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Directed grey-box fuzzing (DGF) is a target-guided fuzzing intended fortesting specific targets (e.g., the potential buggy code). Despite numeroustechniques proposed to enhance directedness, the existing DGF techniques stillface challenges, such as taking into account the difficulty of reachingdifferent basic blocks when designing the fitness metric, and promoting theeffectiveness of symbolic execution (SE) when solving the complex constraintsin the path to the target. In this paper, we propose a directed hybrid fuzzercalled HyperGo. To address the challenges, we introduce the concept of pathprobability and combine the probability with distance to form an adaptivefitness metric called probability-based distance. By combining the two factors,probability-based distance can adaptively guide DGF toward paths that arecloser to the target and have more easy-to-satisfy path constraints. Then, weput forward an Optimized Symbolic Execution Complementary (OSEC) scheme tocombine DGF and SE in a complementary manner. The OSEC would prune theunreachable branches and unsolvable branches, and prioritize symbolic executionof the seeds whose paths are closer to the target and have more branches thatare difficult to be covered by DGF. We evaluated HyperGo on 2 benchmarksconsisting of 21 programs with a total of 100 target sites. The experimentalresults show that HyperGo achieves 38.47$\times$, 30.89$\times$, 28.52$\times$,106.09$\times$ and 143.22$\times$ speedup compared to AFLGo, AFLGoSy, BEACON,WindRanger, and ParmeSan, respectively in reaching target sites, and3.44$\times$, 3.63$\times$, 4.10$\times$, 3.26$\times$, and 3.00$\times$speedup in exposing known vulnerabilities. Moreover, HyperGo discovered 37undisclosed vulnerabilities from 7 real-world programs.

HyperGo: Probability-based Directed Hybrid Fuzzing