Skill Market

detecting email account compromise

Detect compromised O365 and Google Workspace email accounts by analyzing inbox rule creation, suspicious sign-in

GitHub
githubcommunityai-agentsclaude-codecloud-securitycybersecuritydevsecopsethical-hacking
0.0
0 installs33.4K GitHub starsby mukul975

Skill Introduction

Overview
Detect compromised O365 and Google Workspace email accounts by analyzing inbox rule creation, suspicious sign-in

Core value

Turns reusable Debugging know-how into an installable skill, helping users complete github, community, ai-agents, claude-code work faster.

Target users

  • Developers, testers, and maintainers who handle Debugging tasks in Focus Code.
  • Teams that already trust workflows or content from mukul975.
  • Users who want standardized prompts, steps, or conventions instead of repeating setup work.

Best practices

  • Read the skill content first to confirm required inputs, expected outputs, and dependencies.
  • Try it on a small task before relying on it for critical work.
  • Add project-specific constraints such as coding style, target platform, test expectations, and delivery format.
  • For external sources, verify the source link, version, and recent maintenance activity.

Best use cases

  • Tasks related to github, community, ai-agents, claude-code that need a reusable execution flow.
  • Converting a community repo, team convention, or personal workflow into day-to-day assistance.
  • Starting from a proven skill instead of writing prompts or procedures from scratch.

Limits and boundaries

  • Results depend on the quality of the original skill content and may need human correction.
  • It does not replace code review, tests, security review, or professional judgment.
  • External tools, APIs, account permissions, and local dependencies still need separate setup.

Differentiation

  • Structured around Debugging, making it easier to discover and reuse than loose prompt snippets.
  • Marked as GitHub, which helps users judge trust and maintenance expectations.
  • Keeps the original source link available for repository, documentation, or discussion follow-up.
  • Tagged with github, community, ai-agents, claude-code, so it can be filtered by concrete task intent.

Install and use

Install
Copy Install Command
focus install detecting-email-account-compromise-e3b71f
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SKILL.md

Primary filemarkdown3 KB

name: detecting-email-account-compromise description: Detect compromised O365 and Google Workspace email accounts by analyzing inbox rule creation, suspicious sign-in locations, mail forwarding rules, and unusual API access patterns via Microsoft Graph and audit logs. domain: cybersecurity subdomain: incident-response tags:

  • email-compromise
  • office365
  • microsoft-graph
  • bec
  • inbox-rules
  • sign-in-analysis
  • account-takeover mitre_attack:
  • T1114
  • T1566
  • T1078
  • T1534 version: '1.0' author: mahipal license: Apache-2.0 nist_csf:
  • RS.MA-01
  • RS.MA-02
  • RS.AN-03
  • RC.RP-01

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

  • 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

  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.

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