Quickstart

Assuming you have followed the installation process described here, you should be able to run SightHouse without any arguments to get the help:

$ sighthouse

          _^_
          |@|
         =====
          #::
          #::     SightHouse v1.0.0
          #::        by: Fenrisfulsur & Madsquirrels
          #::
          #::
          #::
        ###::^-..
                 ^ ~ ~~ ~~ ~ ~ ~
                  \~~ ~~ ~ ~  ~~~~~


usage: sighthouse [-h] [--version] [-d] {package,pipeline,frontend} ...

SightHouse CLI

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  -d, --debug           Enable debug

COMMAND:
  {package,pipeline,frontend}
    package             Handle sighthouse package
    pipeline            Handle sighthouse pipeline
    frontend            Handle sighthouse frontend

Running the signature pipeline involves the following steps:

1. Choosing which packages to use: SightHouse was designed with modularity in mind, so each component is designed as a package that can be downloaded and installed. This allows you to scale and tailor the setup for each use case.

2. Download & install packages: Packages are distributed as a single TAR archive and can be installed using the following command: sighthouse package install package.tar.gz.

3. Start each package: Once installed, a package can be started using the command: sighthouse package run "Name of the package". To query installed packages, you can use the following command: sighthouse package list.

4. Query and inspect the pipeline: The health of the pipeline can be monitored using various commands present under sighthouse pipeline ....

Available Packages

For now, the following packages are available:

Name	Version	Author	Description
Ghidra Analyzer	1.0.0	Sighthouse Core Team	Extract signatures from binaries using Ghidra
uClibc Compiler	1.0.0	Sighthouse Core Team	Compiler for the uClibc library
Git Scrapper	1.0.0	Sighthouse Core Team	Scrapper for Git projects
PlatformIo Scrapper	1.0.0	Sighthouse Core Team	Scrapper for PlatformIo projects
PlatformIo Compiler	1.0.0	Sighthouse Core Team	Compiler for PlatformIo projects
Linux Kernel Compiler	1.0.0	Sighthouse Core Team	Compiler for the Linux Kernel
Autotools Compiler	1.0.0	Sighthouse Core Team	Compiler for Autotools projects
PlatformIo Preprocessor	1.0.0	Sighthouse Core Team	Preprocessor for PlatformIo projects
uClibc Preprocessor	1.0.0	Sighthouse Core Team	Preprocessor for the uClibc library

Deploy using Docker

Deploying all the required services with the correct setup can be time-consuming and error-prone. In order to simplify this process, we provided a Docker Compose file, allowing you to deploy an instance of the pipeline:

services:
  redis:
    image: redis:7
    hostname: redis
    ports:
      - "6379:6379"
    volumes:
      - ./data/redis:/data
  minio:
    image: minio/minio:RELEASE.2025-04-22T22-12-26Z
    hostname: minio
    environment:
      - MINIO_ROOT_USER=admin
      - MINIO_ROOT_PASSWORD=password
    command: 'minio server --console-address ":9001" /data'
    volumes:
      - ./data/minio:/data
    ports:
      - "9000:9000"
      - "9001:9001"

  createbuckets:
    image: minio/minio:RELEASE.2025-04-22T22-12-26Z
    depends_on:
      - minio
    restart: on-failure
    entrypoint: >
      /bin/sh -c "
      sleep 3;
      /usr/bin/mc alias set dockerminio http://minio:9000 admin password;
      /usr/bin/mc mb dockerminio/uploads;
      /usr/bin/mc anonymous set public dockerminio/uploads;
      exit 0;
      "

  bsim_postgres:
    image: ghidra-bsim-postgres:1.0.0
    hostname: bsim_postgres
    volumes:
      - ./data/postgres:/home/user/ghidra-data
    restart: unless-stopped
    ports:
      - "5432:5432"
    healthcheck:
      test: ["CMD-SHELL", "/ghidra/Ghidra/Features/BSim/support/pg_is_ready.sh || exit 1 "]
      retries: 5
      interval: "30s"
      timeout: "5s"

  create_bsim_db_postgres:
    image: create_bsim_db:1.0.0
    command: 'user "" bsim_postgres postgresql 5432'
    depends_on:
      bsim_postgres:
        condition: service_healthy
    restart: no

  sighthouse_analyzer:
    image: sighthouse:1.0.0
    restart: unless-stopped
    command: [
      "src/sighthouse/core_modules/GhidraAnalyzer",   # PACKAGE_PATH
      "Ghidra Analyzer",                              # ANALYZER_NAME
      "-w", "redis://redis:6379/0",
      "-r", "s3://minio:9000/uploads",
      "-g", "/ghidra",
      "-b", "postgresql://user@bsim_postgres:5432/bsim"
    ]
    depends_on:
      - minio
      - redis

  sighthouse_compiler:
    image: sighthouse:1.0.0
    restart: unless-stopped
    command: [
      "src/sighthouse/core_modules/PlatformIoCompiler",   # PACKAGE_PATH
      "PlatformIo Compiler",
      "-w", "redis://redis:6379/0",
      "-r", "s3://minio:9000/uploads",
    ]
    depends_on:
      - sighthouse_analyzer
      - minio
      - redis

  sighthouse_scrapper:
    image: sighthouse:1.0.0
    restart: unless-stopped
    volumes:
      - ./data/scrapper:/data
    command: [
      "src/sighthouse/core_modules/PlatformIoScrapper",   # PACKAGE_PATH
      "PlatformIo Scrapper",
      "-w", "redis://redis:6379/0",
      "-r", "s3://minio:9000/uploads",
      "-d", "sqlite:////data/scrapper.db"
    ]
    depends_on:
      - sighthouse_compiler
      - minio
      - redis

This setup uses one scrapper, one compiler and one analyzer but it can be easily extended to fit your needs.

You can now run the pipeline using docker compose up -d.