Chenyang Lyu (Zhejiang University), Shouling Ji (Zhejiang University), Xuhong Zhang (Zhejiang University & Zhejiang University NGICS Platform), Hong Liang (Zhejiang University), Binbin Zhao (Georgia Institute of Technology), Kangjie Lu (University of Minnesota), Raheem Beyah (Georgia Institute of Technology)

Mutation-based fuzzing is one of the most popular approaches to discover vulnerabilities in a program. To alleviate the inefficiency of mutation-based fuzzing incurred by high randomness in the mutation process, multiple solutions are developed in recent years, especially coverage-based fuzzing. They mainly employ adaptive mutation strategies or integrate constraint-solving techniques to make a good exploration of the test cases which trigger unique paths and crashes. However, they lack a fine-grained reusing of fuzzing history to construct these interesting test cases, i.e., they largely fail to properly utilize fuzzing history across different fuzzing trials. In fact, we discover that test cases in fuzzing history contain rich knowledge of the key mutation strategies that lead to the discovery of unique paths and crashes. Specifically, partial path constraint solutions implicitly carried in these mutation strategies can be reused to accelerate the discovery of new paths and crashes that share similar partial path constraints.

Therefore, we first propose a lightweight and efficient Probabilistic Byte Orientation Model (PBOM) that properly captures the byte-level mutation strategies from intra- and inter-trial history and thus can effectively trigger unique paths and crashes. We then present a novel history-driven mutation framework named EMS that employs PBOM as one of the mutation operators to probabilistically provide desired mutation byte values according to the input ones. We evaluate EMS against state-of-the-art fuzzers including AFL, QSYM, MOPT, MOPT-dict, EcoFuzz, and AFL++ on 9 real world programs. The results show that EMS discovers up to 4.91X more unique vulnerabilities than the baseline, and finds more line coverage than other fuzzers on most programs. We report all of the discovered new vulnerabilities to vendors and will open source the prototype of EMS on GitHub.

View More Papers

An In-Depth Analysis on Adoption of Attack Mitigations in...

Ruotong Yu (Stevens Institute of Technology, University of Utah), Yuchen Zhang, Shan Huang (Stevens Institute of Technology)

Read More

Demo #14: In-Vehicle Communication Using Named Data Networking

Zachariah Threet (Tennessee Tech), Christos Papadopoulos (University of Memphis), Proyash Poddar (Florida International University), Alex Afanasyev (Florida International University), William Lambert (Tennessee Tech), Haley Burnell (Tennessee Tech), Sheikh Ghafoor (Tennessee Tech) and Susmit Shannigrahi (Tennessee Tech)

Read More

datAFLow: Towards a Data-Flow-Guided Fuzzer

Adrian Herrera (Australian National University), Mathias Payer (EPFL), Antony Hosking (Australian National University)

Read More

PHYjacking: Physical Input Hijacking for Zero-Permission Authorization Attacks on...

Xianbo Wang (The Chinese University of Hong Kong), Shangcheng Shi (The Chinese University of Hong Kong), Yikang Chen (The Chinese University of Hong Kong), Wing Cheong Lau (The Chinese University of Hong Kong)

Read More