RPFUZZ: Efficient network service fuzzing via pruning redundant mutation

Coverage-guided fuzzing (CGF) has proven its outstanding performance on vulnerability detection. However, existing approaches exhibit limitations when handling network service. Restricted by network I/O duration and chronology, long packet sequences crafted by fuzzers incur a substantial execution cost. Test cases with such non-coverage-improving mutations (i.e. redundant mutation) can significantly reduce fuzzing throughput and compromise vulnerability discovery.

To address this issue, we propose RPFUZZ, a novel network fuzzing framework designed to systematically reduce redundant mutations: (1) We propose redundant mutation pruning for network service fuzzing. By early terminating redundant mutations’ execution, RPFUZZ can achieve higher throughput. (2) To detect redundant mutation, we propose redundant mutation oracle. This oracle dynamically judges whether a test case is redundant according to current code coverage and value of service-related variables (SRVs). (3)To identify SRVs, we propose an integrated approach combining dynamic call stack analysis with static value-flow graph (VFG) analysis.

To evaluate the performance of RPFUZZ, we implement a prototype on top of NYX-NET. We conduct thorough experiments on ProFuzzBench, a benchmark that consists of 12 real-world network services. The results indicate that RPFUZZ achieves over 185% improvement in throughput and 1.02% rise in code coverage compared with NYX-NET. Besides, RPFUZZ has successfully uncovered 1753 unique crashes across 6 network services, including an unreported vulnerability (assigned to CVE-2024-57392) in ProFTPD, which has been well tested.