Towards Automated Application-Specific Software Stacks

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Abstract

Software complexity has increased over the years. One common way to tackle this complexity during development is to encapsulate features into a shared library. This allows developers to reuse already implemented features instead of reimplementing them over and over again. However, not all features provided by a shared library are actually used by an application. As a result, an application using shared libraries loads unused code into memory, which an attacker can use to perform code-reuse and similar types of attacks. The same holds for applications written in a scripting language such as PHP or Ruby: The interpreter typically offers much more functionality than is actually required by the application and hence provides a larger overall attack surface. In this paper, we tackle this problem and propose a first step towards automated application-specific software stacks. We present a compiler extension capable of removing unneeded code from shared libraries and—with the help of domain knowledge—also capable of removing unused functionalities from an interpreter’s code base during the compilation process. Our evaluation against a diverse set of real-world applications, among others Nginx, Lighttpd, and the PHP interpreter, removes on average 71.3% of the code in musl-libc, a popular libc implementation. The evaluation on web applications show that a tailored PHP interpreter can mitigate entire vulnerability classes, as is the case for OpenConf. We demonstrate the applicability of our debloating approach by creating an application-specific software stack for a Wordpress web application: we tailor the libc library to the Nginx web server and PHP interpreter, whereas the PHP interpreter is tailored to the Wordpress web application. In this real-world scenario, the code of the libc is decreased by 65.1% in total, thereby reducing the available code for code-reuse attacks.

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APA

Davidsson, N., Pawlowski, A., & Holz, T. (2019). Towards Automated Application-Specific Software Stacks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11736 LNCS, pp. 88–109). Springer. https://doi.org/10.1007/978-3-030-29962-0_5

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