RAP is a static Rust analysis platform developed by researchers at Artisan-Lab, Fudan University. The project aims to provide a foundation for Rust programmers to develop or use advanced static analysis features beyond those offered by the rustc compiler. For further details, please refer to the RAP-Book.

The project is still under heavy development.

Quick Start

git clone https://github.com/Artisan-Lab/RAP.git
cd RAP 
./install.sh

Usage

Install nightly-2024-06-30 on which rap is compiled with. This just needs to do once on your machine. If the toolchain exists, this will do nothing.

rustup toolchain install nightly-2024-06-30 --profile minimal --component rustc-dev,rust-src,llvm-tools-preview

Navigate to your Rust project folder containing a Cargo.toml file. Then run cargo-rap with toolchain override shorthand syntax.

cargo +nightly-2024-06-30 rap # ... rest of options of cargo-rap

Check out supported options with -help:

cargo +nightly-2024-06-30 rap -help

Use-After-Free Detection

Detect bugs such as use-after-free and double free in Rust crates caused by unsafe code.

cargo +nightly-2024-06-30 rap -uaf

If RAP gets stuck after executing cargo clean, try manually downloading metadata dependencies by running cargo metadata.

The feature is based on our SafeDrop paper, which was published in TOSEM.

@article{cui2023safedrop,
  title={SafeDrop: Detecting memory deallocation bugs of rust programs via static data-flow analysis},
  author={Mohan Cui, Chengjun Chen, Hui Xu, and Yangfan Zhou},
  journal={ACM Transactions on Software Engineering and Methodology},
  volume={32},
  number={4},
  pages={1--21},
  year={2023},
  publisher={ACM New York, NY, USA}
}

Memory Leakage Detection

Detect memory leakage bugs caused by apis like ManuallyDrop and into_raw().

cargo +nightly-2024-06-30 rap -mleak

The feature is based on our rCanary work, which was published in TSE

@article{cui2024rcanary,
  title={rCanary: rCanary: Detecting memory leaks across semi-automated memory management boundary in Rust},
  author={Mohan Cui, Hongliang Tian, Hui Xu, and Yangfan Zhou},
  journal={IEEE Transactions on Software Engineering},
  year={2024},