DDGF: Dynamic Directed Greybox Fuzzing with Path Profiling
Pages 832 - 843
Abstract
Coverage-Guided Fuzzing (CGF) has become the most popular and effective method for vulnerability detection. It is usually designed as an automated “black-box” tool. Security auditors start it and then just wait for the results. However, after a period of testing, CGF struggles to find new coverage gradually, thus making it inefficient. It is difficult for users to explain reasons that prevent fuzzing from making further progress and to determine whether the existing coverage is sufficient. In addition, there is no way to interact and direct the fuzzing process. In this paper, we design the dynamic directed greybox fuzzing (DDGF) to facilitate collaboration between the user and fuzzer. By leveraging Ball-Larus path profiling algorithm, we propose two new techniques: dynamic introspection and dynamic direction. Dynamic introspection reveals the significant imbalance in the distribution of path frequency through encoding and decoding. Based on the insight from introspection, users can dynamically direct the fuzzer to focus testing on the selected paths in real time. We implement DDGF based on AFL++. Experiments on Magma show that DDGF is effective in helping the fuzzer to reproduce vulnerabilities faster, with up to 100x speedup and only 13% performance overhead. DDGF shows the great potential of human-in-the-loop for fuzzing.
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Index Terms
- DDGF: Dynamic Directed Greybox Fuzzing with Path Profiling
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