Biblio

Proactive security review and test efforts are a necessary component of the software development lifecycle. Since resource limitations often preclude reviewing, testing and fortifying the entire code base, prioritizing what code to review/test can improve a team's ability to find and remove more vulnerabilities that are reachable by an attacker. One way that professionals perform this prioritization is the identification of the attack surface of software systems. However, identifying the attack surface of a software system is non-trivial. The goal of this poster is to present the concept of a risk-based attack surface approximation based on crash dump stack traces for the prioritization of security code rework efforts. For this poster, we will present results from previous efforts in the attack surface approximation space, including studies on its effectiveness in approximating security relevant code for Windows and Firefox. We will also discuss future research directions for attack surface approximation, including discovery of additional metrics from stack traces and determining how many stack traces are required for a good approximation.

While software systems are being developed and released to consumers more rapidly than ever, security remains an important issue for developers. Shorter development cycles means less time for these critical security testing and review efforts. The attack surface of a system is the sum of all paths for untrusted data into and out of a system. Code that lies on the attack surface therefore contains code with actual exploitable vulnerabilities. However, identifying code that lies on the attack surface requires the same contested security resources from the secure testing efforts themselves. My research proposes an automated technique to approximate attack surfaces through the analysis of stack traces. We hypothesize that stack traces user crashes represent activity that puts the system under stress, and is therefore indicative of potential security vulnerabilities. The goal of this research is to aid software engineers in prioritizing security efforts by approximating the attack surface of a system via stack trace analysis. In a trial on Mozilla Firefox, the attack surface approximation selected 8.4% of files and contained 72.1% of known vulnerabilities. A similar trial was performed on the Windows 8 product.