Fuzz testing is a widely used method for finding security issues in software. However, certain code paths can only be explored under specific program states. Flag variables, which represent internal states, are crucial in influencing program behavior through flag-guarded branches. Unfortunately, existing fuzzing tools struggle to efficiently explore them due to the implicit data dependency between flag variables and the input. As a result, they commonly lack awareness of the dependency between program input and the assignments of critical flag variables, leading to a blind or random approach to satisfy flag-checking constraints, which greatly impacts the fuzzing efficiency.To address this issue, this paper proposes a dynamic flag-guided hybrid fuzzing approach, which automates the identification of flag variables and provides guidance for fuzz testing. Specifically, we first design a pre-fuzzing program analysis to recognize flag variables and a novel data structure to present how flag variables guard code branches. Then, we propose a new constraint-solving approach by separating complex flag-checking constraints into a set of atomic ones and sequentially solving them by traversing our FDG to locate execution paths that could assign the flag variables with the desired values.We implement a prototype tool, called Algernon, and evaluate it on 20 popular open-source programs. Across all tested programs, Algernon outperforms QSYM, Angora, AFL++, and INVSCOV in terms of both code coverage and vulnerability discovery, demonstrating the effectiveness of our approach. During our experiments, Algernon successfully found 30 zero-day vulnerabilities with 11 CVE IDs assigned.

Fuzzing is one of the most popular and practical techniques for security analysis. In this work, we aim to address the critical problem of high-quality input generation with a novel input-aware fuzzing approach called NestFuzz. NestFuzz can universally and automatically model input format specifications and generate valid input.