performing-sqlite-database-forensics

What this skill does

# Performing SQLite Database Forensics

## Overview

SQLite is the most widely deployed database engine in the world, used by virtually every mobile application, web browser, and many desktop applications to store user data. In digital forensics, SQLite databases are critical evidence sources containing browser history, messaging records, call logs, GPS locations, application preferences, and cached content. Forensic analysis goes beyond simple SQL queries to examine the internal B-tree page structures, freelist pages containing deleted records, Write-Ahead Log (WAL) files preserving transaction history, and unallocated space within database pages where recoverable data may persist after deletion.


## When to Use

- When conducting security assessments that involve performing sqlite database forensics
- When following incident response procedures for related security events
- When performing scheduled security testing or auditing activities
- When validating security controls through hands-on testing

## Prerequisites

- DB Browser for SQLite (sqlitebrowser)
- SQLite command-line tools (sqlite3)
- Python 3.8+ with sqlite3 module
- Belkasoft Evidence Center or Axiom (commercial)
- Hex editor (HxD, 010 Editor) for manual page inspection
- Understanding of B-tree data structures

## SQLite Internal Structure

### Database Header (First 100 Bytes)

| Offset | Size | Description |
|--------|------|-------------|
| 0 | 16 | Magic string: "SQLite format 3\000" |
| 16 | 2 | Page size (512-65536 bytes) |
| 18 | 1 | File format write version |
| 19 | 1 | File format read version |
| 24 | 4 | File change counter |
| 28 | 4 | Database size in pages |
| 32 | 4 | First freelist trunk page number |
| 36 | 4 | Total freelist pages |
| 52 | 4 | Text encoding (1=UTF-8, 2=UTF-16le, 3=UTF-16be) |
| 96 | 4 | Version-valid-for number |

### Page Types

| Type | ID | Description |
|------|----|-------------|
| B-tree Interior | 0x05 | Internal table node |
| B-tree Leaf | 0x0D | Table leaf page containing actual records |
| Index Interior | 0x02 | Internal index node |
| Index Leaf | 0x0A | Index leaf page |
| Freelist Trunk | - | Tracks freed pages |
| Freelist Leaf | - | Freed page with recoverable data |
| Overflow | - | Continuation of large records |

## Deleted Record Recovery

### Method 1: Freelist Page Analysis

When records are deleted, SQLite may place their pages on the freelist rather than overwriting them immediately.

```python
import struct
import sqlite3
import os


def analyze_freelist(db_path: str) -> dict:
    """Analyze SQLite freelist to identify pages containing deleted data."""
    with open(db_path, "rb") as f:
        # Read header
        header = f.read(100)
        page_size = struct.unpack(">H", header[16:18])[0]
        if page_size == 1:
            page_size = 65536
        first_freelist_page = struct.unpack(">I", header[32:36])[0]
        total_freelist_pages = struct.unpack(">I", header[36:40])[0]

        freelist_info = {
            "page_size": page_size,
            "first_freelist_page": first_freelist_page,
            "total_freelist_pages": total_freelist_pages,
            "trunk_pages": [],
            "leaf_pages": []
        }

        if first_freelist_page == 0:
            return freelist_info

        # Walk the freelist trunk chain
        trunk_page = first_freelist_page
        while trunk_page != 0:
            offset = (trunk_page - 1) * page_size
            f.seek(offset)
            page_data = f.read(page_size)

            next_trunk = struct.unpack(">I", page_data[0:4])[0]
            leaf_count = struct.unpack(">I", page_data[4:8])[0]

            leaves = []
            for i in range(leaf_count):
                leaf_page = struct.unpack(">I", page_data[8 + i * 4:12 + i * 4])[0]
                leaves.append(leaf_page)

            freelist_info["trunk_pages"].append({
                "page_number": trunk_page,
                "next_trunk": next_trunk,
                "leaf_count": leaf_count,
                "leaf_pages": leaves
            })
            freelist_info["leaf_pages"].extend(leaves)
            trunk_page = next_trunk

    return freelist_info


def extract_freelist_content(db_path: str, output_dir: str):
    """Extract raw content from freelist pages for analysis."""
    info = analyze_freelist(db_path)
    os.makedirs(output_dir, exist_ok=True)

    with open(db_path, "rb") as f:
        page_size = info["page_size"]
        for page_num in info["leaf_pages"]:
            offset = (page_num - 1) * page_size
            f.seek(offset)
            page_data = f.read(page_size)
            output_file = os.path.join(output_dir, f"freelist_page_{page_num}.bin")
            with open(output_file, "wb") as out:
                out.write(page_data)

    return len(info["leaf_pages"])
```

### Method 2: WAL (Write-Ahead Log) Analysis

The WAL file contains pending transactions that have not yet been checkpointed back to the main database.

```python
def parse_wal_header(wal_path: str) -> dict:
    """Parse SQLite WAL file header and frame inventory."""
    with open(wal_path, "rb") as f:
        header = f.read(32)
        magic = struct.unpack(">I", header[0:4])[0]
        file_format = struct.unpack(">I", header[4:8])[0]
        page_size = struct.unpack(">I", header[8:12])[0]
        checkpoint_seq = struct.unpack(">I", header[12:16])[0]
        salt1 = struct.unpack(">I", header[16:20])[0]
        salt2 = struct.unpack(">I", header[20:24])[0]

        wal_info = {
            "magic": hex(magic),
            "format": file_format,
            "page_size": page_size,
            "checkpoint_sequence": checkpoint_seq,
            "frames": []
        }

        # Parse frames (24-byte header + page_size data each)
        frame_offset = 32
        frame_num = 0
        file_size = os.path.getsize(wal_path)

        while frame_offset + 24 + page_size <= file_size:
            f.seek(frame_offset)
            frame_header = f.read(24)
            page_number = struct.unpack(">I", frame_header[0:4])[0]
            db_size_after = struct.unpack(">I", frame_header[4:8])[0]

            wal_info["frames"].append({
                "frame_number": frame_num,
                "page_number": page_number,
                "db_size_pages": db_size_after,
                "offset": frame_offset
            })
            frame_offset += 24 + page_size
            frame_num += 1

    return wal_info
```

### Method 3: Unallocated Space Within Pages

Deleted cells within active B-tree pages leave data in the unallocated region between the cell pointer array and the cell content area.

```python
def analyze_unallocated_space(db_path: str, page_number: int) -> dict:
    """Analyze unallocated space within a specific B-tree page."""
    with open(db_path, "rb") as f:
        header = f.read(100)
        page_size = struct.unpack(">H", header[16:18])[0]
        if page_size == 1:
            page_size = 65536

        offset = (page_number - 1) * page_size
        f.seek(offset)
        page_data = f.read(page_size)

        # Parse page header (8 or 12 bytes depending on type)
        page_type = page_data[0]
        first_freeblock = struct.unpack(">H", page_data[1:3])[0]
        cell_count = struct.unpack(">H", page_data[3:5])[0]
        cell_content_offset = struct.unpack(">H", page_data[5:7])[0]
        if cell_content_offset == 0:
            cell_content_offset = 65536

        header_size = 12 if page_type in (0x02, 0x05) else 8
        cell_pointer_end = header_size + cell_count * 2

        unallocated_start = cell_pointer_end
        unallocated_end = cell_content_offset
        unallocated_size = unallocated_end - unallocated_start

        return {
            "page_number": page_number,
            "page_type": hex(page_type),
            "cell_count": cell_count,
            "unallocated_start": unallocated_start,
            "unallocated_end": unallocated_end,
            "unallocated_size": unal