HotFuzz: Discovering Algorithmic Denial-of-Service Vulnerabilities Through Guided Micro-Fuzzing William Blairy, Andrea Mambretti∗, Sajjad Arshadz, Michael Weissbacher∗, William Robertson∗, Engin Kirda∗, and Manuel Egeley yBoston University ∗zNortheastern University yfwdblair,
[email protected], ∗fmbr, mw, wkr,
[email protected],
[email protected] Abstract—Fifteen billion devices run Java and many of them are being released to unauthorized principals (confidentiality vio- connected to the Internet. As this ecosystem continues to grow, lations). The third traditional security property, availability, is it remains an important task to discover any unknown security not exempt from this issue. However, denial-of-service (DoS) threats these devices face. Fuzz testing repeatedly runs software as a vulnerability class tends to be viewed as simplistic, noisy, on random inputs in order to trigger unexpected program and easy (in principle) to defend against. behaviors, such as crashes or timeouts, and has historically re- vealed serious security vulnerabilities. Contemporary fuzz testing This view, however, is simplistic, as availability vulnerabilities techniques focus on identifying memory corruption vulnerabilities and exploits against them can take sophisticated forms. Algo- that allow adversaries to achieve either remote code execution or rithmic Complexity (AC) vulnerabilities are one such form, information disclosure. Meanwhile, Algorithmic Complexity (AC) where a small adversarial input induces worst-case1 behavior vulnerabilities, which are a common attack vector for denial-of- in the processing of that input, resulting in a denial of service. service attacks, remain an understudied threat. While in the textbook example against a hash table an adver- In this paper, we present HotFuzz, a framework for automatically sary inserts values with colliding keys to degrade the complex- discovering AC vulnerabilities in Java libraries.