Performing Sqlite Database Forensics

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

Trigger phrases:

• "performing sqlite database forensics"

• "Perform forensic analysis of SQLite databases to recover deleted records from fr"

• 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

This section covers sqlite internal structure for performing sqlite database forensics.

• Ensure all prerequisites are met before proceeding
• Follow the documented workflow steps in sequence
• Record results and any anomalies encountered during this phase

Database Header (First 100 Bytes)

Page Types

Deleted Record Recovery

This section covers deleted record recovery for performing sqlite database forensics.

• Ensure all prerequisites are met before proceeding
• Follow the documented workflow steps in sequence
• Record results and any anomalies encountered during this phase

Method 1: Freelist Page Analysis

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

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.

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.

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": unallocated_size,
            "unallocated_data": page_data[unallocated_start:unallocated_end].hex()
        }

Common Forensic Databases

Timestamp Decoding

from datetime import datetime, timedelta

def decode_chrome_timestamp(chrome_ts: int) -> datetime:
    """Convert Chrome/WebKit timestamp to datetime (microseconds since 1601-01-01)."""
    epoch_delta = 11644473600
    return datetime.utcfromtimestamp((chrome_ts / 1000000) - epoch_delta)

def decode_unix_timestamp(unix_ts: int) -> datetime:
    """Convert Unix timestamp to datetime."""
    return datetime.utcfromtimestamp(unix_ts)

def decode_mac_absolute_time(mac_ts: float) -> datetime:
    """Convert Mac Absolute Time (seconds since 2001-01-01)."""
    mac_epoch = datetime(2001, 1, 1)
    return mac_epoch + timedelta(seconds=mac_ts)

def decode_mozilla_timestamp(moz_ts: int) -> datetime:
    """Convert Mozilla PRTime (microseconds since Unix epoch)."""
    return datetime.utcfromtimestamp(moz_ts / 1000000)

When NOT to Use

• You don't have explicit written authorization to test
• Task is about defense/detection, not offense (use detection skills)
• You need to implement security controls (use implementing-* skills)
• Task requires compliance auditing (use auditing-* skills)
• You're investigating an incident (use incident response skills)
• Target is out of scope for your engagement
• Task is about vulnerability scanning only (use scanning tools)

Red Flags

• Performing actions without explicit written authorization from the asset owner
• Testing against production systems without a defined scope and rules of engagement
• Failing to use write-blockers when acquiring forensic evidence
• Not verifying hash integrity before and after imaging
• Modifying original evidence during analysis

Verification

• All steps executed successfully against a test environment before production use
• Output documented with screenshots or logs demonstrating expected behavior
• Hash values computed and verified match between source and image
• Chain of custody log complete with timestamps and examiner names
• Analysis tools and versions documented for reproducibility

References

• SQLite File Format: https://www.sqlite.org/fileformat2.html
• Belkasoft SQLite Analysis: https://belkasoft.com/sqlite-analysis
• Spyder Forensics SQLite Training: https://www.spyderforensics.com/sqlite-forensic-fundamentals-2025/
• Forensic Analysis of Damaged SQLite Databases: https://www.forensicfocus.com/articles/forensic-analysis-of-damaged-sqlite-databases/

Example Output

$ python3 sqlite_forensics.py --db /evidence/chrome/Default/History \
    --wal /evidence/chrome/Default/History-wal \
    --journal /evidence/chrome/Default/History-journal \
    --output /analysis/sqlite_report

SQLite Database Forensic Analyzer v2.0
========================================
Database:    /evidence/chrome/Default/History
Size:        48.2 MB
SQLite Ver:  3.39.5
Page Size:   4096 bytes
Total Pages: 12,345
Encoding:    UTF-8

[+] Analyzing WAL (Write-Ahead Log)...
    WAL file:       History-wal (2.1 MB)
    WAL frames:     512
    Checkpointed:   No (contains uncommitted data)
    Recoverable rows from WAL: 234

[+] Analyzing journal file...
    Journal file:   History-journal (0 bytes - rolled back)

[+] Scanning for deleted records (freelist pages)...
    Freelist pages:     456
    Deleted records recovered: 1,892

[+] Analyzing table: urls
    Active rows:     12,456
    Deleted rows:    1,234 (recovered from freelist)
    WAL-only rows:   89

--- Recovered Deleted URLs (Last 10) ---
Row ID | URL                                              | Title                    | Visit Count | Last Visit (UTC)
-------|--------------------------------------------------|--------------------------|-------------|---------------------
89234  | https://mega.nz/folder/xYz123#key=AbCdEf        | MEGA                     | 5           | 2024-01-16 03:20:00
89235  | https://transfer.sh/abc123/data.7z               | transfer.sh              | 1           | 2024-01-16 03:25:00
89240  | https://temp-mail.org/en/                        | Temp Mail                | 3           | 2024-01-15 13:00:00
89241  | https://browserleaks.com/ip                      | IP Leak Test             | 1           | 2024-01-15 12:55:00
89245  | https://www.virustotal.com/gui/file/a1b2c3...    | VirusTotal               | 2           | 2024-01-15 14:30:00
89250  | https://github.com/gentilkiwi/mimikatz/releases  | Mimikatz Releases        | 1           | 2024-01-15 16:00:00
89260  | https://raw.githubusercontent.com/.../payload.ps1| GitHub Raw               | 1           | 2024-01-15 14:34:00
89270  | https://pastebin.com/edit/kL9mN2pQ               | Pastebin - Edit          | 2           | 2024-01-15 14:42:00
89280  | https://duckduckgo.com/?q=clear+browser+history  | DuckDuckGo               | 1           | 2024-01-17 22:00:00
89285  | https://duckduckgo.com/?q=anti+forensics+tools   | DuckDuckGo               | 1           | 2024-01-17 22:05:00

[+] Analyzing table: downloads
    Active rows:     234
    Deleted rows:    12 (recovered)

--- Recovered Deleted Downloads ---
Row ID | Filename               | URL                                    | Size      | Start Time (UTC)
-------|------------------------|----------------------------------------|-----------|---------------------
5012   | payload.ps1            | https://raw.githubusercontent.com/...  | 4,096     | 2024-01-15 14:34:00
5015   | mimikatz_trunk.zip     | https://github.com/.../releases/...    | 1,892,352 | 2024-01-15 16:00:00
5018   | netscan_portable.zip   | https://www.softperfect.com/...        | 5,242,880 | 2024-01-15 15:05:00

[+] Slack space analysis...
    Pages with slack space data: 234
    Partial strings recovered:   67 fragments

Summary:
  Total records analyzed:  14,578 (active) + 3,126 (deleted/WAL)
  Evidence-relevant URLs:  23 (flagged)
  Deleted downloads:       12 (3 tool-related)
  Anti-forensics evidence: Browser history deletion detected
  Report: /analysis/sqlite_report/sqlite_forensics.json
  Recovered DB: /analysis/sqlite_report/History_recovered.db

Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality

Anti-Rationalization Table