P-Code Graph

This library helps to build, display and vectorize the Control-Data Graph (CDG) of a chunk of binary code, using Ghidra P-Code representation.

Command-line interface

A cdg tool is provided to play with binary of assembly files.

For example to dump the CDG of a function in markdown format:

(venv) $ cdg mermaid test.o -f func -o graph.md

Or to display the graph of the whole content of an assembly file in HTML:

(venv) $ cdg html test.asm -o graph.html

Tests

Run tests

$ python3.14 -m venv venv
$ source venv/bin/activate
(venv) $ pip install .[test,torch]
(venv) $ python -m pytest tests

Test output

Tests using the outdir fixture generate their files in a dedicated output directory, in which you can usually find:

• the log file
• the dump of P-Code operations
• the dump of generated graph in markdown+mermaid format
• a HTML file to display the related graph in a browser

You can display the dataflow graph resulting from a test in its output directory:

$ firefox out/tests/maker_test/test_call/graph.html

API

Graph representation

The graph.py module contains the data classes defining a CDG.

classDiagram
  Graph *-- Node : nodes
  Graph *-- Edge : edges
  Graph: +list[Node] nodes
  Graph: +list[Edge] edges

  Node: +NodeKinds kind
  Node: +int size
  Node: +int value
  Node: +int register_id
  Node: +str register_name
  Node: +OpCodes opcode

  Edge: +EdgeKinds kind
  Edge: +int source_node
  Edge: +int destination_node
  Edge: +Optional[int] operand_number

Loading

Build a graph

We use LIEF library as a default binary loader. To build a graph from binary code:

from pcode_graph.maker import make_graph_from_binary
from pcode_graph.pcode import Translator
from pcode_graph.arch import Arch
from pcode_graph.lief_importer import iter_functions

translator = Translator(Arch.x86_64)

for function in iter_functions(Path("a.out")):
    graph = make_graph_from_binary(translator, 
                                   function.content, 
                                   function.address,
                                   ignore_flag_outputs=True)

Note that you can alternatively collect basic blocks (see lief_importer.py module), or use your own loader and make a graph from an arbitrary piece of binary code.

For testing purpose, you can also build it from assembly:

from pcode_graph.maker import make_graph_from_asm
from pcode_graph.asm import Assembler
from pcode_graph.pcode import Translator
from pcode_graph.arch import Arch

translator = Translator(Arch.arm_64)
assembler = Assembler(Arch.arm_64)
asm = """
add x0, x1, x2
blr x16
mul x1, x0, x0
"""

graph = make_graph_from_asm(assembler, translator, asm)

View the graph

You can unparse the graph into markdown using the mermaid syntax:

with open("graph.md", "w") as f:
    f.write(str(graph))

The module pcode_graph.view wraps the pyvis library to display graphs in a browser:

from pcode_graph.view import draw_graph

draw_graph(graph, "out/graph.html", open_browser=True)

Vectorize the graph

To convert a graph to tensors, you will need the list of the registers you expect your model to know, in order to convert the references to these registers into tensors.

If you know in advance a list of interesting registers, the map_registers function will create a dict that maps these registers and their alias to an hot-encoded vector:

from pcode_graph.gnn_exporter import map_registers

reg_map = map_registers("rsp", "rax", "rbx", "rsi", "rdi")

Then you can export your graph to a GNN as a torch_geometric Data object:

data = graph_to_data(graph, reg_map)
assert data.x is not None

If you want to help your GNN to deal with different architectures, a helper is provided to encode registers as a mask, depending on their role regarding the calling convention:

from pcode_graph.gnn_exporter import map_calling_convention_registers

reg_map = map_registers(Arch.x86_64)
print(reg_map)
{'rax': tensor([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
 'rdx': tensor([0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
 'rdi': tensor([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
 'rsi': tensor([0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
 ...

Many options are provided by the graph_to_data function depending on how you want to export graph information into node and edges features. Look at the function documentation for more details.

Converting into networkx

In case you want to apply some classical graph-theory algorithms, you can use export_to_networkx from nx_exporter module:

def export_to_networkx(graph: CDG, ignore_cfg: bool) -> nx.MultiDiGraph: ...