
Analyzing Browser Forensics
FreeExtract and correlate browser data for investigations.
Free · Opens the source repo
What Analyzing Browser Forensics does
Analyzing Browser Forensics with Hindsight is a specialized skill designed for security professionals and forensic analysts who need to investigate user web activity through detailed browser data. This tool leverages Hindsight, an open-source application, to parse various artifacts from Chromium-based browsers, including Google Chrome, Microsoft Edge, Brave, Opera, and Vivaldi. By extracting data such as browsing history, cookies, downloads, and saved passwords, it enables users to create a unified timeline of web activity, which is crucial during incident response and investigations.
The skill supports multiple output formats, including XLSX, JSON, and SQLite, making it adaptable for different analysis needs. Users can easily run the tool via command line or through a web interface, allowing for flexibility in how they handle and visualize the extracted data. The comprehensive nature of the data collected—from URL visits to cached content—ensures that analysts can reconstruct user behavior effectively, which is vital for understanding potential insider threats or criminal activities.
This tool is particularly useful for Security Operations Center (SOC) analysts and incident responders who require structured procedures for browser forensic analysis. It provides a systematic way to validate security monitoring coverage and build detection rules for threat hunting. By utilizing the Hindsight tool, analysts can streamline their investigations and gain insights that would be difficult to obtain through manual analysis of browser profiles.
For those involved in cybersecurity, this skill is an essential addition to their toolkit, facilitating thorough investigations into user behavior and enhancing overall incident response capabilities.
When to use it
Use this skill when you need to analyze browser profiles during security incidents or investigations.
When not to use it
This skill is not suitable for analyzing non-Chromium-based browsers or when browser data is encrypted at the OS level.
What you can build with it
Incident Response Analysis
Use this skill to extract and analyze browser data during security incidents to understand user actions.
Insider Threat Investigations
Employ Hindsight to correlate browsing history and downloads when investigating potential insider threats.
Criminal Case Support
Utilize the unified timeline generated by Hindsight to support criminal investigations involving web activity.
How to install Analyzing Browser Forensics
View source1. Install with the skills CLI
npx skills add mukul975/anthropic-cybersecurity-skills/analyzing-browser-forensics-with-hindsight --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by mukul975Analyzing Browser Forensics with Hindsight
Overview
Hindsight is an open-source browser forensics tool designed to parse artifacts from Google Chrome and other Chromium-based browsers (Microsoft Edge, Brave, Opera, Vivaldi). It extracts and correlates data from multiple browser database files to create a unified timeline of web activity. Hindsight can parse URLs, download history, cache records, bookmarks, autofill records, saved passwords, preferences, browser extensions, HTTP cookies, Local Storage (HTML5 cookies), login data, and session/tab information. The tool produces chronological timelines in multiple output formats (XLSX, JSON, SQLite) that enable investigators to reconstruct user web activity for incident response, insider threat investigations, and criminal cases.
When to Use
- When investigating security incidents that require analyzing browser forensics with hindsight
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Python 3.8+ with Hindsight installed (
pip install pyhindsight) - Access to browser profile directories from forensic image
- Browser profile data (not encrypted with OS-level encryption)
- Timeline Explorer or spreadsheet application for analysis
Browser Profile Locations
| Browser | Windows Profile Path |
|---|---|
| Chrome | %LOCALAPPDATA%\Google\Chrome\User Data\Default\ |
| Edge | %LOCALAPPDATA%\Microsoft\Edge\User Data\Default\ |
| Brave | %LOCALAPPDATA%\BraveSoftware\Brave-Browser\User Data\Default\ |
| Opera | %APPDATA%\Opera Software\Opera Stable\ |
| Vivaldi | %LOCALAPPDATA%\Vivaldi\User Data\Default\ |
| Chrome (macOS) | ~/Library/Application Support/Google/Chrome/Default/ |
| Chrome (Linux) | ~/.config/google-chrome/Default/ |
Key Artifact Files
| File | Contents |
|---|---|
| History | URL visits, downloads, keyword searches |
| Cookies | HTTP cookies with domain, expiry, values |
| Web Data | Autofill entries, saved credit cards |
| Login Data | Saved usernames/passwords (encrypted) |
| Bookmarks | JSON bookmark tree |
| Preferences | Browser configuration and extensions |
| Local Storage/ | HTML5 Local Storage per domain |
| Session Storage/ | Session-specific storage per domain |
| Network Action Predictor | Previously typed URLs |
| Shortcuts | Omnibox shortcuts and predictions |
| Top Sites | Frequently visited sites |
Running Hindsight
Command Line
# Basic analysis of a Chrome profile
hindsight.exe -i "C:\Evidence\Users\suspect\AppData\Local\Google\Chrome\User Data\Default" -o C:\Output\chrome_analysis
# Specify browser type
hindsight.exe -i "/path/to/profile" -o /output/analysis -b Chrome
# JSON output format
hindsight.exe -i "C:\Evidence\Chrome\Default" -o C:\Output\chrome --format jsonl
# With cache parsing (slower but more complete)
hindsight.exe -i "C:\Evidence\Chrome\Default" -o C:\Output\chrome --cache
Web UI
# Start Hindsight web interface
hindsight_gui.exe
# Navigate to http://localhost:8080
# Upload or point to browser profile directory
# Configure output format and analysis options
# Generate and download report
Artifact Analysis Details
URL History and Visits
-- Chrome History database schema (key tables)
-- urls table: id, url, title, visit_count, typed_count, last_visit_time
-- visits table: id, url, visit_time, from_visit, transition, segment_id
-- Timestamps are Chrome/WebKit format: microseconds since 1601-01-01
-- Convert: datetime((visit_time/1000000)-11644473600, 'unixepoch')
Download History
-- downloads table: id, current_path, target_path, start_time, end_time,
-- received_bytes, total_bytes, state, danger_type, interrupt_reason,
-- url, referrer, tab_url, mime_type, original_mime_type
Cookie Analysis
-- cookies table: creation_utc, host_key, name, value, encrypted_value,
-- path, expires_utc, is_secure, is_httponly, last_access_utc,
-- has_expires, is_persistent, priority, samesite
Python Analysis Script
import sqlite3
import os
import json
import sys
from datetime import datetime, timedelta
CHROME_EPOCH = datetime(1601, 1, 1)
def chrome_time_to_datetime(chrome_ts: int):
"""Convert Chrome timestamp to datetime."""
if chrome_ts == 0:
return None
try:
return CHROME_EPOCH + timedelta(microseconds=chrome_ts)
except (OverflowError, OSError):
return None
def analyze_chrome_history(profile_path: str, output_dir: str) -> dict:
"""Analyze Chrome History database for forensic evidence."""
history_db = os.path.join(profile_path, "History")
if not os.path.exists(history_db):
return {"error": "History database not found"}
os.makedirs(output_dir, exist_ok=True)
conn = sqlite3.connect(f"file:{history_db}?mode=ro", uri=True)
# URL visits with timestamps
cursor = conn.cursor()
cursor.execute("""
SELECT u.url, u.title, v.visit_time, u.visit_count,
v.transition & 0xFF as transition_type
FROM visits v JOIN urls u ON v.url = u.id
ORDER BY v.visit_time DESC LIMIT 5000
""")
visits = [{
"url": r[0], "title": r[1],
"visit_time": str(chrome_time_to_datetime(r[2])),
"total_visits": r[3], "transition": r[4]
} for r in cursor.fetchall()]
# Downloads
cursor.execute("""
SELECT target_path, tab_url, start_time, end_time,
received_bytes, total_bytes, mime_type, state
FROM downloads ORDER BY start_time DESC LIMIT 1000
""")
downloads = [{
"path": r[0], "source_url": r[1],
"start_time": str(chrome_time_to_datetime(r[2])),
"end_time": str(chrome_time_to_datetime(r[3])),
"received_bytes": r[4], "total_bytes": r[5],
"mime_type": r[6], "state": r[7]
} for r in cursor.fetchall()]
# Keyword searches
cursor.execute("""
SELECT k.term, u.url, k.url_id
FROM keyword_search_terms k JOIN urls u ON k.url_id = u.id
ORDER BY u.last_visit_time DESC LIMIT 1000
""")
searches = [{"term": r[0], "url": r[1]} for r in cursor.fetchall()]
conn.close()
report = {
"analysis_timestamp": datetime.now().isoformat(),
"profile_path": profile_path,
"total_visits": len(visits),
"total_downloads": len(downloads),
"total_searches": len(searches),
"visits": visits,
"downloads": downloads,
"searches": searches
}
report_path = os.path.join(output_dir, "browser_forensics.json")
with open(report_path, "w") as f:
json.dump(report, f, indent=2)
return report
def main():
if len(sys.argv) < 3:
print("Usage: python process.py <chrome_profile_path> <output_dir>")
sys.exit(1)
analyze_chrome_history(sys.argv[1], sys.argv[2])
if __name__ == "__main__":
main()
References
- Hindsight GitHub: https://github.com/obsidianforensics/hindsight
- Chrome Forensics Guide: https://allenace.medium.com/hindsight-chrome-forensics-made-simple-425db99fa5ed
- Browser Forensics Tools: https://www.cyberforensicacademy.com/blog/browser-forensics-tools-how-to-extract-user-activity
- Chromium Source (History): https://source.chromium.org/chromium/chromium/src/+/main:components/history/
Example Output
$ python hindsight.py -i /evidence/chrome-profile -o /analysis/hindsight_output
Hindsight v2024.01 - Chrome/Chromium Browser Forensic Analysis
================================================================
Profile: /evidence/chrome-profile (Chrome 120.0.6099.130)
OS: Windows 10
[+] Parsing History database...
URL records: 12,456
Download records: 234
Search terms: 567
[+] Parsing Cookies database...
Cookie records: 8,923
Encrypted cookies: 6,712
[+] Parsing Web Data (Autofill)...
Autofill entries: 1,234
Credit card entries: 2 (encrypted)
[+] Parsing Login Data...
Saved credentials: 45 (encrypted)
[+] Parsing Bookmarks...
Bookmark entries: 189
--- Browsing History (Last 10 Entries) ---
Timestamp (UTC) | URL | Title | Visit Count
2024-01-15 14:32:05.123 | https://mail.corporate.com/inbox | Corporate Mail | 45
2024-01-15 14:33:12.456 | https://drive.google.com/file/d/1aBcDe... | Q4_Financial_Report.xlsx | 1
2024-01-15 14:35:44.789 | https://mega.nz/folder/xYz123 | MEGA - Secure Cloud | 3
2024-01-15 14:36:01.234 | https://mega.nz/folder/xYz123#upload | MEGA - Upload | 8
2024-01-15 14:42:15.567 | https://pastebin.com/raw/kL9mN2pQ | Pastebin (raw) | 1
2024-01-15 15:01:33.890 | https://192.168.1.50:8443/admin | Admin Panel | 12
2024-01-15 15:15:22.111 | https://transfer.sh/upload | transfer.sh | 2
2024-01-15 15:30:45.222 | https://vpn-gateway.corporate.com | VPN Login | 5
2024-01-15 16:00:00.333 | https://whatismyipaddress.com | What Is My IP | 1
2024-01-15 16:05:12.444 | https://protonmail.com/inbox | ProtonMail | 3
--- Downloads (Suspicious) ---
Timestamp (UTC) | Filename | URL Source | Size
2024-01-15 14:33:15.000 | Q4_Financial_Report.xlsm | https://phish-domain.com/docs/report | 245 KB
2024-01-15 14:34:02.000 | update_client.exe | https://cdn.evil-updates.com/client.exe | 1.2 MB
--- Cookies (Session Tokens) ---
Domain | Name | Expires | Secure | HttpOnly
.corporate.com | SESSION_ID | 2024-01-16 14:32 | Yes | Yes
.mega.nz | session | Session | Yes | Yes
.protonmail.com | AUTH-TOKEN | 2024-02-15 00:00 | Yes | Yes
Report saved to: /analysis/hindsight_output/Hindsight_Report.xlsx
Frequently asked questions about Analyzing Browser Forensics
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