Peeling Off the Cocoon: Unveiling Suppressed Golden Seeds for Mutational Greybox Fuzzing

2026-02-27

AI Summary

Existing coverage-based seed selection methods struggle to identify high-value seeds suppressed by obstructive conditional statements, limiting the depth and efficiency of greybox fuzzing. This work proposes a progressive debloating mechanism that dynamically identifies and eliminates path-hindering conditions through program analysis and dynamic instrumentation, thereby uncovering previously masked high-quality seeds. Integrated into a coverage-guided greybox fuzzing framework, the approach significantly enhances seed quality and exploration depth, consistently discovering more unique execution paths and potential vulnerabilities across multiple real-world programs.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

PoCo is a technique that aims to enhance modern coverage-based seed selection (CSS) techniques (such as afl-cmin) by gradually removing obstacle conditional statements and conducting deeper seed selection.

Problem

• mutational greybox fuzzing
• coverage-based seed selection
• golden seeds
• conditional statements
• fuzzing efficiency

Innovation

• mutational greybox fuzzing
• coverage-based seed selection
• conditional statement removal
• golden seeds
• PoCo