Detecting Email Account Compromise

Detecting Email Account Compromise

Overview

Email account compromise (EAC) is a prevalent attack vector where adversaries gain unauthorized access to mailboxes to exfiltrate sensitive data, conduct business email compromise (BEC), or establish persistence through inbox rule manipulation. Attackers commonly create forwarding rules to siphon emails, delete rules to hide evidence, or use OAuth tokens for persistent access. Detection relies on analyzing Microsoft 365 Unified Audit Logs, Azure AD sign-in logs for impossible travel or suspicious locations, inbox rule creation events (Set-InboxRule, New-InboxRule), and Microsoft Graph API access patterns. Key indicators include forwarding rules to external addresses, rules that delete or move messages matching keywords like "invoice" or "payment", and sign-ins from unusual user agents such as python-requests.

When to Use

Trigger phrases:

• "detecting email account compromise"

• "Detect compromised O365 and Google Workspace email accounts by analyzing inbox r"

• When investigating security incidents that require detecting email account compromise

• When building detection rules or threat hunting queries for this domain

• When SOC analysts need structured procedures for this analysis type

• When validating security monitoring coverage for related attack techniques

Prerequisites

• Microsoft 365 with Unified Audit Logging enabled
• Azure AD P1/P2 for risk detection APIs
• Python 3.9+ with requests, msal libraries
• Microsoft Graph API application registration with Mail.Read, AuditLog.Read.All permissions
• Understanding of OAuth2 client credential flows

Steps

# 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. Export audit logs or connect to Microsoft Graph API using MSAL authentication
2. Query inbox rules for all monitored mailboxes via /users/{id}/mailFolders/inbox/messageRules
3. Analyze rules for external forwarding (ForwardTo, RedirectTo external addresses)
4. Detect suspicious rule patterns: deletion rules, keyword-matching rules targeting financial terms
5. Query sign-in logs via /auditLogs/signIns for unusual locations and impossible travel
6. Check for suspicious user agent strings (python-requests, PowerShell, curl)
7. Identify OAuth application consent grants for suspicious third-party apps
8. Correlate findings across users to detect campaign-level compromise
9. Generate compromise indicators report with severity scores

Expected Output

A JSON report listing compromised or suspicious accounts, malicious inbox rules detected, impossible travel events, suspicious OAuth grants, and recommended containment actions with severity ratings.

When NOT to Use

• You need to perform the attack to test detection (use performing-* skills)
• Task is about analyzing past incidents (use analyzing-* skills)
• You need to implement detection rules (use implementing-* skills)
• Task is about threat hunting proactively (use hunting-* skills)
• You don't have access to logs or monitoring data
• Task requires incident response (use IR skills)

Red Flags

• Performing actions without explicit written authorization from the asset owner
• Testing against production systems without a defined scope and rules of engagement
• Sharing sensitive findings or credentials in unencrypted communications
• Failing to properly scope and contain the assessment before starting

Process

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

Verification

• All steps executed successfully against a test environment before production use
• Output documented with screenshots or logs demonstrating expected behavior
• Results validated against known-good baselines or reference implementations
• Documentation complete enough for another analyst to reproduce findings

Anti-Rationalization Table