Raise or Check? Predicting the Necessity of Additional Fuzzing for Continuous Fuzzing

Riku Kato, Tatsuya Shirai, Olivier Nourry, Yutaro Kashiwa, Kenji Fujiwara +2

Continuous fuzzing is used for vulnerability detection in Open Source Software (OSS). Large-scale services such as Google's OSS-Fuzz fuzz over 1,300 projects and have revealed more than 10,000 vulnerabilities. Despite this, a considerable time lag often exists between when a vulnerability is introduced and when it is detected (i.e., the window of exposure). Given the computational cost of additional fuzzing, we aim to allocate fuzzing resources efficiently using vulnerability-risk estimates. We propose RAISE (Risk-Aware Intensive Supplemental Execution Framework), a Just-In-Time (JIT) framework that prioritizes additional fuzzing for high-risk project-days. RAISE performs binary classification at the project-day granularity to estimate whether a vulnerability is introduced on a given day. We construct a dataset combining vulnerability-introducing commits from the OSV database with daily coverage and change metrics from OSS-Fuzz, covering 15 C/C++ projects and 15,210 project-days, of which 347 (2.28%) are vulnerability-introducing. In a cross-project validation setting, our best model, a Random Forest combining VCCFinder-based static features with dynamic fuzzing features,…