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Anthropic-Cybersecurity-Skills/skills/performing-sqlite-database-forensics/SKILL.md
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mukul975 efca3ec611 feat: add NIST CSF 2.0 nist_csf field to all 754 cybersecurity skills 
Mapped every skill to NIST CSF 2.0 subcategory IDs (GV/ID/PR/DE/RS/RC functions)
based on subdomain and content analysis. Restores 11 skills corrupted during
prior rebase, re-enriching with ATLAS, D3FEND, NIST AI RMF, and CSF 2.0 fields.

All 754 skills now carry structured mappings for all 5 security frameworks:
- MITRE ATT&CK (in tags)
- MITRE ATLAS v5.5 (atlas_techniques)
- MITRE D3FEND v1.3 (d3fend_techniques)
- NIST AI RMF 1.0 (nist_ai_rmf)
- NIST CSF 2.0 (nist_csf)
2026-04-06 11:17:40 +02:00

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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
---
# 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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