Fuzz Smarter, Not Harder: Towards Greener Fuzzing with GreenAFL

2025-10-29

AI Summary

Existing grey-box fuzzers (e.g., AFL++) prioritize code coverage while ignoring energy consumption incurred during path exploration, leading to excessive computational resource usage and substantial carbon footprint. This work proposes GreenAFL—the first framework to explicitly integrate power consumption modeling into grey-box fuzzing heuristics, optimizing energy efficiency without compromising coverage. Its core contributions are: (1) energy-aware corpus minimization, (2) energy-guided mutation scheduling, and (3) hardware-monitored real-time power modeling. Experimental evaluation demonstrates that enabling each component individually reduces system energy consumption by 18.7%–32.4% while preserving high coverage. GreenAFL establishes a practical, green paradigm for software testing, bridging energy-awareness with robust fuzzing performance.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

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.

Problem

• Reducing computational resource consumption in continuous fuzzing campaigns
• Incorporating energy awareness into fuzzing heuristics for sustainability
• Maintaining testing coverage while minimizing environmental impact

Innovation

• Energy-aware framework incorporating power consumption into fuzzing
• Energy-aware corpus minimisation considering power consumption
• Energy-guided heuristics directing mutation to low-energy inputs