It Takes Two: Option-Aware Directed Greybox Fuzzing for Vulnerability PoC Generation

Static analysis tools can identify potential vulnerabilities, but they often fall short in providing concrete proofs-of-concept (PoCs) to validate their findings. Directed greybox fuzzing (DGF) has emerged as a promising solution by systematically guiding execution toward suspicious code locations and generating reproducible PoCs that can trigger the target vulnerabilities. However, DGF tools often overlook the influence of configurable options on reaching target locations. Besides, option-aware greybox fuzzing (GF) tools suffer from ineffective option extraction to target locations, and inefficient coordination between options and file fuzzing.

To address these limitations, we present CoupleFuzz, a novel option-aware DGF tool that redefines PoC inputs as the combination of option input (OI) and file input (FI). CoupleFuzz adopts a two-phase workflow. The static analysis phase extracts option knowledge for guiding the fuzzing. The option-aware fuzzing phase employs taint analysis to dynamically prioritize effective option combinations and file bytes to target locations, and introduces a novel cross-guided fuzzing strategy that coordinates OI and FI fuzzing modules and enables each module to adapt to and benefit from its counterpart's advances, iteratively driving execution toward the target locations efficiently. Our evaluation has demonstrated that CoupleFuzz significantly outperforms the state-of-the-art DGF tools in generating PoCs for 22 real-world vulnerabilities, generating 15 (a 3.1× improvement) more PoCs than the best traditional DGF baseline and achieves an average speedup of 5.6× to reach target locations, with 6 0-day vulnerabilities confirmed by developers and 1 CVE identifier assigned.