Academic Commons


libdft: Practical Dynamic Data Flow Tracking for Commodity Systems

Kemerlis, Vasileios; Portokalidis, Georgios; Jee, Kangkook; Keromytis, Angelos D.

Dynamic data flow tracking (DFT) deals with the tagging and tracking of "interesting" data as they propagate during program execution. DFT has been repeatedly implemented by a variety of tools for numerous purposes, including protection from zero-day and cross-site scripting attacks, detection and prevention of information leaks, as well as for the analysis of legitimate and malicious software. We present libdft, a dynamic DFT framework that unlike previous work is at once fast, reusable, and works with commodity software and hardware. libdft provides an API, which can be used to painlessly deliver DFT-enabled tools that can be applied on unmodified binaries, running on common operating systems and hardware, thus facilitating research and rapid prototyping. We explore different approaches for implementing the low-level aspects of instruction-level data tracking, introduce a more efficient and 64-bit capable shadow memory, and identify (and avoid) the common pitfalls responsible for the excessive performance overhead of previous studies. We evaluate libdft using real applications with large codebases like the Apache and MySQL servers, and the Firefox web browser. We also use a series of benchmarks and utilities to compare libdft with similar systems. Our results indicate that it performs at least as fast, if not faster, than previous solutions, and to the best of our knowledge, we are the first to evaluate the performance overhead of a fast dynamic DFT implementation in such depth. Finally, our implementation is freely available as open source software.



More About This Work

Academic Units
Computer Science
Department of Computer Science, Columbia University
Columbia University Computer Science Technical Reports, CUCS-044-11
Published Here
October 28, 2011