CASFinder: Detecting Common Attack Surface

Published

June 11, 2019

Author(s)

Meng Zhang, Yue Xin, Lingyu Wang, Sushil Jajodia, Anoop Singhal

Abstract

Code reusing is a common practice in software development due to its various benefits. Such a practice, however, may also cause large scale security issues since one vulnerability may appear in many different software due to cloned code fragments. The well known concept of relying on software diversity for security may also be compromised since seemingly different software may in fact share vulnerable code fragments. Although there exist efforts on detecting cloned code fragments, there lack solutions for formally characterizing their specific impact on security. In this paper, we revisit the concept of software diversity from a security viewpoint. Specifically, we define the novel concept of common attack surface to model the relative degree to which a pair of software may be sharing potentially vulnerable code fragments. To implement the concept, we develop an automated tool, CASFinder, in order to efficiently identify common attack surface between any given pair of software with minimum human intervention. Finally, we conduct experiments by applying our tool to real world open source software applications. Our results demonstrate many seemingly unrelated software applications indeed share significant common attack surface.

Proceedings Title

Data and Applications Security and Privacy XXXIII

Volume

11559

Conference Dates

July 15-18, 2019

Conference Location

Charleston, SC, US

Conference Title

33rd Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy (DBSec'19)

Pub Type

Conferences

Download Paper

https://doi.org/10.1007/978-3-030-22479-0_18

Local Download

Keywords

Mission Impact, Active Cyber Defense, Cloud Computing, Attack Graphs

Citation

Zhang, M. , Xin, Y. , Wang, L. , Jajodia, S. and Singhal, A. (2019), CASFinder: Detecting Common Attack Surface, Data and Applications Security and Privacy XXXIII , Charleston, SC, US, [online], https://doi.org/10.1007/978-3-030-22479-0_18, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=927918 (Accessed April 30, 2024)