FuZZan: Efficient sanitizer metadata design for fuzzing

Abstract

Fuzzing is one of the most popular and effective techniques for finding software bugs. To detect triggered bugs, fuzzers leverage a variety of sanitizers in practice. Unfortunately, sanitizers target long running experiments-e.g., developer test suites-not fuzzing, where execution time is highly variable ranging from extremely short to long. Design decisions made for developer test suites introduce high overhead on short lived fuzzing executions, decreasing the fuzzer's throughput and thereby reducing effectiveness. The root cause of this sanitization overhead is the heavyweight metadata structure that is optimized for frequent metadata operations over long executions. To address this, we design new metadata structures for sanitizers, and propose FuZZan to automatically select the optimal metadata structure without any user configuration. Our new metadata structures have the same bug detection capabilities as the ones they replace. We implement and apply these ideas to Address Sanitizer (ASan), which is the most popular sanitizer. Our evaluation shows that on the Google fuzzer test suite, FuZZan improves fuzzing throughput over ASan by 48% starting with Google's provided seeds (52% when starting with empty seeds on the same applications). Due to this improved throughput, FuZZan discovers 13% more unique paths given the same 24 hours and finds bugs 42% faster. Furthermore, FuZZan catches all bugs ASan does; i.e., we have not traded precision for performance. Our findings show that sanitizer performance overhead is avoidable when metadata structures are designed for fuzzing, and that the performance difference will have a meaningful difference in squashing software bugs. Copyright © Proc. of the 2020 USENIX Annual Technical Conference, ATC 2020. All rights reserved.