llvm-python 0.0.1b1

A library for analyzing LLVM IR in Python

llvm_python

A fairly large proportion of programs are written in C/C++.

Let's imagine that you need to analyze programs in a given language, to search for vulnerabilities, to detect patterns or for optimization purposes. To solve such a problem, it is necessary to have the code in a slightly more convenient form than the source code of the program - an intermediate representation.

You might come up with the idea of building your own compiler, which is quite difficult, or you might decide to use intermediate representations of GCC or LLVM, but in that case you have to deal with the C/C++ API, which is something you don't want when you have elegant solutions in Python.

llvm_python allows you to analyze C/C++ programs in the LLVM IR representation in Python.

Usage example

The following example will build a control flow graph for the following function factorial.c, factorial.ll.

int factorial_req(int n)
{
    if (n == 1)
    {
        return 1;
    }
    return factorial_req(n - 1) * n;
}

from llvm_python import ir
from llvm_python import parse_assembly
from graphviz import Digraph


with open("../test_files/factorial.ll") as file:
    ll = file.read()

mod: ir.Module = parse_assembly(ll)

g = Digraph()

node_attributes = {
    "shape": "record",
    "style": "filled"
}


def graph_node(node: ir.Value):
    text = str(node).replace("\n", "\l")
    return node.name[1:], node.name + ":" + text


for block in mod.get_function("factorial_req").blocks:
    color = "#f59c7d70"  # Default block color
    last_instruction = block.instructions[-1]

    if last_instruction.op_code_name == "br":
        operands = last_instruction.operands
        if len(operands) == 3:
            g.edge(block.name[1:], operands[1].name[1:], label="True")
            g.edge(block.name[1:], operands[2].name[1:], label="False")
            color = "#b70d2870"  # switch-type block
        else:
            g.edge(block.name[1:], operands[0].name[1:])
    if len(block.pred_blocks) >= 2:
        color = "#b70d2870"  # merge-type block

    g.node(*graph_node(block), **node_attributes, color=color)

g.save("cfg.dot")

Installation

Preferred way

pip install llvm_python

Supported versions

CPython3.7 - CPython3.12

On Windows 64bit and manylinux x86_64 platforms.

Manual installation

1. Dependencies

• For correct build you need CMake >= 3.27

• C and C++ compilers.

• The system must also have libraries libffi и libtinfo.

If the libraries are not found, you can add them manually, example CMakeLists.txt, lines 12-15.

• Preferred build system Ninja

2. Cloning llvm_python

git clone [e-mail]:Papr1ka/llvm_python.git

cd llvm_python

3. LLVM setup

You must have the static library LLVM version >= 16 installed on the system.

This can be done via the distribution's package manager.

Example:

sudo dnf install llvm

sudo dnf install llvm-devel

Or you can build LLVM manually and specify the path to the cmake folder, line 21

LLVM_DIR = "/usr/lib/llvm-18/cmake" # Path to cmake folder of llvm library

Or you can download compiled libraries

• Deb пакеты https://apt.llvm.org/
• Windows https://packages.msys2.org/package/mingw-w64-x86_64-llvm

4. Run setup.py

in the directory with setup.py:

python -m pip install .

Contact

For any questions related to the library, you can contact us by mail

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