DecLib

The decompiler API that works everywhere!

DecLib is an abstracted decompiler API that enables you to write plugins/scripts that work, with minimal edit, in every decompiler supported by DecLib. DecLib was originally designed to work with BinSync, and is the backbone for all BinSync based plugins. As an example, with the same script, you can redefine the types of function variables with custom structs, all in less than 30 lines, in any supported decompilers.

Install

pip install declib

The minimum Python version is 3.10.

Supported Decompilers

• IDA Pro: >= 8.4 (if you have an older version, use v1.26.0)
• Binary Ninja: >= 2.4
• angr-management: >= 9.0
• Ghidra: >= 12.0 (started in PyGhidra mode)
• JADX: >= 1.5.6 (headless Java/Android support; see the JADX backend guide). JADX is optional and is not bundled with DecLib.

Usage

DecLib exposes all decompiler API through the abstract class DecompilerInterface. The DecompilerInterface can be used in either the default mode, which assumes a GUI, or headless mode. In headless mode, the interface will start a new process using a specified decompiler.

You can find various examples using DecLib in the examples folder. Examples that are plugins show off more of the complicated API that allows you to use an abstracted UI, artifacts, and more.

Agent CLI

If you want a simplified command line interface (especially well-suited for LLMs), see the decompiler CLI guide.

decompiler load /path/to/binary --backend ida
decompiler decompile main --id 1234

UI Mode (default)

To use the same script everywhere, use the convenience function DecompilerInterface.discover_interface(), which will auto find the correct interface. Copy the below code into any supported decompiler and it should run without edit.

from declib.api import DecompilerInterface

deci = DecompilerInterface.discover()
for addr in deci.functions:
    function = deci.functions[addr]
    if function.header.type == "void":
        function.header.type = "int"
        deci.functions[function.addr] = function

Note that for Ghidra in UI mode you must first start it in PyGhidra mode. You can do this by going to your install dir and running ./support/pyghidraRun.

Headless Mode

To use headless mode you must specify a decompiler to use. You can get the traditional interface using the following:

from declib.api import DecompilerInterface

deci = DecompilerInterface.discover(force_decompiler="ghidra", headless=True)

In the case of Ghidra, you must have the environment variable GHIDRA_INSTALL_DIR set to the path of the Ghidra installation (the place the ghidraRun script is located).

Artifact Access Caveats

In designing the dictionaries that contain all Artifacts in a decompiler, we had a clash between ease-of-use and speed. When accessing some artifacts like a Function, we must decompile the function. Decompiling is slow. Due to this issue we slightly changed how these dictionaries work to fast accessing.

The only way to access a full artifact is to use the getitem interface of a dictionary. In practice this looks like the following:

for func_addr, light_func in deci.functions.items():
    full_function = deci.function[func_addr]

Notice, when using the items function the function is light, meaning it does not contain stack vars and other info. This also means using keys, values, or list on an artifact dictionary will have the same affect.

Serializing Artifacts

All artifacts are serializable to the TOML and JSON formats. Serialization is done like so:

from declib.artifacts import Function
import json

my_func = Function(name="my_func", addr=0x4000, size=0x10)
json_str = my_func.dumps(fmt="json")
loaded_dict = json.loads(json_str) # now loadable through normal JSON parsing
loaded_func = Function.loads(json_str, fmt="json")

Sponsors

BinSync and its associated projects would not be possible without sponsorship. In no particular order, we'd like to thank all the organizations that have previously or are currently sponsoring one of the many BinSync projects.