Shisong Qin (Tsinghua University), Fan Hu (State Key Laboratory of Mathematical Engineering and Advanced Computing), Bodong Zhao (Tsinghua University), Tingting Yin (Tsinghua University), Chao Zhang (Tsinghua University)

As the essential component responsible for communication, network services are security-critical, and it is vital to find vulnerabilities in them. Fuzzing is currently one of the most popular software vulnerability discovery techniques, widely adopted due to its high efficiency and low false positives. However, existing coverage-guided fuzzers mainly aim at stateless local applications, leaving stateful network services underexplored. Recently, some fuzzers targeting network services have been proposed but have certain limitations, e.g., insufficient or inaccurate state representation and low testing efficiency.

In this paper, we propose a new fuzzing solution NSFuzz for stateful network services. Specifically, we studied typical implementations of network service programs and figured out how they represent states and interact with clients, and accordingly propose (1) a program variable-based state representation scheme and (2) an efficient interaction synchronization mechanism to improve efficiency. We have implemented a prototype of NSFuzz, which uses static analysis to identify network event loops and extract state variables, then achieves fast I/O synchronization and efficient s t ate-aware fuzzing via lightweight compile-time instrumentation. The preliminary evaluation results show that, compared with state-of-the-art network service fuzzers AFLNET and STATEAFL, our solution NSFuzz could infer a more accurate state model during fuzzing and improve the testing throughput by up to 50x and the coverage by up to 20%.

View More Papers

Dissecting American Fuzzy Lop – A FuzzBench Evaluation

Andrea Fioraldi (EURECOM), Alessandro Mantovani (EURECOM), Dominik Maier (TU Berlin), Davide Balzarotti (EURECOM)

Read More

Packet-Level Open-World App Fingerprinting on Wireless Traffic

Jianfeng Li (The Hong Kong Polytechnic University), Shuohan Wu (The Hong Kong Polytechnic University), Hao Zhou (The Hong Kong Polytechnic University), Xiapu Luo (The Hong Kong Polytechnic University), Ting Wang (Penn State), Yangyang Liu (The Hong Kong Polytechnic University), Xiaobo Ma (Xi'an Jiaotong University)

Read More

Demo #1: Security of Multi-Sensor Fusion based Perception in...

Yulong Cao (University of Michigan), Ningfei Wang (UC, Irvine), Chaowei Xiao (Arizona State University), Dawei Yang (University of Michigan), Jin Fang (Baidu Research), Ruigang Yang (University of Michigan), Qi Alfred Chen (UC, Irvine), Mingyan Liu (University of Michigan) and Bo Li (University of Illinois at Urbana-Champaign)

Read More