Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing

Control Flow Graphs (CFGs) provide fundamental data for many program analyses, such as malware analysis, vulnerability detection, code similarity etc. Existing techniques constructing control flow graphs include static, dynamic, and hybrid which each having their own advantages disadvantages. However, due to the difficulty of resolving indirect jump relations, existing are limited in completeness. In this paper, we propose a practical technique that applies static analysis dynamic construct more complete graphs. The main innovation our approach is adopt directed gray-box fuzzing (DGF) instead coverage-based (CGF) used generate test cases can exercise jumps. We first employ CFGs without relations. Then, utilize resolve relations by monitoring execution traces these cases. Finally, combine with CFGs. addition, also an iterative feedback mechanism further improve completeness have implemented prototype evaluated it through comparing approaches on eight benchmarks. results show than approaches.