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Dynamic binary translation framework for instrumenting x86-64 user space Linux programs
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Granary is a dynamic binary translation (DBT) framework for 64-bit user space Linux programs. It is permissively licensed (MIT license).

Granary has several novelties. The instrumentation model is unique in that it allows instrumentation tools to take control of the just-in-time basic block decoder. This feature enables tools to implement things like tracing (typically implemented as a core optimization in DBT systems).

Granary also has a flexible virtual register and inline assembly system. Instrumentation tools can specify what assembly instructions they want to inject into a program and where, and they can write this assembly without concerning themselves with register allocation/saving/restoring.

Granary has some other cool stuff too. It is a big melting pot of experimental ideas. Some have panned out, some haven't. In retrospect, the instrumentation model is overly complex. I think virtual registers are a big win, but I originally designed them so that they would work for kernel-space instrumentation as well. This put huge constraints on how they work and complicated the implementation. I eventually abandoned the kernel-side of things, but the complicated code persists...

Anyway, enjoy!


Step 0: Make sure you have everything that you need.

  1. Get LLVM and clang:
sudo apt-get install clang-3.5 llvm libc++-dev libc++1 binutils

Note: If clang-3.5 is not available on your distribution, then try getting clang-3.4 or clang-3.3.

Note: Granary uses llvm-link-3.5 by default. If you don't have it, then you can manually specify a different version of llvm-link. For example, make all GRANARY_LLVM_LINK=llvm-link (this will use the system's default version of llvm-link).

  1. Make sure you have Python 2.7 or above, but not Python 3.

Note: If you have multiple Python 2.7 is not your system default, then you can still specify the path to the Python 2.7. For example: make all GRANARY_PYTHON=/usr/bin/python-2.7.

Step 1: Initial setup.

This does things like fetch dependencies.

make setup

Step 2: Compiling Granary.

Test Cases

Be sure to run Granary through its paces first. These tests are definitely not exhaustive, but can help to determine if things are generally in working order:

make clean test


For debug builds, run:

make clean all

For release builds, run:

make clean all GRANARY_TARGET=release

Step 3: Run Granary.

You can use Granary's "injector" (called grr) to inject Granary into a process. Below is an example where Granary is injected into ls using grr.

./bin/debug_linux_user/grr -- ls

Command-line arguments can be passed to grr, and they will be forwarded to Granary itself. For example, if you've already compiled Granary's clients (see below) then you could try the following:

./bin/debug_linux_user/grr --tools=watchpoints -- ls

The list of available command-line arguments can be seen by invoking:

./bin/debug_linux_user/grr --help -- ls

Notice that some options have a tool name, shown in green, listed beside them. That means that those options are tool-specific and are ignored when that tool is not used.

Okay, time to actually do something. Lets see what system calls are executed by a program. To do this, tell Granary to use the system call tracing tool:

./bin/debug_linux_user/grr --tools=strace -- ls

You'll need to press enter to get past the "debug GDB prompt". This is a convenience feature for Granary developers. It enables GDB-based debugging of Granary by pausing the program and waiting for the user to press Enter, thus giving the developer plenty of time to attach GDB to the running process. Granary comes with its own .gdbinit file to improve the GDB-based debugging experience.

Here's an example where we disable the prompt:

./bin/debug_linux_user/grr --tools=strace --no_debug_gdb_prompt -- ls

Many Granary tools, e.g. memop, don't provide any kind of user interface. Instead, they implement common functionality (e.g. generic memory operand interposition) and hooks for other tools to use to achieve common tasks. For example, the poly_code tool uses the watchpoints tool, which then uses the memop tool.

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