Mohammad Waseem

Posted on Feb 1

Using Python and Open Source Tools to Prevent Spam Trap Email Addressing

#python #security #email

Introduction

Spam traps pose a significant challenge for email marketers and security teams alike. These are addresses intentionally set up by ISPs and blacklist providers to catch malicious or poorly managed email lists. Sending emails to spam traps can damage sender reputation, leading to deliverability issues or even blacklisting. In this post, we'll explore how a security researcher can leverage Python and open source tools to identify potential spam traps in mailing lists, thereby avoiding them proactively.

Understanding Spam Traps

Spam traps are categorized mainly into two types:

Pristine traps: Addresses that were never used for legitimate communication.
Recycled traps: Old addresses that were once active and are now repurposed as traps.

Detecting these addresses early requires analyzing email lists against known sources and performing heuristics to prevent delivery to risky addresses.

The Open Source Approach

Tools like SMTP libraries for connection testing, DNS lookups with dnspython, and data processing with pandas can be combined to create an effective spam trap detection pipeline.

Implementation Steps

Step 1: Gather Known Spam Trap Domains

Many open source projects maintain lists of spam trap domains. For demonstration, we can use a simple list:

spam_trap_domains = ['spamtrap.org', 'trapdomain.com', 'nope.com']

Step 2: Parse Email List

Suppose we have a CSV file with email addresses:

import pandas as pd

df = pd.read_csv('email_list.csv')
# assuming column is named 'email'
 emails = df['email'].dropna().tolist()

Step 3: Domain Extraction and Checking

Extract domains and check against our spam trap list:

def extract_domain(email):
    return email.split('@')[-1].lower()

def is_spam_trap_domain(domain):
    return domain in spam_trap_domains

filtered_emails = []
for email in emails:
    domain = extract_domain(email)
    if is_spam_trap_domain(domain):
        print(f"Potential spam trap detected: {email}")
    else:
        filtered_emails.append(email)

Step 4: DNS Validation

Perform DNS lookups to verify domain presence:

import dns.resolver

def validate_domain(domain):
    try:
        answers = dns.resolver.resolve(domain, 'MX')
        return True
    except (dns.resolver.NoAnswer, dns.resolver.NXDOMAIN, dns.resolver.Timeout):
        return False

# Check if domain resolves
for email in filtered_emails:
    domain = extract_domain(email)
    if not validate_domain(domain):
        print(f"Domain does not resolve: {domain} in email {email}")
        # Consider removing or flagging this email

Step 5: Final Review and Cleanup

By combining domain list checks and DNS validation, security researchers can flag potentially harmful email addresses, avoiding spam traps effectively.

Conclusion

This approach leverages Python’s extensive ecosystem and open source tools to build a lightweight yet powerful spam trap avoidance strategy. It ensures better email reputation management and enhances deliverability, especially for security-conscious environments. Continual updating of spam trap domain lists and DNS validation enhances this method's robustness.

Implementing this pipeline as part of your email hygiene process can prevent costly deliverability issues, safeguarding your infrastructure from malicious traps that threaten your sender reputation. Regularly reviewing and refining your detection rules will yield even better results over time.

🛠️ QA Tip

To test this safely without using real user data, I use TempoMail USA.

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