SlowFuzz: Automated Domain-Independent Detection of Algorithmic Complexity Vulnerabilities

TL;DR

SlowFuzz is a domain-independent framework that automatically detects algorithmic complexity vulnerabilities by using resource-guided evolutionary search to find inputs causing worst-case behavior, aiding in preventing DoS attacks.

Contribution

It introduces SlowFuzz, the first domain-independent tool that automates detection of algorithmic complexity vulnerabilities using resource-guided evolutionary search.

Findings

Successfully identified complexity vulnerabilities in various applications

Reduced manual effort in vulnerability detection

Demonstrated effectiveness in real-world scenarios

Abstract

Algorithmic complexity vulnerabilities occur when the worst-case time/space complexity of an application is significantly higher than the respective average case for particular user-controlled inputs. When such conditions are met, an attacker can launch Denial-of-Service attacks against a vulnerable application by providing inputs that trigger the worst-case behavior. Such attacks have been known to have serious effects on production systems, take down entire websites, or lead to bypasses of Web Application Firewalls. Unfortunately, existing detection mechanisms for algorithmic complexity vulnerabilities are domain-specific and often require significant manual effort. In this paper, we design, implement, and evaluate SlowFuzz, a domain-independent framework for automatically finding algorithmic complexity vulnerabilities. SlowFuzz automatically finds inputs that trigger worst-case algorithmic behavior in the tested binary. SlowFuzz uses resource-usage-guided evolutionary search techniques to automatically find inputs that maximize computational resource utilization for a given application.