Python for Social Engineering Tests: Automate Ethical Hacking Safely

Imagine this: You’re a cybersecurity consultant hired to test a company’s defenses. Instead of manually crafting phishing emails or cold-calling employees, you write a Python script that automates the entire process—sending simulated phishing links, tracking who clicks them, and even analyzing responses. Within hours, you have a detailed report on vulnerabilities, all without breaking a sweat.

This is the power of Python in social engineering tests—a hacker’s favorite tactic, now supercharged with automation. But remember: Always have consent! Ethical hacking is about strengthening security, not exploiting it.

In this guide, we’ll explore:

1. What Social Engineering Tests Are (And Why Python Helps)
2. Python Libraries for Phishing, OSINT, and Behavior Analysis
3. Automating Ethical Tests (With Code Snippets)
4. The Golden Rule: Ethics & Legal Compliance

1. What Are Social Engineering Tests?

Social engineering (SE) is the art of manipulating people into revealing sensitive information—think phishing emails, fake tech support calls, or baiting USB drops. Companies run SE tests to:

• Identify weak points (e.g., employees who fall for scams).
• Train staff to recognize attacks.
• Improve security policies.

Python shines here because it can:
✔ Automate repetitive tasks (e.g., sending 1000+ phishing emails).
✔ Analyze data (e.g., tracking click-through rates).
✔ Integrate with APIs (e.g., SMS gateways for fake "verification" texts).

2. Python Libraries for Social Engineering

Here’s a toolkit for ethical SE testing:

A. Phishing Simulations

• smtplib + email: Send bulk phishing emails with custom templates.
• pywhatkit: Automate WhatsApp messages (e.g., "Urgent: Click this link!").
• phonenumbers: Validate and format phone numbers for SMS tests.

B. Open-Source Intelligence (OSINT)

• requests + BeautifulSoup: Scrape public data (e.g., LinkedIn profiles).
• twint: Scrape Twitter for employee mentions.
• holehe: Check if an email is linked to social accounts.

C. Behavior Analysis

• pandas: Analyze which departments click phishing links most.
• matplotlib: Visualize attack success rates.

3. Automating Ethical Tests (With Code)

Example 1: Phishing Email Simulator

import smtplib
from email.mime.text import MIMEText

def send_phish_email(target, subject, body):
    msg = MIMEText(body)
    msg['Subject'] = subject
    msg['From'] = 'trusted_sender@company.com'
    msg['To'] = target

    with smtplib.SMTP('smtp.gmail.com', 587) as server:
        server.starttls()
        server.login('your_email@gmail.com', 'app_password')
        server.send_message(msg)

# Usage (for authorized tests only!)
send_phish_email(
    target="employee@test.com",
    subject="URGENT: Password Reset Required",
    body="Click here to secure your account: [MALICIOUS LINK]"
)

Example 2: WhatsApp Phishing Alert

import pywhatkit as kit

# Send a WhatsApp message (must have the app open)
kit.sendwhatmsg_instantly(
    phone_no="+1234567890", 
    message="Your account has been compromised. Verify here: [FAKE LINK]",
    wait_time=15
)

Example 3: OSINT Email Checker

import holehe

def check_email_breaches(email):
    results = holehe.email(email)
    for site, data in results.items():
        if data["exists"]:
            print(f"[!] {email} found on {site}")

check_email_breaches("target@company.com")

4. The Golden Rule: Ethics & Legal Compliance

⚠️ Never run SE tests without written permission. Unauthorized hacking is illegal, even with good intentions.

Best Practices:

• Get explicit consent from the organization.
• Define clear scope (e.g., "Only test these 10 employees").
• Anonymize data in reports.
• Use a sandbox environment (don’t test on real systems).

Final Thoughts

Python turns social engineering tests from tedious manual work into a scalable, data-driven process. Whether you’re a security pro or a developer exploring ethical hacking, automation lets you uncover vulnerabilities faster—while keeping your work ethical.

Have you ever run a social engineering test? Share your experiences (and favorite Python tools) in the comments!

🚀 Call to Action

Want to dive deeper? Try:

• Automating a mock phishing campaign (with consent!).
• Experimenting with phonenumbers to validate targets.
• Reading "Social Engineering: The Science of Human Hacking" by Christopher Hadnagy.

Remember: With great power comes great responsibility. Happy (ethical) hacking!