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Grey-box fuzz testing has revealed thousands of vulnerabilities in real-worldsoftware owing to its lightweight instrumentation, fast coverage feedback, anddynamic adjusting strategies. However, directly applying grey-box fuzzing toinput-dependent multithreaded programs can be extremely inefficient. Inpractice, multithreading-relevant bugs are usually buried in sophisticatedprogram flows. Meanwhile, the existing grey-box fuzzing techniques do notstress thread-interleavings which affect execution states in multithreadedprograms. Therefore, mainstream grey-box fuzzers cannot effectively testproblematic segments in multithreaded programs despite they might obtain highcode coverage statistics.  To this end, we propose MUZZ, a new grey-box fuzzing technique that hunts forbugs in multithreaded programs. MUZZ owns three novel thread-awareinstrumentations, namely coverage-oriented instrumentation, thread-contextinstrumentation, and schedule-intervention instrumentation. During fuzzing,these instrumentations engender runtime feedback to stress execution statescaused by thread interleavings. By leveraging the feedback in the dynamic seedselection and execution strategies, MUZZ preserves more valuable seeds thatexpose bugs in a multithreading context.  We evaluate MUZZ on 12 real-world software programs. Experiments show thatMUZZ outperforms AFL in both multithreading-relevant seed generation andconcurrency-vulnerability detection. Further, by replaying the target programsagainst the generated seeds, MUZZ also reveals more concurrency-bugs (e.g.,data-races, thread-leaks) than AFL. In total, MUZZ detected 8 newconcurrency-vulnerabilities and 19 new concurrency-bugs. At the time ofwriting, 4 CVE IDs have been assigned to the reported issues.

MUZZ: Thread-aware Grey-box Fuzzing for Effective Bug Hunting in Multithreaded Programs