Fuzz Smarter, Not Harder: Towards Greener Fuzzing with GreenAFL
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909876559544320 |
|---|---|
| author | Ercevik, Ayse Irmak Dakhama, Aidan Navaratnarajah, Melane Cao, Yazhuo Fernandes, Leo |
| author_facet | Ercevik, Ayse Irmak Dakhama, Aidan Navaratnarajah, Melane Cao, Yazhuo Fernandes, Leo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25665 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fuzz Smarter, Not Harder: Towards Greener Fuzzing with GreenAFL Ercevik, Ayse Irmak Dakhama, Aidan Navaratnarajah, Melane Cao, Yazhuo Fernandes, Leo Software Engineering 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. |
| title | Fuzz Smarter, Not Harder: Towards Greener Fuzzing with GreenAFL |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2510.25665 |