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.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
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.
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.
import struct
import sqlite3
import os
defanalyze_freelist(db_path: str) -> dict:
"""Analyze SQLite freelist to identify pages containing deleted data."""withopen(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 inrange(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
defextract_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)
withopen(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")
withopen(output_file, "wb") as out:
out.write(page_data)
returnlen(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.
