Generics-Aware Fuzz Target Generation for Rust Libraries via Structured API Analysis
Yiming Chen, Kaiwen Zhang, Guanjun Liu, Xiaofeng Li, Xiaogang Dong
Abstract
Fuzzing Rust library APIs requires constructing well-typed, compilable call sequences that satisfy ownership rules, generic parameters, and trait bounds; existing tools ignore these constraints or use shallow heuristics, yielding low coverage. We present GRAFT, which extracts structured API information from Rust documentation, builds an API dependency graph via recursive generics-aware type matching, and uses topology-guided traversal plus LLM synthesis with compiler-error feedback to produce compilable fuzz targets. On 13 crates from crates.io, GRAFT achieves 80.75% macro-average API coverage at 96.19% compilation success, outperforming RULF and RPG by 4.76x and 2.43x, and reaching 1.41x the average API coverage of deepSURF on crates with unsafe-reaching APIs.
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