spell_hunter 0.1.2
Python module for finding interesting bytes in files
License: MIT
Requires Python: >=3.8
Keywords: reverse engineering, rust, files, binaries, forensics, forensic, research, security, binary analysis

![cdlogo](https://carefuldata.com/images/cdlogo.png)

# Spell Hunter

Spell Hunter is a Python module for searching out "interesting" bytes from files.

The functionality comes from the 'hunter' feature of the Rust CLI tool named [giant-spellbook](https://github.com/jpegleg/giant-spellbook/).

The patterns that Spell Hunter is searching for are various bytes that might be useful for research, including
patterns related to software exploits, vulnerabilities, malware, as well as useful items for reverse engineering.

There is a singular function named "hunt" in this module that searches a given file for all of the "interesting" bytes,
outputing JSON with the file name, UTC time, along with any patterns matched and the byte positions in the file of those patterns.

An example ELF file will have the "elf_magic" pattern found:

```
{
  "File": "/usr/bin/avahi-publish",
  "Report time": "2026-01-28 06:04:33.710910326 UTC",
  "Matched patterns": [
    {
      "Pattern name": "elf_magic",
      "Byte offset": [0]
    }
  ]
}

```

Here is another example JSON of a normal file that has more interesting bytes:

```
{
  "File": "/usr/bin/busybox",
  "Report time": "2026-01-28 06:05:50.842018625 UTC",
  "Matched patterns": [
    {
      "Pattern name": "pe_magic",
      "Byte offset": [805278, 805314]
    },
    {
      "Pattern name": "elf_magic",
      "Byte offset": [0]
    },
    {
      "Pattern name": "gzip_magic",
      "Byte offset": [447791]
    },
    {
      "Pattern name": "zip_magic_local",
      "Byte offset": [459675]
    },
    {
      "Pattern name": "zip_magic_central",
      "Byte offset": [459645]
    },
    {
      "Pattern name": "zip_magic_end",
      "Byte offset": [459786]
    },
    {
      "Pattern name": "bin_sh_use",
      "Byte offset": [756212, 756513, 784805]
    },
    {
      "Pattern name": "shadow_access",
      "Byte offset": [696275]
    },
    {
      "Pattern name": "passwd_access",
      "Byte offset": [696263]
    }
  ]
}
```

All matches are _just known pattern matches_, not conclusions. The tool is an aide to research, it doesn't do the research for you.

## Installation

Install with `pip`:

```
pip install spell_hunter
```

Or `uv`:

```
uv add spell_hunter
```

Alternatively, compile the wheel from source and install the wheel directly.

```
maturin build
...
uv pip install target/wheels/spell_hunter-0.1.0-cp313-cp313-manylinux_2_34_x86_64.whl

```


#### Example usage

Let's start with a simple use of loading a hard-coded file and playing with the JSON:

```
import spell_hunter
import json

def main():
    hunter = spell_hunter.hunt('/bin/ls')
    a = json.loads(hunter)
    print(a['File'], "was the file")
    print("reported at", a['Report time'])
    print("which the matches of", a['Matched patterns'])

if __name__ == "__main__":
    main()

```

Next let's look at an example of taking command line arguments and investigating each file provided:

```
import spell_hunter
import json
import sys

def main():
    for arg in sys.argv[1:]:
        print(json.loads(spell_hunter.hunt(arg)))

if __name__ == "__main__":
    main()

```

When we execute this latest example, we get output like this:

```
.venv/bin/python3.13 main.py /bin/uptime /bin/bash /bin/sh /usr/local/bin/enchant
{'File': '/bin/uptime', 'Report time': '2026-01-28 05:59:05.859766527 UTC', 'Matched patterns': [{'Pattern name': 'elf_magic', 'Byte offset': [0]}]}
{'File': '/bin/bash', 'Report time': '2026-01-28 05:59:06.643373702 UTC', 'Matched patterns': [{'Pattern name': 'elf_magic', 'Byte offset': [0]}, {'Pattern name': 'bin_sh_use', 'Byte offset': [204386, 204545]}]}
{'File': '/bin/sh', 'Report time': '2026-01-28 05:59:06.722365909 UTC', 'Matched patterns': [{'Pattern name': 'elf_magic', 'Byte offset': [0]}, {'Pattern name': 'bin_sh_use', 'Byte offset': [98562]}]}
{'File': '/usr/local/bin/enchant', 'Report time': '2026-01-28 05:59:07.038956316 UTC', 'Matched patterns': [{'Pattern name': 'elf_magic', 'Byte offset': [0, 98913]}]}

```

Let's use another variation, this time keeping the output as the original JSON for each argument file:

```
import spell_hunter
import json
import sys

def main():
    for arg in sys.argv[1:]:
        print(spell_hunter.hunt(arg))

if __name__ == "__main__":
    main()

```

And when we run that:

```
.venv/bin/python3.13 main.py /bin/uptime /bin/bash /bin/sh /usr/local/bin/enchant
{
  "File": "/bin/uptime",
  "Report time": "2026-01-28 07:41:14.917662092 UTC",
  "Matched patterns": [
    {
      "Pattern name": "elf_magic",
      "Byte offset": [0]
    }
  ]
}
{
  "File": "/bin/bash",
  "Report time": "2026-01-28 07:41:15.098475373 UTC",
  "Matched patterns": [
    {
      "Pattern name": "elf_magic",
      "Byte offset": [0]
    },
    {
      "Pattern name": "bin_sh_use",
      "Byte offset": [204386, 204545]
    }
  ]
}
{
  "File": "/bin/sh",
  "Report time": "2026-01-28 07:41:15.119242895 UTC",
  "Matched patterns": [
    {
      "Pattern name": "elf_magic",
      "Byte offset": [0]
    },
    {
      "Pattern name": "bin_sh_use",
      "Byte offset": [98562]
    }
  ]
}
{
  "File": "/usr/local/bin/enchant",
  "Report time": "2026-01-28 07:41:15.187756273 UTC",
  "Matched patterns": [
    {
      "Pattern name": "elf_magic",
      "Byte offset": [0, 98913]
    }
  ]
}
```

If we send invalid input to the hunt function, we'll get back JSON with an error:

```
.venv/bin/python3.13 main.py /bin/uptime -1
{
  "File": "/bin/uptime",
  "Report time": "2026-01-28 07:37:59.756211114 UTC",
  "Matched patterns": [
    {
      "Pattern name": "elf_magic",
      "Byte offset": [0]
    }
  ]
}
{ "ERROR": "Invalid input" }
```

Rather than just printing out JSON like that, we more likely would want to write out to files or do something fancier with the data.

Let's illustrate a more complex example with storing the data in sqlite and creating a report visualizing the files.

```
import spell_hunter
import sqlite3
import uuid
from datetime import datetime
from functools import reduce
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from collections import defaultdict
import numpy as np
import json
import sys
import os

def timeslice():
    global TIMESTAMP
    TIMESTAMP = datetime.now()
    return(TIMESTAMP)

def createtable():
    C.execute('''CREATE TABLE hunter
                        (TIME text, FILE text, PATTERNS text)''')
def hunting(FILE):
    h = json.loads(spell_hunter.hunt(FILE))
    global TIME
    TIME = h['Report time']
    global PATTERNS
    PATTERNS = json.dumps(h['Matched patterns'])
    #print(json.dumps(h['Matched patterns']))

def insertstat(TXID, FILE):
    try:
        sqlite_insert_with_param = """INSERT INTO hunter
                                            (TIME, FILE, PATTERNS)
                                            VALUES (?, ?, ?);"""
        hunting(FILE)
        TIMESTAMP = timeslice()
        timeslice()
        print(TIMESTAMP, TXID, " hunter-demo: inserting ", FILE)
        data_tuple = (TIME, FILE, PATTERNS)
        C.execute(sqlite_insert_with_param, data_tuple)
        CONN.commit()
    except sqlite3.Error as error:
        timeslice()
        print(TIMESTAMP, TXID, " hunter-demo: Failed to insert into hunter.db FILE table:", error)

def main():
    try:
        os.remove('hunter.db')
    except OSError:
        pass
    global CONN
    global C
    CONN = sqlite3.connect('hunter.db')
    C = CONN.cursor()
    createtable()

    for arg in sys.argv[1:]:
        global TXID
        TXID = uuid.uuid4()
        insertstat(TXID, arg)

    C.execute("SELECT TIME, FILE, PATTERNS FROM hunter")
    rows = C.fetchall()
    first_timestamp = rows[0][0] if rows else "No data"
    file_patterns = defaultdict(set)

    for time, file_path, patterns_json in rows:
        patterns = json.loads(patterns_json)
        file_name = file_path.split('/')[-1]

        for pattern_dict in patterns:
            pattern_name = pattern_dict.get("Pattern name")
            if pattern_name:
                file_patterns[file_name].add(pattern_name)

    all_patterns = sorted(set(pattern for patterns in file_patterns.values() for pattern in patterns))
    all_files = sorted(file_patterns.keys())
    matrix = np.zeros((len(all_files), len(all_patterns)))

    for i, file_name in enumerate(all_files):
        for j, pattern in enumerate(all_patterns):
            if pattern in file_patterns[file_name]:
                matrix[i][j] = 1
    fig, ax = plt.subplots(figsize=(max(12, len(all_patterns) * 0.8), max(8, len(all_files) * 0.5)))
    cax = ax.imshow(matrix, cmap='YlOrRd', aspect='auto', interpolation='nearest')
    ax.set_xticks(np.arange(len(all_patterns)))
    ax.set_yticks(np.arange(len(all_files)))
    ax.set_xticklabels(all_patterns, rotation=45, ha='right', fontsize=9)
    ax.set_yticklabels(all_files, fontsize=9)
    ax.set_xticks(np.arange(len(all_patterns)) - 0.5, minor=True)
    ax.set_yticks(np.arange(len(all_files)) - 0.5, minor=True)
    ax.grid(which='minor', color='gray', linestyle='-', linewidth=0.5)
    ax.set_xlabel('Pattern Names', fontsize=12, fontweight='bold')
    ax.set_ylabel('Files', fontsize=12, fontweight='bold')
    ax.set_title('Pattern Detection Across Files', fontsize=14, fontweight='bold', pad=20)
    info_text = f'First Scan Time: {first_timestamp}\nTotal Files: {len(all_files)}\nUnique Patterns: {len(all_patterns)}'
    props = dict(boxstyle='round', facecolor='lightblue', alpha=0.8)
    ax.text(0.02, 0.98, info_text, transform=fig.transFigure, fontsize=10,
            verticalalignment='top', bbox=props)
    cbar = plt.colorbar(cax, ax=ax, fraction=0.046, pad=0.04)
    cbar.set_ticks([0, 1])
    cbar.set_ticklabels(['Not Found', 'Found'])
    plt.tight_layout(rect=[0, 0, 1, 0.96])
    output_path = 'hunter_patterns_visualization.png'
    plt.savefig(output_path, dpi=300, bbox_inches='tight')
    print(f"Visualization saved to: {output_path}")
    print(f"\n=== Summary ===")
    print(f"First scan time: {first_timestamp}")
    print(f"Total files scanned: {len(all_files)}")
    print(f"Unique patterns detected: {len(all_patterns)}")
    print(f"\nPattern frequency:")
    for pattern in all_patterns:
        count = sum(1 for file_patterns_set in file_patterns.values() if pattern in file_patterns_set)
        print(f"  {pattern}: found in {count} file(s)")

    if (CONN):
        CONN.close()
        timeslice()
        print(TIMESTAMP, TXID, " hunter-demo: The DB CONNection is now closed.")


if __name__ == "__main__":
    main()

```

Then when running our latest example, we can get output like this:

```
.venv/bin/python3.13 main.py /bin/uptime /bin/bash /bin/sh /usr/local/bin/enchant /usr/bin/X /usr/bin/clang /usr/bin/apt-mark /bin/firefox-esr /bin/curl
2026-01-28 04:10:39.258996 b7fa84a5-e4b1-4567-9e5b-1c1b7618379b  hunter-demo: inserting  /bin/uptime
2026-01-28 04:10:39.480018 3988f2da-873c-4bd8-ae49-9669fda60c29  hunter-demo: inserting  /bin/bash
2026-01-28 04:10:39.655655 93a7d7e8-8444-4de6-96de-028173acaf24  hunter-demo: inserting  /bin/sh
2026-01-28 04:10:39.802700 2b1144d7-ae9e-4acc-ac96-adcd5e91ca19  hunter-demo: inserting  /usr/local/bin/enchant
2026-01-28 04:10:39.841773 a3e32fc7-444f-41fb-848d-93d0c88aac57  hunter-demo: inserting  /usr/bin/X
2026-01-28 04:10:39.904944 83f43ccd-03c8-4e53-ab48-84aa54541759  hunter-demo: inserting  /usr/bin/clang
2026-01-28 04:10:39.951992 66e1e999-6c4f-49d1-a8c0-0b11386278cd  hunter-demo: inserting  /usr/bin/apt-mark
2026-01-28 04:10:40.078290 7c128646-616e-4975-9dab-6bca19d0e02c  hunter-demo: inserting  /bin/firefox-esr
2026-01-28 04:10:40.169208 a4bfc004-35e6-48ac-a6d6-da580ce6d98f  hunter-demo: inserting  /bin/curl
Visualization saved to: hunter_patterns_visualization.png

=== Summary ===
First scan time: 2026-01-28 09:10:39.258927893 UTC
Total files scanned: 9
Unique patterns detected: 4

Pattern frequency:
  bin_sh_use: found in 3 file(s)
  elf_magic: found in 8 file(s)
  gzip_magic: found in 1 file(s)
  pe_magic: found in 1 file(s)
2026-01-28 04:10:41.187847 a4bfc004-35e6-48ac-a6d6-da580ce6d98f  hunter-demo: The DB CONNection is now closed.
```

And then we can open the image file and use it in a report or investigation.

```
open hunter_patterns_visualization.png
```

The sqlite database file could of course also then be saved for later use.