Publication
Mutation-based fuzzing effectively discovers defects in JS engines. High-quality mutations are key for the performance of mutation-based fuzzers. The choice of the underlying representation (e.g., a sequence of tokens, an abstract syntax tree, or an intermediate representation) defines the possible mutation space and subsequently influences the design of mutation operators. Current program representations in JS engine fuzzers center around abstract syntax trees and customized bytecode-level intermediate languages. However, existing efforts struggle to generate semantically valid and meaningful mutations, limiting the discovery of defects in JS engines. Our proposed graph-based intermediate representation, FlowIR, directly represents the JS control flow and data flow as the mutation target. FlowIR is essential for the implementation of powerful semantic mutation. It supports mutation operators at the data flow and control flow level, thereby expanding the granularity of mutation operators. Experimental results show that our method is more effective in discovering new bugs. Our prototype, FuzzFlow, outperforms state-of-the-art fuzzers in generating valid test cases and exploring code coverage. In our evaluation, we detected 37 new defects in thoroughly tested mainstream JS engines.