Project 10: Email Reputation and Blacklist Checker

Build a tool that queries DNSBLs and aggregates reputation signals for IPs and domains.

Quick Reference

Attribute Value
Difficulty Intermediate
Time Estimate 1-2 weeks
Language Python (Alternatives: Go, Rust)
Prerequisites DNS queries, IP parsing
Key Topics DNSBLs, reputation scoring, rate limits

1. Learning Objectives

  1. Query multiple DNSBLs for an IP address.
  2. Parse responses and interpret list meanings.
  3. Aggregate signals into a reputation score.
  4. Produce a clear report with evidence.

2. Theoretical Foundation

2.1 Core Concepts

  • DNSBLs: DNS-based blocklists that return A records for listed IPs.
  • Reverse IP lookup: IP octets are reversed for query (e.g., 1.2.3.4 -> 4.3.2.1).
  • Reputation scoring: Combine multiple signals into a risk score.
  • Rate limiting: Many DNSBLs require throttling or API keys.

2.2 Why This Matters

Reputation is often the deciding factor in deliverability. Even perfectly authenticated mail can be rejected if the sender is listed.

2.3 Historical Context / Background

Blocklists grew out of early spam prevention and remain part of modern filtering pipelines, often combined with ML and user feedback.

2.4 Common Misconceptions

  • Misconception: One DNSBL hit means definite spam. Reality: It is one signal among many.
  • Misconception: DNSBLs are always up-to-date. Reality: Some are stale or overly aggressive.

3. Project Specification

3.1 What You Will Build

A CLI tool that checks an IP or domain against a configurable list of DNSBLs and outputs a reputation score with details.

3.2 Functional Requirements

  1. Accept IP or domain input.
  2. For IPs, query multiple DNSBLs using reversed IP format.
  3. Interpret return codes and list names.
  4. Compute a weighted reputation score.
  5. Output a report with list hits and recommended actions.

3.3 Non-Functional Requirements

  • Performance: Parallel queries with timeouts.
  • Reliability: Handle NXDOMAIN and timeouts gracefully.
  • Usability: Clear summary of risk and evidence.

3.4 Example Usage / Output

$ ./reputation-check 203.0.113.10
Reputation Score: 65/100 (Medium Risk)
Hits:
  zen.spamhaus.org: LISTED (policy)
  bl.spamcop.net: not listed
  b.barracudacentral.org: timeout
Recommendation: investigate outbound traffic, request delisting if clean

3.5 Real World Outcome

You can quickly determine whether a sending IP is likely to be blocked and why, and provide actionable remediation advice.

4. Solution Architecture

4.1 High-Level Design

Input Parser
  -> DNSBL Query Engine
  -> Response Interpreter
  -> Score Aggregator
  -> Report Generator

4.2 Key Components

Component Responsibility Key Decisions
Query Engine Build DNSBL names Reverse IP correctly
Interpreter Parse A/TXT replies Map to list meaning
Scoring Weight list hits Configurable weights
Reporter Output summary Include evidence and hints

4.3 Data Structures

class ListHit:
    def __init__(self, list_name, status, detail):
        self.list_name = list_name
        self.status = status
        self.detail = detail

4.4 Algorithm Overview

Key Algorithm: DNSBL Check

  1. Reverse IP octets.
  2. Append DNSBL zone.
  3. Query A record and TXT record.
  4. Interpret response.

Complexity Analysis:

  • Time: O(n) for n lists
  • Space: O(n)

5. Implementation Guide

5.1 Development Environment Setup

python -m venv .venv
source .venv/bin/activate

5.2 Project Structure

reputation-check/
├── lists.yml
├── query.py
├── score.py
└── report.py

5.3 The Core Question You’re Answering

“Is this sender trusted by the email ecosystem, and if not, why?”

5.4 Concepts You Must Understand First

Stop and research these before coding:

  1. DNSBL query format
  2. NXDOMAIN vs listed
  3. Timeout handling
  4. Weighted scoring

5.5 Questions to Guide Your Design

  1. Which DNSBLs will you include by default?
  2. How will you handle lists that require API keys?
  3. How will you avoid hammering DNS servers?

5.6 Thinking Exercise

If one list reports a hit and three others do not, how should that affect your reputation score?

5.7 The Interview Questions They’ll Ask

  1. “How do DNSBL lookups work?”
  2. “Why are DNSBLs only one part of reputation?”
  3. “What are the risks of relying on a single list?”

5.8 Hints in Layers

Hint 1: Add timeouts

  • Some DNSBLs are slow or down.

Hint 2: Use a config file

  • Store list names and weights.

Hint 3: Provide evidence

  • Include which list caused the score change.

5.9 Books That Will Help

Topic Book Chapter
DNS TCP/IP Illustrated Vol 1 Ch. 11
Email reputation Email marketing guides deliverability section

5.10 Implementation Phases

Phase 1: Foundation (3-4 days)

Goals:

  • Query a single DNSBL

Tasks:

  1. Reverse IP and query DNS.
  2. Interpret NXDOMAIN vs listed.

Checkpoint: Correctly detect a known test IP.

Phase 2: Core Functionality (4-5 days)

Goals:

  • Multiple lists and scoring

Tasks:

  1. Query multiple DNSBLs with timeouts.
  2. Aggregate into a score.

Checkpoint: Report list hits and score.

Phase 3: Polish and Edge Cases (2-3 days)

Goals:

  • Domain checks and reporting

Tasks:

  1. Add basic domain reputation checks.
  2. Output recommendation text.

Checkpoint: Full report with suggestions.

5.11 Key Implementation Decisions

Decision Options Recommendation Rationale
Query method sequential vs parallel parallel with timeout Faster overall
Output plain vs JSON both For automation
Scoring fixed vs configurable configurable Different contexts

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit Tests Reverse IP 1.2.3.4 -> 4.3.2.1
Integration Tests Known lists test IPs
Edge Case Tests Timeout simulate DNS failure

6.2 Critical Test Cases

  1. NXDOMAIN treated as not listed.
  2. Timeout yields unknown status.
  3. Multiple hits increase risk score.

6.3 Test Data

127.0.0.2 -> listed in many DNSBL test zones

7. Common Pitfalls and Debugging

7.1 Frequent Mistakes

Pitfall Symptom Solution
Wrong reverse format always not listed reverse octets correctly
No timeout tool hangs enforce DNS timeouts
Over-weight one list false alarms balance weights

7.2 Debugging Strategies

  • Compare results with mxtoolbox or dnsbl.info.
  • Log raw DNS responses.

7.3 Performance Traps

  • Querying too many lists serially. Use concurrency.

8. Extensions and Challenges

8.1 Beginner Extensions

  • Add cached results with TTL.
  • Support CIDR ranges.

8.2 Intermediate Extensions

  • Add reputation feeds with API keys.
  • Build a daily report.

8.3 Advanced Extensions

  • Correlate with outbound volume metrics.
  • Build automated delisting workflow.

9. Real-World Connections

9.1 Industry Applications

  • Email ops teams monitor DNSBL status daily.
  • Security gateways use reputation scoring in filtering.
  • rspamd: https://rspamd.com/ - spam filtering with reputation
  • mailq tools in MTAs

9.3 Interview Relevance

  • DNS and reputation are common email infrastructure interview topics.

10. Resources

10.1 Essential Reading

  • DNSBL documentation for each list

10.2 Video Resources

  • Deliverability and reputation talks

10.3 Tools and Documentation

  • dig for manual DNSBL checks

11. Self-Assessment Checklist

11.1 Understanding

  • I can explain DNSBL lookup format
  • I understand reputation scoring basics
  • I can interpret list responses

11.2 Implementation

  • Tool queries multiple DNSBLs
  • Handles timeouts and NXDOMAIN
  • Produces a clear report

11.3 Growth

  • I can recommend remediation based on results
  • I can tune scoring for different contexts

12. Submission / Completion Criteria

Minimum Viable Completion:

  • Query one DNSBL and report listing

Full Completion:

  • Query multiple lists with scoring and reporting

Excellence (Going Above and Beyond):

  • Add caching and advanced reputation feeds

This guide was generated from EMAIL_SYSTEMS_DEEP_DIVE_PROJECTS.md. For the complete learning path, see the parent directory.