SlowFuzz: Automated Domain-Independent Detection of Algorithmic Complexity Vulnerabilities
This addresses a critical security issue for software developers and system operators by providing a tool to prevent attacks, though it is an incremental improvement over existing domain-specific methods.
The paper tackled the problem of detecting algorithmic complexity vulnerabilities, which can cause denial-of-service attacks, by introducing SlowFuzz, a domain-independent framework that automatically finds inputs triggering worst-case behavior, achieving automated detection without domain-specific manual effort.
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.