A Priority Based Path Searching Method for Improving Hybrid Fuzzing

Abstract Hybrid fuzzing which combines classical fuzzing with concolic execution to produce effective test suites is an advanced software vulnerability detection technique. Because fuzzing and concolic execution are complementary in nature, some researchers propose “optimal strategy” and “discriminative dispatch strategy” to improve the performance of hybrid fuzzing. Although the ideas are interesting and useful, they have some limitations, such as high time overhead and difficulties in implementation. In this paper, we propose a Priority Based Path Searching method (PBPS) to utilize the capability of concolic execution better. PBPS evaluates each path's solving cost and solving demand, and prioritizes them based on two path characteristics, which are path lengths and sample-hits for concolic execution. The rationale is to keep the pipeline full by readily feeding the concolic engine with paths whose constraints are simpler to solve and are less likely to be explored by fuzz testing. We implement PBPS in Driller, which is a popular hybrid fuzzer and we evaluate our system “QuickFuzz” with the CQE dataset. Experimental results show that compared with DigFuzz and the original Driller, “QuickFuzz” discovers more vulnerabilities and achieves higher code coverage on the CQE dataset.