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Introduction:
Open-Source Intelligence (OSINT) is a cornerstone of red team engagements, enabling attackers to gather critical employee data for social engineering and phishing campaigns. LeakedIn, a tool developed by security researcher Nikita Makarov, enhances this process by automating employee discovery from LinkedIn while evading detection through human-like web parsing. The recent v1.10 update introduces customizable email format options, allowing red teamers to generate precise email lists for targeted attacks, thereby streamlining the reconnaissance phase.
Learning Objectives:
- Understand the role of LeakedIn in automating OSINT for red team reconnaissance and how it differs from traditional API-based tools.
- Learn to configure LeakedIn’s new email format customization features for effective email guessing and employee targeting.
- Master techniques for integrating LeakedIn into a comprehensive red team workflow, including command-line usage and evasion strategies.
You Should Know:
1. The OSINT Landscape and LeakedIn’s Unique Approach
LeakedIn is designed for red teamers to extract employee information from LinkedIn without triggering account bans, unlike recruiter-focused tools that spam APIs. It parses frontend HTML and simulates human browsing by iterating through search pages slowly. This method reduces the risk of IP blocks or CAPTCHAs, making it a stealthier option for gathering data like names, job titles, and now, customizable email addresses. To use it, you must first access the GitHub repository (https://github.com/nikitaromanov/LeakedIn) and set it up in a controlled environment.
Step-by-step guide:
- Clone the repository from GitHub using: `git clone https://github.com/nikitaromanov/LeakedIn.git`
– Navigate to the directory: `cd LeakedIn` - Install Python dependencies: `pip install -r requirements.txt` (ensure Python 3.8+ is installed on Linux or Windows).
- Run the tool with basic options: `python leakedin.py -h` to view help menu and understand parameters like search queries and output formats.
2. Setting Up LeakedIn: Environment Configuration and Dependencies
Proper setup ensures LeakedIn runs smoothly across operating systems. On Linux, use virtual environments to isolate dependencies; on Windows, ensure Python path is set. The tool relies on libraries like `requests` and `beautifulsoup4` for web scraping, which must be installed securely to avoid conflicts.
Step-by-step guide:
- For Linux (Kali/Ubuntu), create a virtual environment:
python3 -m venv leakedin_env source leakedin_env/bin/activate pip install requests beautifulsoup4 lxml
- For Windows, open PowerShell as administrator and install dependencies:
py -m pip install --upgrade pip py -m pip install requests beautifulsoup4
- Verify installation by running a test scan: `python leakedin.py -q “company name” -o output.txt` to fetch employee data from LinkedIn search results.
- Customizing Email Formats for Precision Targeting in v1.10
The v1.10 update allows tweaking email guess patterns by adjusting character counts and swapping name orders (e.g., [email protected] vs. [email protected]). This is crucial for phishing campaigns, as email guessing often relies on common corporate formats. LeakedIn now lets you define these patterns via command-line arguments or configuration files.
Step-by-step guide:
- Use the `-e` flag to specify email format: `python leakedin.py -q “Target Corp” -e [email protected] -c 10` where `-c` limits characters for name truncation.
- To swap name order, add `–reverse-names` flag: `python leakedin.py -q “Target Corp” -e [email protected] –reverse-names`
– For batch processing, create a config file `email_formats.json` with entries like `{“format”: “[email protected]”, “max_length”: 8}` and reference it in commands.
4. Human-Like Parsing: Evading LinkedIn’s Anti-Scraping Measures
LeakedIn avoids detection by adding delays between requests, randomizing user-agents, and handling cookies like a browser. This mimics human behavior, reducing the chance of LinkedIn flagging the activity. You can configure these settings in the script or via environment variables.
Step-by-step guide:
- Edit the `leakedin.py` script to adjust delay times (default is 2-5 seconds): Look for `time.sleep(random.uniform(2, 5))` and modify as needed.
- Rotate user-agents by importing a list from a file: Create `user_agents.txt` and update the script to read from it randomly.
- Use proxy support to mask IP addresses: Add `–proxy http://proxy-ip:port` to commands, or set up a proxy chain with tools like `proxychains` on Linux.
- Monitor logs for errors: `tail -f leakedin.log` on Linux or use PowerShell’s `Get-Content` on Windows to ensure stealth.
- Integrating LeakedIn with Red Team Tools for Comprehensive Recon
Combine LeakedIn with other OSINT tools like `theHarvester` or `Hunter.io` to validate emails and enrich data. For cloud hardening assessments, feed outputs into vulnerability scanners or phishing platforms like Gophish. Automation scripts can streamline this integration.
Step-by-step guide:
- On Linux, use a bash script to run LeakedIn and then theHarvester:
!/bin/bash python leakedin.py -q "$1" -o employees.txt theHarvester -d "$1" -b google -f harvester_output.html
- On Windows, use PowerShell to chain commands:
python leakedin.py -q "Target" -o emps.csv Import-Csv emps.csv | ForEach-Object { Test-NetConnection -ComputerName $_.Email -Port 25 } - For API security, use gathered emails to test OAuth endpoints with tools like `Postman` or
Burp Suite.
6. Defensive Measures: Mitigating OSINT Risks for Organizations
Organizations can protect against such tools by implementing security controls on LinkedIn profiles, monitoring for scraping activity, and educating employees. Technical mitigations include web application firewalls (WAFs) and rate limiting.
Step-by-step guide:
- Configure LinkedIn privacy settings: Encourage employees to limit public visibility of their profiles and use anonymous browsing modes.
- Deploy WAF rules to block suspicious scrapers: On cloud platforms like AWS, use AWS WAF to set rate-based rules for LinkedIn domains.
- Set up logging and alerts: Use SIEM tools like Splunk to detect unusual traffic patterns from tools like LeakedIn, with queries like
index=web_logs user_agent=python. - Conduct regular OSINT audits: Run self-assessments with tools like `LinkedInt` to identify exposed data and patch gaps.
- Advanced Techniques: Leveraging LeakedIn for Social Engineering Campaigns
Use LeakedIn’s output to craft targeted phishing emails or pretexting calls. Combine with vulnerability exploitation frameworks like Metasploit for full-chain attacks. Always operate within authorized engagement boundaries.
Step-by-step guide:
- Parse LeakedIn output files (e.g., CSV) to extract names and emails: Use Python scripts to generate personalized phishing templates.
- On Linux, use `sed` and `awk` to format data: `awk -F, ‘{print “Dear ” $1 “, …”}’ employees.txt > phishing_emails.txt`
– For cloud hardening, simulate attacks via services like Azure AD: Use `MailSniper` to test email security with commands: `Invoke-DomainHarvest -Domain target.com -OutputFile targets.txt`
– Document findings for reports: Include commands and outputs to demonstrate risk, such as `leakedin.py` runs with timestamps.
What Undercode Say:
- Key Takeaway 1: LeakedIn’s human-like parsing sets a new standard for stealth in OSINT tools, enabling red teams to gather employee data without triggering alarms, which is critical for realistic security assessments.
- Key Takeaway 2: The email format customization in v1.10 enhances phishing precision by allowing attackers to mimic corporate naming conventions, reducing guesswork and increasing success rates.
Analysis: LeakedIn reflects a growing trend towards automation in red teaming, where tools evolve to bypass platform defenses. While it aids ethical hackers, it also lowers the barrier for malicious actors, emphasizing the need for robust defensive strategies. Organizations must balance LinkedIn’s utility with security, as OSINT remains a prime attack vector. The tool’s open-source nature encourages community improvements but also requires continuous updates to counter LinkedIn’s anti-scraping measures.
Prediction:
In the future, tools like LeakedIn will incorporate AI to predict email formats and automate social engineering at scale, making reconnaissance faster and more accurate. This will force platforms like LinkedIn to enhance detection with machine learning-based defenses, leading to an arms race in OSINT techniques. Consequently, red team engagements will become more sophisticated, requiring organizations to adopt advanced monitoring and employee training to mitigate risks. Cloud and API security will also tighten, with a focus on limiting public data exposure without hindering legitimate use.
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