Hashashin: A Fuzzy Matching Tool for Binary Ninja
This tool detects similar functions between binaries, and ports annotations (currently only in the form of tags) for matching basic blocks.
Setup
Installation in Docker
Pre-requisites:
- Obtain Binary Ninja headless
BinaryNinja.zipfrom Vector35 and place atthird-party/binaryninja/BinaryNinja.zip - Obtain Binary Ninja headless license from Vector35 and place at
third-party/binaryninja/license.dat
Then, build the provided Docker containers as described here:
docker build -t safedocs/base/static-light -f Dockerfile.base .
docker build -t safedocs/hashashin:local -f Dockerfile.dev .Due to how the Developer docker is setup below, your code changes will sync into it and you do not need to rebuild unless dependencies change. Launch and enter the container:
docker run -it --rm -v"$(pwd):/processor" --name dev-hashashin safedocs/hashashin:localInstallation Locally
The Docker method is supported to enable more consistent development envrionments. However, you should be able to run locally as long as the dependencies are installed:
- BinaryNinja headless
- Python numpy
Usage
Generate Signatures
Provide a Binary Ninja database with tags applied to it which you want to identify.
./src/generate_signatures.py <input_bndb> <signature_file>For example, ./src/generate_signatures.py tests/test1_annotated.bndb test1_annotated.sig.
NOTE: On sizeable files with many functions, this process will take time.
Apply Signatures
./src/apply_signatures.py <input_binary> <signature_file>This will output to a file at the same path as the input_binary with .bndb appended,
which is the annotated Binary Ninja database as a result of applying signatures.

Formed in 2009, the Archive Team (not to be confused with the archive.org Archive-It Team) is a rogue archivist collective dedicated to saving copies of rapidly dying or deleted websites for the sake of history and digital heritage. The group is 100% composed of volunteers and interested parties, and has expanded into a large amount of related projects for saving online and digital history.
