NDSS

HotFuzz: Discovering Algorithmic Denial-of-Service Vulnerabilities Through Guided Micro-Fuzzing

William Blair (Boston University), Andrea Mambretti (Northeastern University), Sajjad Arshad (Northeastern University), Michael Weissbacher (Northeastern University), William Robertson (Northeastern University), Engin Kirda (Northeastern University), Manuel Egele (Boston University)

Fifteen billion devices run Java and many of them are connected to the Internet. As this ecosystem continues to grow, it remains an important task to discover the unknown security threats these devices face. Fuzz testing repeatedly runs software on random inputs in order to trigger unexpected program behaviors, such as crashes or timeouts, and has historically revealed serious security vulnerabilities. Contemporary fuzz testing techniques focus on identifying memory corruption vulnerabilities that allow adversaries to achieve remote code execution. Meanwhile, algorithmic complexity (AC) vulnerabilities, which are a common attack vector for denial-of-service attacks, remain an understudied threat.

In this paper, we present HotFuzz, a framework for automatically discovering AC vulnerabilities in Java libraries. HotFuzz uses micro-fuzzing, a genetic algorithm that evolves arbitrary Java objects in order to trigger the worst-case performance for a method under test. We define Small Recursive Instantiation (SRI) which provides seed inputs to micro-fuzzing represented as Java objects. After micro-fuzzing, HotFuzz synthesizes test cases that triggered AC vulnerabilities into Java programs and monitors their execution in order to reproduce vulnerabilities outside the analysis framework. HotFuzz outputs those programs that exhibit high CPU utilization as witnesses for AC vulnerabilities in a Java library.

We evaluate HotFuzz over the Java Runtime Environment (JRE), the 100 most popular Java libraries on Maven, and challenges contained in the DARPA Space and Time Analysis for Cyber-Security (STAC) program. We compare the effectiveness of using seed inputs derived using SRI against using empty values. In this evaluation, we verified known AC vulnerabilities, discovered previously unknown AC vulnerabilities that we responsibly reported to vendors, and received confirmation from both IBM and Oracle. Our results demonstrate micro-fuzzing finds AC vulnerabilities in real-world software, and that micro-fuzzing with SRI derived seed inputs complements using empty seed inputs.