{"title":"Pinpointing Vulnerabilities","authors":"Yueh-Ting Chen, M. Khandaker, Zhi Wang","doi":"10.1145/3052973.3053033","DOIUrl":null,"url":null,"abstract":"Memory-based vulnerabilities are a major source of attack vectors. They allow attackers to gain unauthorized access to computers and their data. Previous research has made significant progress in detecting attacks. However, developers still need to locate and fix these vulnerabilities, a mostly manual and time-consuming process. They face a number of challenges. Particularly, the manifestation of an attack does not always coincide with the exploited vulnerabilities, and many attacks are hard to reproduce in the lab environment, leaving developers with limited information to locate them. In this paper, we propose Ravel, an architectural approach to pinpoint vulnerabilities from attacks. Ravel consists of an online attack detector and an offline vulnerability locator linked by a record & replay mechanism. Specifically, Ravel records the execution of a production system and simultaneously monitors it for attacks. If an attack is detected, the execution is replayed to reveal the targeted vulnerabilities by analyzing the program's memory access patterns under attack. We have built a prototype of Ravel based on the open-source FreeBSD operating system. The evaluation results in security and performance demonstrate that Ravel can effectively pinpoint various types of memory vulnerabilities and has low performance overhead.","PeriodicalId":20540,"journal":{"name":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","volume":"10 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2017-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3052973.3053033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 23
Abstract
Memory-based vulnerabilities are a major source of attack vectors. They allow attackers to gain unauthorized access to computers and their data. Previous research has made significant progress in detecting attacks. However, developers still need to locate and fix these vulnerabilities, a mostly manual and time-consuming process. They face a number of challenges. Particularly, the manifestation of an attack does not always coincide with the exploited vulnerabilities, and many attacks are hard to reproduce in the lab environment, leaving developers with limited information to locate them. In this paper, we propose Ravel, an architectural approach to pinpoint vulnerabilities from attacks. Ravel consists of an online attack detector and an offline vulnerability locator linked by a record & replay mechanism. Specifically, Ravel records the execution of a production system and simultaneously monitors it for attacks. If an attack is detected, the execution is replayed to reveal the targeted vulnerabilities by analyzing the program's memory access patterns under attack. We have built a prototype of Ravel based on the open-source FreeBSD operating system. The evaluation results in security and performance demonstrate that Ravel can effectively pinpoint various types of memory vulnerabilities and has low performance overhead.