Deep learning (DL) libraries are widely used in critical applications, where even subtle silent bugs can lead to serious consequences. While existing DL fuzzing techniques have made progress in detecting crashes, they inherently struggle to detect silent bugs due to the lack of effective test programs and corresponding oracles. Building on the observation that historical bug reports contain rich, underutilized information about silent bugs, we leverage large language models (LLMs) to perform versatile yet controlled bug transfer for silent bug fuzzing. Specifically, our approach uses LLMs to extract context-aware bug patterns from historical issues, match semantically related Application Programming Interfaces (APIs) using functionality-based embeddings, and synthesize test cases with customized oracles. This enables proactive detection of silent bugs by transferring high-risk contexts and oracle designs from known buggy APIs to functionally similar target APIs. To ensure the reliability of our context-aware bug transfer, we introduce an LLM-powered self-validation module that systematically evaluates the validity of each transferred bug instance. We implement this methodology in a tool named TransFuzz and evaluate it on three mainstream DL libraries: PyTorch, TensorFlow, and MindSpore. TransFuzz successfully discovers 79 previously unknown bugs (12 confirmed as Common Vulnerabilities and Exposures (CVEs)) in 10 bug types, demonstrating its effectiveness and generalizability in migrating DL library bug discovery capabilities.

Rust is an emerging programming language that has gained popularity in many academic and industrial projects due to its memory safety features. However, despite its safety advantages, there are still many serious bugs in existing Rust projects. The lack of suitable fuzz targets has been a major challenge in Rust fuzz testing, as existing fuzzing tools often rely on manual efforts to write test targets, which is time-consuming and cannot be directly applied to library function interfaces. While the previous work has made significant progress in generating fuzz targets for Rust, it still cannot effectively test special functions with generics and polymorphism.To address this issue, this paper proposes an automated approach for generating fuzz targets for Rust library functions, called RVFuzz. RVFuzz addresses the challenges by performing static analysis through variable instantiation, ensuring that special functions in Rust, such as those with generics and polymorphism, are covered in the interface sequence analysis. It then traverses the library function dependency graph to generate a set of reasonable and non-redundant target function sequences for fuzz testing. Any compilation errors encountered during the process are used to refine the sequence generation. RVFuzz successfully generated fuzz targets for 10 popular Rust projects, achieving an impressive interface coverage rate of 92.3%.