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首页> 外文会议>Asia-Pacific Software Engineering Conference >Exniffer: Learning to Prioritize Crashes by Assessing the Exploitability from Memory Dump
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Exniffer: Learning to Prioritize Crashes by Assessing the Exploitability from Memory Dump

机译：Exniffer：通过评估内存转储的可利用性来学习确定崩溃的优先级

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摘要

An important component of software reliability is the assurance of certain security guarantees, such as absence of low-level bugs that may result in code exploitation, for example. A program crash is an early indicator of possible errors in the program like memory corruption, access violation or division by zero. In particular, a crash may indicate the presence of safety or security critical errors. A safety-error crash does not result in any exploitable condition, whereas a security-error crash allows an attacker to exploit a vulnerability. However, distinguishing one from the other is a non-trivial task. This exacerbates the problem in cases where we get hundreds of crashes and programmers have to make choices which crash to patch first! In this work, we present a technique to identify security critical crashes by applying machine learning on a set of features derived from core-dump files and runtime information obtained from hardware assisted monitoring such as the last branch record (LBR) register. We implement the proposed technique in a prototype called Exniffer. Our empirical results, obtained by experimenting Exniffer on several crashes on real-world applications show that proposed technique is able to classify a given crash as exploitable or not-exploitable with high accuracy.
机译：软件可靠性的重要组成部分是某些安全保证的保证，例如，缺少可能导致代码利用的低级错误。程序崩溃是程序中可能出现错误的早期指示，例如内存损坏，访问冲突或被零除。特别是，崩溃可能表明存在安全性或安全性严重错误。安全错误崩溃不会导致任何可利用的状况，而安全错误崩溃则使攻击者能够利用漏洞。但是，将一个与另一个区分开是一项艰巨的任务。如果我们遇到数百起崩溃，而程序员不得不选择首先要修复的崩溃，这会加剧该问题！在这项工作中，我们提出了一种技术，该技术通过将机器学习应用于从核心转储文件衍生的一组功能以及从硬件辅助监视（例如最后分支记录（LBR）寄存器）获得的运行时信息来识别安全关键崩溃。我们在名为Exniffer的原型中实现了建议的技术。通过对Exniffer在现实应用中的几次崩溃进行实验获得的经验结果表明，所提出的技术能够将给定的崩溃以高准确度分类为可利用或不可利用的崩溃。

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