# Analyzing Email Headers For Phishing Investigation

> Use when parse and analyze email headers to trace the origin of phishing emails, verify sender authenticity, and identify spoofing through SPF, DKIM, and DMARC validation. Use when working with analyzing email headers for phishing investigation.

- Skill: `oyi77/analyzing-email-headers-for-phishing-investigation` (Agent Skill)
- Install (CLI): `npx skillmds add oyi77/analyzing-email-headers-for-phishing-investigation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/oyi77/analyzing-email-headers-for-phishing-investigation/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- License: Apache-2.0
- Author: oyi77 (https://skillmd.com/u/oyi77)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/oyi77/analyzing-email-headers-for-phishing-investigation

---


# Analyzing Email Headers For Phishing Investigation

## Overview

Cybersecurity skill for analyzing email headers for phishing investigation. Follows industry best practices and security standards.

## When to Use
**Trigger phrases:**
- "analyzing email headers for phishing investigation"
- "Parse and analyze email headers to trace the origin of phishing emails, verify s"

- When investigating a suspected phishing email to determine its true origin
- For verifying sender authenticity and detecting email spoofing
- During incident response when a user has clicked a phishing link
- When tracing the delivery path and relay servers of a suspicious email
- For validating SPF, DKIM, and DMARC alignment to identify forgery


## When NOT to Use

- When you lack proper authorization for testing
- For production systems without change management
- When the task requires legal or compliance expertise beyond technical scope


## Prerequisites
- Raw email headers from the suspicious message (EML or MSG format)
- Understanding of SMTP protocol and email header fields
- Access to DNS lookup tools (dig, nslookup) for SPF/DKIM/DMARC verification
- Email header analysis tools (MHA, emailheaders.net concepts)
- Python with email parsing libraries for automated analysis
- Access to threat intelligence platforms for IP/domain reputation

## Workflow

```python
# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
```

1. **Scope the Analysis** — Define what email headers artifacts or data sources to examine and the investigation timeline.
2. **Preserve Evidence** — Create forensic copies of relevant data. Maintain chain of custody documentation.
3. **Extract Key Indicators** — Use phishing investigation to parse and extract relevant email headers data points from collected artifacts.
4. **Correlate Findings** — Cross-reference extracted data with other sources (threat intel, logs, timelines).
5. **Build Timeline** — Construct a chronological sequence of events related to email headers.
6. **Document Analysis** — Write findings report with evidence, conclusions, and recommendations.

## Tools

- **phishing investigation** — Primary tool for this skill
- **Forensic Toolkit** — Evidence collection and analysis
- **Timeline Tools** — Chronological event reconstruction
- **Log Analysis Platform** — Centralized log parsing and search


## Process

1. **Reconnaissance** — Gather target information, identify attack surface, enumerate services
1. **Analysis/Exploitation** — Execute the technique, analyze results, document findings
1. **Reporting** — Document IOCs, write findings, provide remediation recommendations

## Verification

- [ ] All email headers procedures executed completely and documented
- [ ] Findings validated against multiple data sources
- [ ] False positives identified and filtered
- [ ] Results documented with evidence and timestamps
- [ ] Recommendations provided with risk-based prioritization

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |