[Review] Large Language Models are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models

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The paper proposes a new approach to leveraging LLMs to generate input programs for fuzzing DL libraries. More specifically, apply LLMs(Codex & INCODER) to fuzz DL libraries(pytorch & tensorflow).

Background:

• Previous work on fuzzing DL libraries mainly falls into two categories: API-level fuzzing and model-level fuzzing. They still have some limitations.
• Model level fuzzers attempt to leverage complete DL models (which cover various sets of DL library APIs) as test inputs. But due to the input/output constraints of DL APIs, model-level mutation/generation is hard to perform, leading to a limited number of unique APIs covered.
• API-level fuzzing focuses on finding bugs within a single API at a time. But API-level fuzzers cannot detect any bug that arises from interactions within a complex API sequence.