Report on Current Developments in Directed Grey-box Fuzzing
General Direction of the Field
The field of directed grey-box fuzzing (DGF) is witnessing significant advancements aimed at improving the efficiency and effectiveness of vulnerability detection in specific code areas. Recent developments are focused on refining the core components of DGF frameworks, including distance metrics, initial seed corpus generation, and exploration strategies. These innovations are driven by the need to enhance the precision and speed of fuzzing processes, particularly in complex and specialized environments like OS kernels and containerized applications.
One of the key trends is the empirical evaluation of distance metrics, which are critical for guiding the fuzzing process towards target vulnerabilities. Recent studies have highlighted the limitations of existing distance metrics in accurately describing the difficulty of triggering vulnerabilities, suggesting a need for more sophisticated metrics or alternative guidance mechanisms. This has led to the exploration of new methods for calculating and applying distance metrics, with a focus on fine-grained and context-aware approaches.
Another notable trend is the integration of advanced technologies, such as Large Language Models (LLMs), into the fuzzing process. LLMs are being leveraged to generate optimized initial seed corpora, which can significantly enhance the efficiency of directed fuzzing by guiding the fuzzer towards critical areas of the code more effectively. This approach not only speeds up the fuzzing process but also improves the detection of target vulnerabilities, demonstrating the potential of AI-driven techniques in advancing fuzzing methodologies.
Exploration strategies are also undergoing significant refinement, with a focus on understanding and enhancing the effectiveness of strategies that assist fuzzers in navigating complex program states. Recent studies have highlighted the importance of comprehensive evaluations and the development of customized strategies to improve coverage and bug detection. This includes the use of customized dictionaries and dynamic switching between exploration and exploitation phases, which are shown to enhance the performance of fuzzing frameworks.
Noteworthy Papers
G-Fuzz: Introduces a novel directed fuzzing framework for gVisor, addressing the challenges of fuzzing OS kernels written in Go. G-Fuzz's performance significantly outperforms existing state-of-the-art kernel fuzzers.
ISC4DGF: Proposes a novel approach to generating optimized initial seed corpus for DGF using Large Language Models (LLMs), achieving a significant speedup and improved vulnerability detection.
