IJON: Exploring Deep State Spaces via Fuzzing
In this paper, we propose IJON, an annotation mechanism that a human analyst can use to guide the fuzzer. In contrast to the two aforementioned techniques, this approach allows a more systematic exploration of the program’s behavior based on the data representing the internal state of the program. As a consequence, using only a small (usually one line) annotation, a user can help the fuzzer to solve previously unsolvable challenges. We extended various AFL-based fuzzers with the ability to annotate the source code of the target application with guidance hints. Our evaluation demonstrates that such simple annotations are able to solve problems that—to the best of our knowledge—no other current fuzzer or symbolic execution based tool can overcome. For example, with our extension, a fuzzer is able to play and solve games such as Super Mario Bros. or resolve more complex patterns such as hash map lookups. To further demonstrate the capabilities of our annotations, we use AFL combined with IJON to uncover both novel security issues and issues that previously required a custom and comprehensive grammar to be uncovered. Lastly, we show that using IJON and AFL, one can solve many challenges from the CGC data set that resisted all fully automated and human guided attempts so far.