Year: 2018
Type: preprint
Abstract: Greybox fuzzing is a lightweight testing approach that effectively detects bugs and security vulnerabilities. However, greybox fuzzers randomly mutate program inputs to exercise new paths; this makes it challenging to cover code that is guarded by complex checks. In this paper, we present a technique that extends greybox fuzzing with a method for learning new inputs based on already explored program executions. These inputs can be learned such that they guide exploration toward specific executions, for instance, ones that increase path coverage or reveal vulnerabilities. We have evaluated our technique and compared it to traditional greybox fuzzing on 26 real-world benchmarks. In comparison, our technique significantly increases path coverage (by up to 3X) and detects more bugs (up to 38% more), often orders-of-magnitude faster.
Source: arXiv (Cornell University)
Authors: Wüstholz, Valentin, Christakis, Maria
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2884619780