  Mutation-based fuzzing has become one of the most common vulnerability
discovery solutions over the last decade. Fuzzing can be optimized when
targeting specific programs, and given that, some studies have employed online
optimization methods to do it automatically, i.e., tuning fuzzers for any given
program in a program-agnostic manner. However, previous studies have neither
fully explored mutation schemes suitable for online optimization methods, nor
online optimization methods suitable for mutation schemes. In this study, we
propose an optimization framework called SLOPT that encompasses both a
bandit-friendly mutation scheme and mutation-scheme-friendly bandit algorithms.
The advantage of SLOPT is that it can generally be incorporated into existing
fuzzers, such as AFL and Honggfuzz. As a proof of concept, we implemented
SLOPT-AFL++ by integrating SLOPT into AFL++ and showed that the
program-agnostic optimization delivered by SLOPT enabled SLOPT-AFL++ to achieve
higher code coverage than AFL++ in all of ten real-world FuzzBench programs.
Moreover, we ran SLOPT-AFL++ against several real-world programs from OSS-Fuzz
and successfully identified three previously unknown vulnerabilities, even
though these programs have been fuzzed by AFL++ for a considerable number of
CPU days on OSS-Fuzz.
