Fuzz Smarter, Not Harder: Towards Greener Fuzzing with GreenAFL

TL;DR

GreenAFL is an energy-aware fuzzing framework that reduces environmental impact by incorporating power consumption into fuzzing heuristics, achieving high coverage with lower energy use.

Contribution

This paper introduces GreenAFL, the first fuzzing approach that integrates energy consumption metrics into its core heuristics to promote greener software testing.

Findings

GreenAFL reduces energy consumption compared to traditional fuzzers.

Energy-aware heuristics maintain high coverage levels.

Combining energy-based corpus minimisation and heuristics yields optimal results.

Abstract

Fuzzing has become a key search-based technique for software testing, but continuous fuzzing campaigns consume substantial computational resources and generate significant carbon footprints. Existing grey-box fuzzing approaches like AFL++ focus primarily on coverage maximisation, without considering the energy costs of exploring different execution paths. This paper presents GreenAFL, an energy-aware framework that incorporates power consumption into the fuzzing heuristics to reduce the environmental impact of automated testing whilst maintaining coverage. GreenAFL introduces two key modifications to traditional fuzzing workflows: energy-aware corpus minimisation considering power consumption when reducing initial corpora, and energy-guided heuristics that direct mutation towards high-coverage, low-energy inputs. We conduct an ablation study comparing vanilla AFL++, energy-based corpus minimisation, and energy-based heuristics to evaluate the individual contributions of each component. Results show that highest coverage, and lowest energy usage is achieved whenever at least one of our modifications is used.