analyzing-browser-forensics-with-hindsight▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Analyze Chromium-based browser artifacts using Hindsight to extract browsing history, downloads, cookies, cached content, autofill data, saved passwords, and browser extensions from Chrome, Edge, Brave, and Opera for forensic investigation.
| name | analyzing-browser-forensics-with-hindsight |
| description | Analyze Chromium-based browser artifacts using Hindsight to extract browsing history, downloads, cookies, cached content, autofill data, saved passwords, and browser extensions from Chrome, Edge, Brave, and Opera for forensic investigation. |
| domain | cybersecurity |
| subdomain | digital-forensics |
| tags | - browser-forensics - hindsight - chrome-forensics - chromium - edge - browsing-history - cookies - downloads - cache - web-artifacts |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - RS.AN-01 - RS.AN-03 - DE.AE-02 - RS.MA-01 |
Analyzing 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
How to use analyzing-browser-forensics-with-hindsight on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add analyzing-browser-forensics-with-hindsight
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches analyzing-browser-forensics-with-hindsight from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate analyzing-browser-forensics-with-hindsight. Access the skill through slash commands (e.g., /analyzing-browser-forensics-with-hindsight) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★58 reviews- ★★★★★Dhruvi Jain· Dec 28, 2024
Keeps context tight: analyzing-browser-forensics-with-hindsight is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Yusuf Reddy· Dec 24, 2024
Useful defaults in analyzing-browser-forensics-with-hindsight — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Yusuf Park· Dec 20, 2024
analyzing-browser-forensics-with-hindsight has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Yusuf Sethi· Dec 16, 2024
We added analyzing-browser-forensics-with-hindsight from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Diego Thomas· Dec 12, 2024
I recommend analyzing-browser-forensics-with-hindsight for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Maya Sanchez· Dec 8, 2024
Keeps context tight: analyzing-browser-forensics-with-hindsight is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Diego Tandon· Dec 4, 2024
analyzing-browser-forensics-with-hindsight is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Yusuf Shah· Nov 27, 2024
Registry listing for analyzing-browser-forensics-with-hindsight matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Kofi Jain· Nov 23, 2024
analyzing-browser-forensics-with-hindsight reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Oshnikdeep· Nov 19, 2024
Registry listing for analyzing-browser-forensics-with-hindsight matched our evaluation — installs cleanly and behaves as described in the markdown.
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