What it does. Performs forensic analysis of SQLite databases by examining B-tree page Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).

Install

• npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-sqlite-database-forensics, or copy the skill folder into ~/.claude/skills/performing-sqlite-database-forensics/.
• Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/performing-sqlite-database-forensics/SKILL.md

SKILL.md (verbatim)

name: performing-sqlite-database-forensics
description: Performs forensic analysis of SQLite databases by examining B-tree page
  structures, recovering deleted records from freelist pages and Write-Ahead Log (WAL)
  files, decoding encoded timestamps, and extracting evidence from browser history,
  messaging apps, and mobile device databases. Use when recovering deleted or unallocated
  data from a SQLite database during digital forensics or mobile/browser evidence
  analysis.
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-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)

Page Types

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.

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)

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

Other files in this skill

• LICENSE
• assets/template.md
• references/api-reference.md
• references/standards.md
• references/workflows.md
• scripts/agent.py
• scripts/process.py

assets/template.md (verbatim)

SQLite Database Forensic Analysis Report

Case Information

Database Summary

Tables and Record Counts

Recovered Deleted Records

Findings

(Summary of forensic analysis)

references/api-reference.md (verbatim)

API Reference: SQLite Database Forensics

SQLite File Header (First 100 Bytes)

Page Types

Timestamp Decoders

Common Forensic Databases

Python Libraries

References

• SQLite File Format: https://www.sqlite.org/fileformat2.html
• SQLite WAL Format: https://www.sqlite.org/wal.html
• Belkasoft SQLite Analysis: https://belkasoft.com/sqlite-analysis
• Sanderson Forensics SQLite: https://sqliteforensictoolkit.com/

references/standards.md (verbatim)

Standards and References - SQLite Database Forensics

Standards

• NIST SP 800-86: Guide to Integrating Forensic Techniques
• SQLite File Format Specification: https://www.sqlite.org/fileformat2.html
• SWGDE Best Practices for Mobile Device Forensics

Tools

• DB Browser for SQLite: Open-source GUI editor
• sqlcipher: Encrypted SQLite database handling
• Belkasoft Evidence Center: Commercial SQLite forensic analysis
• Exponent SQLite Explorer: Forensic SQLite viewer with timestamp auto-detection
• FORC (Forensic Operations for Recognizing SQLite Content): Automated Android extraction

Key Database Locations

• Chrome History: %LOCALAPPDATA%\Google\Chrome\User Data\Default\History
• Firefox places.sqlite: %APPDATA%\Mozilla\Firefox\Profiles*.default\places.sqlite
• Android SMS: /data/data/com.android.providers.telephony/databases/mmssms.db
• iOS SMS: /private/var/mobile/Library/SMS/sms.db
• WhatsApp: /data/data/com.whatsapp/databases/msgstore.db

references/workflows.md (verbatim)

Workflows - SQLite Database Forensics

Workflow 1: Complete Database Analysis

Identify SQLite databases in evidence
    |
Create forensic copies (preserve WAL and journal files)
    |
Analyze database header (page size, encoding, freelist)
    |
Query active tables for evidence
    |
Analyze freelist pages for deleted records
    |
Parse WAL file for transaction history
    |
Examine unallocated space within pages
    |
Decode timestamps (Chrome, Unix, Mac Absolute, Mozilla)
    |
Document and export findings

Workflow 2: Deleted Record Recovery

Open database in hex editor
    |
Identify freelist trunk/leaf pages from header
    |
Extract raw page data from freelist
    |
Parse B-tree cell format to decode records
    |
Check WAL for pre-deletion snapshots
    |
Examine unallocated space between cell pointers and content area
    |
Carve recoverable records

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