Evaluating the Effectiveness of Coverage-Guided Fuzzing for Testing Deep Learning Library APIs

Deep Learning (DL) libraries such as PyTorch provide the core components to build major AI-enabled applications. Finding bugs in these libraries is important and challenging. Prior approaches have tackled this by performing either API-level fuzzing or model-level fuzzing, but they do not use coverage guidance, which limits their effectiveness and efficiency. This raises an intriguing question: can coverage guided fuzzing (CGF), in particular frameworks like LibFuzzer, be effectively applied to DL libraries, and does it offer meaningful improvements in code coverage, bug detection, and scalability compared to prior methods? We present the first in-depth study to answer this question. A key challenge in applying CGF to DL libraries is the need to create a test harness for each API that can transform byte-level fuzzer inputs into valid API inputs. To address this, we propose FlashFuzz, a technique that leverages Large Language Models (LLMs) to automatically synthesize API-level harnesses by combining templates, helper functions, and API documentation. FlashFuzz uses a feedback driven strategy to iteratively synthesize and repair harnesses. With this approach, FlashFuzz synthesizes harnesses for 1,151 PyTorch and 662 TensorFlow APIs. Compared to state-of-the-art fuzzing methods (ACETest, PathFinder, and TitanFuzz), FlashFuzz achieves up to 101.13 to 212.88 percent higher coverage and 1.0x to 5.4x higher validity rate, while also delivering 1x to 1182x speedups in input generation. FlashFuzz has discovered 42 previously unknown bugs in PyTorch and TensorFlow, 8 of which are already fixed. Our study confirms that CGF can be effectively applied to DL libraries and provides a strong baseline for future testing approaches.