Скачать или смотреть USENIX Security '14 - Optimizing Seed Selection for Fuzzing

Optimizing Seed Selection for Fuzzing

Alexandre Rebert, Carnegie Mellon University and ForAllSecure; Sang Kil Cha and Thanassis Avgerinos, Carnegie Mellon University; Jonathan Foote and David Warren, Software Engineering Institute CERT; Gustavo Grieco, Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas (CIFASIS) and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET); David Brumley, Carnegie Mellon University

Randomly mutating well-formed program inputs or simply fuzzing, is a highly effective and widely used strategy to find bugs in software. Other than showing fuzzers find bugs, there has been little systematic effort in understanding the science of how to fuzz properly. In this paper, we focus on how to mathematically formulate and reason about one critical aspect in fuzzing: how best to pick seed files to maximize the total number of bugs found during a fuzz campaign. We design and evaluate six different algorithms using over 650 CPU days on Amazon Elastic Compute Cloud (EC2) to provide ground truth data. Overall, we find 240 bugs in 8 applications and show that the choice of algorithm can greatly increase the number of bugs found. We also show that current seed selection strategies as found in Peach may fare no better than picking seeds at random. We make our data set and code publicly available.

View the full USENIX Security '14 program at https://www.usenix.org/conference/use...