-based Semantic Analysis to Generate Microarchitectural Attack Patterns

Adwait Godbole, Yatin A. Manerkar, Sanjit A. Seshia

AI总结:

本文提出SemPat技术,通过基于非干扰性超性质的语义分析自动生成微架构攻击模式,兼顾形式化验证的表达能力与模式匹配的可扩展性,并在SpectreV1、SpectreV4等案例中验证了其有效性。

摘要:

软件微架构安全验证领域已出现两大类方法。第一类基于语义安全性质(例如非干扰性),针对给定程序和指定的硬件微架构抽象模型进行验证。第二类基于攻击模式,若在程序执行中发现这些模式,则表明存在漏洞利用。前者使用形式化规范,能够捕获针对同一漏洞的多种gadget变体,但受限于验证的可扩展性。攻击模式虽然更具可扩展性,但目前必须人工构建,因为它们范围较窄且对gadget特定结构敏感。本文提出一种技术,在给定基于非干扰性的语义安全超性质的前提下,能够自动生成达到特定复杂度参数(称为骨架大小)的攻击模式。因此,我们结合了两种方法的优势:安全性可以通过一个超性质来指定,该超性质能够统一捕获多种gadget变体,同时自动生成的模式可用于可扩展验证。我们在一个工具中实现了该方法,并展示了生成新模式的能力(例如针对SpectreV1、SpectreV4),以及使用生成模式相比基于超性质的验证所获得的改进的可扩展性。

英文摘要:

Microarchitectural security verification of software has seen the emergence of two broad classes of approaches. The first is based on semantic security properties (e.g., non-interference) which are verified for a given program and a specified abstract model of the hardware microarchitecture. The second is based on attack patterns, which, if found in a program execution, indicates the presence of an exploit. While the former uses a formal specification that can capture several gadget variants targeting the same vulnerability, it is limited by the scalability of verification. Patterns, while more scalable, must be currently constructed manually, as they are narrower in scope and sensitive to gadget-specific structure. This work develops a technique that, given a non-interference-based semantic security hyperproperty, automatically generates attack patterns up to a certain complexity parameter (called the skeleton size). Thus, we combine the advantages of both approaches: security can be specified by a hyperproperty that uniformly captures several gadget variants, while automatically generated patterns can be used for scalable verification. We implement our approach in a tool and demonstrate the ability to generate new patterns, (e.g., for SpectreV1, SpectreV4) and improved scalability using the generated patterns over hyperproperty-based verification.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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