Back in my early days in the field, I was running a phishing simulation for a massive holding company as part of a penetration test. Probably out of pure excitement, I totally botched the database connection that was supposed to log the credentials on my 'fake' login page. Almost the entire board, including the CEO, 'took the bait.' Links were clicked, usernames and passwords were flying around in the ether, but my database was just returning 'null.' We didn't have a single shred of evidence. That day, I realized social engineering isn't just about 'tricking people'; it requires a rock-solid technical infrastructure and operational discipline. Since then, checking my listeners three times before every test has become a ritual.
In the cybersecurity world, we pour millions of dollars into firewalls, EDRs, and SIEM systems. But at the end of the day, sitting behind all those complex systems is a sleep-deprived 'human' who hasn't finished their morning coffee yet. Social engineering is like an art form that targets that human's weakest (or just most distracted) moment. Today, let’s go behind the scenes and see how things work in a setup like 'testCompany' from a Red Teamer’s perspective, and look at the technical gears turning under the hood.
1. OSINT: Don't Fire Before You Know Your Target
A social engineering attack never starts 'randomly.' First comes the 'OSINT' (Open Source Intelligence) phase. Who does what on LinkedIn? Who are the new hires? What technologies is the company using? Collecting this data is 70% of the job.
For instance, to understand the language used by a company's help desk employees, we follow their LinkedIn posts or the technical error screens they share (yes, they actually do that).
Here is a simulated email harvesting scenario using a simple 'crawler' logic (defanged):
# Simulating potential email list generation based on a target domain
import re
def generate_emails(domain, names):
email_patterns = [
"{first}.{last}@{domain}",
"{f}{last}@{domain}",
"{first}@{domain}"
]
discovered_emails = []
for name in names:
first, last = name.lower().split(' ')
for pattern in email_patterns:
email = pattern.format(first=first, last=last, f=first[0], domain=domain)
discovered_emails.append(email)
return discovered_emails
# Example usage
target_domain = "testCompany.example.com"
employees = ["Ahmet Yilmaz", "Ayse Demir"]
print(generate_emails(target_domain, employees))
We then verify this list using tools like hunter[.]io to decide who is 'authorized' and who might be an 'easy target.'
2. Infrastructure Setup: Building Trust
We can send the email, but if it lands in the 'spam' folder, all that effort goes down the drain. In Red Team operations, one of the areas where we spend the most time is the 'reputation' of our SMTP servers.
We buy a domain like testCompany-support[.]example[.]com. Immediately after, we configure the SPF, DKIM, and DMARC records so the recipient's mail server perceives us as 'legitimate.' Without these technical configurations, even the most convincing email won't survive the spam filters.