Bitmap Fuzzing: A Comparative Analysis of Libfuzzer and AFL Tools

This research work compares the effectiveness of bitmap fuzzing between two prominent algorithms exploited in LibFuzzer and AFL, aimed at identifying vulnerabilities in software handling bitmap images. Bitmap fuzzing generates varied image-based test cases that rigorously test software, revealing potential security flaws. In this analysis, LibFuzzer, an in-process guided fuzzer, and AFL, an external fuzzer driven by coverage feedback, are both utilized to evaluate their accuracy in detecting errors within bitmap-processing applications. The performance is assessed based on the types and frequency of errors found, offering a layered perspective on error-handling strength in image processing contexts. By recording software crashes and categorizing the faults, this proposed research provides important insights into the comparative strengths and limitations of these fuzzing tools. The experimental results aid significant improvements in fuzzing practices, enhancing security frameworks by enabling early identification of vulnerabilities in multimedia-focused applications. The devised comparative research highlights the critical role of fuzzing tools in building robust, resilient software defenses.