QuarkslaB Dynamic binary Instrumentation (QBDI) is a modular, cross-platform and cross-architecture DBI framework. It aims to support Linux, macOS, Android, iOS and Windows operating systems running on x86, x86-64, ARM and AArch64 architectures. In addition of C/C++ API, Python and JS/frida bindings are available to script QBDI. Information about what is a DBI framework and how QBDI works can be found in the documentation introduction.
QBDI modularity means it doesn't contain a preferred injection method and it is designed to be
used in conjunction with an external injection tool. QBDI includes a tiny (
Linux and macOS injector for dynamic executables (QBDIPreload).
QBDI is also fully integrated with Frida, a reference dynamic instrumentation toolkit,
allowing anybody to use their combined powers.
A current limitation is that QBDI doesn't handle signals, multithreading (it doesn't deal with new threads creation) and C++ exception mechanisms. However, those system-dependent features will probably not be part of the core library (KISS), and should be integrated as a new layer (to be determined how).
|CPU||Operating Systems||Execution||Memory Access Information|
|x86-64||Android, Linux, macOS, Windows||Supported||Supported|
|x86||Android, Linux, macOS, Windows||Supported||Supported|
|ARM||Linux, Android, iOS||Planned (*)||Planned (*)|
|AArch64||Android, Linux, macOS||Supported (*)||Supported (*)|
* The ARM and AArch64 instruction sets are supported for internal use at the moment.
Every new QBDI version is compiled and made available on the Github release page.
Python API (PyQBDI)
PyQBDI is available through PyPI. The wheel package can be either downloaded or installed with the following command:
pip install PyQBDI
The PyQBDI package is self-contained so completely independent from the C/C++ package.
There is no strict development timeline or scheduled release plan for the QBDI project.
All the new features and fixes are merged onto the
Devel packages can be downloaded in the artefacts of:
- Appveyor for windows packages (C/C++ API and PyQBDI)
- Github Actions for Linux C/C++/frida API (based on ubuntu)
- Github Actions for OSX C/C++/frida API
- Github Actions for android C/C++/frida API
- Github Actions for Linux PyQBDI
- Github Actions for OSX PyQBDI
To build this project, the following dependencies are needed on your system:
- cmake >= 3.12
- ninja or make
- C++17 toolchain (gcc, clang, Visual Studio 2019, ...)
The compilation is a two-step process:
- local library distribution of LLVM is built.
- QBDI is built using the LLVM library.
This local built of LLVM is required because QBDI uses private APIs not exported by regular LLVM installations and because our code is only compatible with a specific version of those APIs. This first step is cached and only needs to be run once, subsequent builds only need to repeat the second step.
QBDI build system relies on CMake and requires to pass build configuration flags. To help with
this step we provide shell scripts for common build configurations which follow the naming pattern
config-OS-ARCH.sh. Modifying these scripts is necessary if you want to compile in debug mode or
Create a new directory at the root of the source tree, and execute the Linux configuration script:
mkdir build cd build ../cmake/config/config-linux-X86_64.sh ninja
You can follow the same instructions as for x86-64 but instead, use the
config-linux-X86.sh configuration script.
Compiling QBDI on macOS requires a few things:
- A modern version of macOS (like Sierra)
- Xcode (from the App Store or Apple Developer Tools)
- the Command Line Tools (
- a package manager (preferably MacPorts, but HomeBrew should also be fine)
- some packages (
port install cmake wget ninja)
Once requirements are met, create a new directory at the root of the source tree, and execute the macOS configuration script:
mkdir build cd build ../cmake/config/config-macOS-X86_64.sh ninja
Building on Windows requires a pure Windows installation of Python 3 (from the official packages, this is mandatory) in order to build our dependencies (we really hope to improve this in the future). It also requires an up-to-date CMake and Ninja.
First of all, the Visual Studio environment must be set up. This can be done with a command such as:
"C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvarsall.bat" x64
Then, the following commands must be run:
mkdir build cd build python ../cmake/config/config-win-X86_64.py ninja
Cross-compiling for Android requires the NDK (or the SDK) to be installed on your workstation.
For now, it has only been tested under Linux.
If not already installed, you can download the latest Android NDK package
through the official website
and extract it.
config-android-*.sh configuration script needs to be
customised to match your NDK installation directory and the target platform.:
# Configure and compile QBDI X86_64 with a NDK mkdir build && cd build NDK_PATH=<your_NDK_PATH> ../cmake/config/config-android-X86_64.sh ninja # Configure and compile QBDI X86 with a SDK mkdir build && cd build ANDROID_SDK_ROOT=<your_SDK_PATH> ../cmake/config/config-android-X86.sh ninja
The PyQDBI library (apart from the wheel package) can be built by solely passing the '-DQBDI_TOOLS_PYQBDI=ON' option to the CMake build system.
However, if you want to build the wheel package, you can run these commands:
python -m pip install --upgrade pip python -m pip install setuptools wheel python setup.py bdist_wheel
A 32-bit version of Python is mandatory for the X86 architecture whereas a 64-bit one is required for the X86-64 architecture.
About the ARM support
QBDI supports the ARM architecture up to its 0.6.2 version. Unfortunately, the ARM architecture hasn't been recently tested so is now marked as deprecated.