Directed grey-box fuzzing aims to test target code in programs and is widely utilized in various scenarios, including patch testing, candidate vulnerability confirmation, and known vulnerability reproduction. However, we find that existing directed fuzzers generally lack effective input mutation strategies and resort to the randomness and empiricism inherent in AFL-based strategies, which prove to be inefficient in directed fuzzing contexts.