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name: performing-sqlite-database-forensics description: Perform forensic analysis of SQLite databases to recover deleted records from freelists and WAL files, decode encoded timestamps, and extract evidence from browser history, messaging apps, and mobile device databases. domain: cybersecurity subdomain: digital-forensics tags:

sqlite
database-forensics
freelist
wal
write-ahead-log
browser-history
mobile-forensics
deleted-records
b-tree
unallocated-space

version: '1.0' author: mahipal license: Apache-2.0 nist_csf:

RS.AN-01
RS.AN-03
DE.AE-02
RS.MA-01

mitre_attack:

T1005
T1074
T1119
T1070
T1059

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

Common Forensic Databases

Application	Database File	Key Tables
Chrome	History	urls, visits, downloads, keyword_search_terms
Firefox	places.sqlite	moz_places, moz_historyvisits
Safari	History.db	history_items, history_visits
WhatsApp	msgstore.db	messages, chat_list
Signal	signal.sqlite	sms, mms
iMessage	sms.db	message, handle, chat
Android SMS	mmssms.db	sms, mms, threads
Skype	main.db	Messages, Conversations

Timestamp Decoding

python

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)

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

text

$ 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

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