Directed Greybox Fuzzing Expanded to Rust and Go Shows Superior Time-to-Exposure

Global: Directed Greybox Fuzzing Expanded to Rust and Go Shows Superior Time-to-Exposure

A research team has introduced a novel directed greybox fuzzing framework specifically engineered for Rust and Go applications, reporting faster Time-to-Exposure (TTE) metrics than several established fuzzers. The approach leverages proximity‑based targeting to focus testing on designated program locations, aiming to improve vulnerability discovery efficiency during software development lifecycles.

Background

Program analysis and automated testing are core components of modern secure software development, with fuzzing serving as a widely adopted dynamic testing technique. Traditional coverage‑guided fuzzers excel at broad exploration but can underperform when the objective is to verify static analysis findings or reproduce known crashes. Directed fuzzing addresses this limitation by guiding input generation toward specific code regions using proximity metrics.

Extending to New Languages

While most existing fuzzing tools target C/C++ binaries, the rising adoption of Rust and Go has created a demand for testing solutions that accommodate their distinct compilation pipelines and runtime characteristics. The new framework expands directed fuzzing capabilities to these languages, aiming to provide developers with precise, language‑aware testing options.

Technical Innovations

The authors describe several technical contributions, including advanced preprocessing steps, custom modifications to the rustc compiler, and sophisticated graph construction and instrumentation techniques. These innovations enable the fuzzer to compute accurate distance metrics within Rust and Go binaries, thereby directing mutations toward the most relevant execution paths.

Implementation

The prototype builds upon the LibAFL‑DiFuzz backend, integrating the aforementioned preprocessing and instrumentation modules. Comparative evaluations were conducted against popular fuzzers such as afl.rs, cargo‑fuzz, and go‑fuzz.

Performance Evaluation

According to TTE experiments, the Rust‑LibAFL‑DiFuzz implementation achieved the best overall TTE result among the tools tested. The Go‑LibAFL‑DiFuzz variant not only recorded the best average TTE but also demonstrated two instances where its exposure time was orders of magnitude faster than competing solutions. The authors note that occasional stability variations can be attributed to differing mutation strategies employed by the framework.

Conclusion

These findings suggest that directed greybox fuzzing, when adapted for Rust and Go, can deliver more efficient vulnerability detection than existing language‑specific fuzzers. The work highlights the potential for broader adoption of targeted fuzzing techniques across diverse programming ecosystems, potentially enhancing software security during early development stages.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.