Titan: Efficient Multi-target Directed Greybox Fuzzing

Modern directed fuzzing often faces scalability issues when analyzing multiple targets in a program simultaneously. We observe that the root cause is that directed fuzzers are unaware of the correlations among the targets, thereby could degenerate into a target-undirected method. As a result, directed fuzzing suffers severely from efficiency when reproducing multiple targets. This paper presents Titan, which enables fuzzers to distinguish correlations among various targets in the program and, thus, optimizes the input generation to reproduce multiple targets effectively. Leveraging these correlations, Titan differentiates seeds' potential of reaching each target for the scheduling and identifies bytes that can be changed simultaneously for the mutation. We compare our approach to eight state-of-the-art (directed) fuzzers. The evaluation demonstrates that Titan outperforms existing approaches by efficiently detecting multiple targets, achieving a 21.4x speedup and requiring 95.0% fewer number of executions. In addition, Titan detects nine incomplete fixes, which cannot be detected by other directed fuzzers, in the latest versions of the benchmark programs with two CVE IDs assigned. © 2024 IEEE.