# Performing Adversary In The Middle Phishing Detection

> Use when detect and respond to Adversary-in-the-Middle (AiTM) phishing attacks that use reverse proxy kits like EvilProxy, Evilginx, and Tycoon 2FA to bypass MFA and steal session tokens. Use when detecting and respond to adversary-in-the-middle (aitm) phishing attacks that use.

- Skill: `oyi77/performing-adversary-in-the-middle-phishing-detection` (Agent Skill)
- Install (CLI): `npx skillmds add oyi77/performing-adversary-in-the-middle-phishing-detection`
- Raw SKILL.md: https://api.skillmd.com/api/skills/oyi77/performing-adversary-in-the-middle-phishing-detection/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: Apache-2.0
- Author: oyi77 (https://skillmd.com/u/oyi77)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/oyi77/performing-adversary-in-the-middle-phishing-detection

---


# Performing Adversary-in-the-Middle Phishing Detection

## Overview
Adversary-in-the-Middle (AiTM) phishing attacks use reverse-proxy infrastructure to sit between the victim and the legitimate authentication service, intercepting both credentials and session cookies in real time. This allows attackers to bypass multi-factor authentication (MFA). The most prevalent PhaaS kits in 2025 include Tycoon 2FA, Sneaky 2FA, EvilProxy, and Evilginx. Over 1 million PhaaS attacks were detected in January-February 2025 alone. These attacks have evolved from QR codes to HTML attachments and SVG files for link distribution.


## When to Use
**Trigger phrases:**
- "performing adversary in the middle phishing detection"
- "Detect and respond to Adversary-in-the-Middle (AiTM) phishing attacks that use r"


- When conducting security assessments that involve performing adversary in the middle phishing detection
- When following incident response procedures for related security events
- When performing scheduled security testing or auditing activities
- When validating security controls through hands-on testing

## Prerequisites
- Azure AD / Entra ID Conditional Access policies
- SIEM with authentication log ingestion (Azure AD sign-in logs)
- Web proxy with SSL inspection and URL categorization
- Endpoint Detection and Response (EDR) solution
- FIDO2/phishing-resistant MFA capability

## Key Concepts

This section covers key concepts for performing adversary in the middle phishing detection.

- Ensure all prerequisites are met before proceeding
- Follow the documented workflow steps in sequence
- Record results and any anomalies encountered during this phase
### How AiTM Works
1. Victim receives phishing email with link to attacker-controlled domain
2. Attacker domain runs reverse proxy that mirrors legitimate login page
3. Victim enters credentials on proxied page; credentials captured in transit
4. Reverse proxy forwards credentials to real authentication service
5. MFA challenge sent to victim; victim completes MFA on proxied page
6. Attacker captures session cookie returned by legitimate service
7. Attacker replays session cookie to access victim's account without MFA

### Major AiTM Kits (2025)
| Kit | Type | Primary Targets | Evasion |
|---|---|---|---|
| Tycoon 2FA | PhaaS | Microsoft 365, Google | CAPTCHA, Cloudflare turnstile |
| EvilProxy | PhaaS | Microsoft 365, Google, Okta | Random URLs, IP rotation |
| Evilginx | Open-source | Any web application | Custom phishlets |
| Sneaky 2FA | PhaaS | Microsoft 365 | Anti-bot checks |
| NakedPages | PhaaS | Multiple | Minimal infrastructure |

### Detection Indicators
- Authentication from unusual IP not matching user profile
- Session cookie reuse from different IP/device than authentication
- Login page served from non-Microsoft/non-Google infrastructure
- CDN requests to legitimate auth providers from phishing domains
- Impossible travel between authentication and session usage

## 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. **Isolate the sample** — ensure the malware is in a sandboxed environment with no network access
2. **Record file metadata** — hash the sample and note file type, size, and compile timestamp
3. **Static analysis** — examine strings, imports, and disassembled code without execution
4. **Dynamic analysis** — execute in a monitored sandbox and record behavior (file, registry, network)
5. **Document IOCs** — extract indicators of compromise and write the analysis report
### Step 1: Deploy Phishing-Resistant MFA
- Implement FIDO2 security keys or Windows Hello for Business for high-value accounts
- Configure Conditional Access to require phishing-resistant MFA for admins
- Enable certificate-based authentication where possible
- Disable SMS and voice MFA for privileged accounts
- AiTM cannot intercept FIDO2 because authentication is bound to origin domain

### Step 2: Configure Conditional Access Policies
- Require compliant/managed device for sensitive application access
- Block authentication from anonymous proxies and Tor exit nodes
- Enforce token binding to limit session cookie replay
- Configure continuous access evaluation (CAE) for real-time token revocation
- Implement sign-in risk policies that require re-authentication for risky sign-ins

### Step 3: Build AiTM Detection Rules
- Alert on sign-in followed by session from different IP within 10 minutes
- Detect authentication where proxy IP does not match user's expected location
- Monitor for impossible travel patterns in session usage
- Alert on inbox rules created immediately after authentication (common post-compromise)
- Detect new MFA method registration from suspicious sign-in

### Step 4: Monitor Web Proxy for AiTM Infrastructure
- Log and analyze DNS queries to newly registered domains
- Detect connections to known PhaaS infrastructure IPs
- Alert on authentication page backgrounds loaded from legitimate CDNs through proxy domains
- Monitor for SSL certificates issued to domains mimicking corporate login pages
- Block access to known EvilProxy/Evilginx infrastructure via threat intelligence

### Step 5: Implement Post-Compromise Detection
- Alert on mailbox forwarding rules created after suspicious authentication
- Detect OAuth app consent after AiTM sign-in
- Monitor for email sending patterns indicating BEC follow-up
- Alert on SharePoint/OneDrive mass download after session hijack
- Track lateral movement from compromised account

## When NOT to Use

- You don't have explicit written authorization to test
- Task is about defense/detection, not offense (use detection skills)
- You need to implement security controls (use implementing-* skills)
- Task requires compliance auditing (use auditing-* skills)
- You're investigating an incident (use incident response skills)
- Target is out of scope for your engagement
- Task is about vulnerability scanning only (use scanning tools)


## Red Flags

- Performing actions without explicit written authorization from the asset owner
- Testing against production systems without a defined scope and rules of engagement
- Analyzing malware on a machine connected to the production network
- Failing to isolate the analysis environment from the internet
- Executing samples without proper containment (VM, sandbox)

## Verification

- All steps executed successfully against a test environment before production use
- Output documented with screenshots or logs demonstrating expected behavior
- Sample hash recorded and verified (MD5, SHA-1, SHA-256)
- Analysis environment confirmed isolated from production network
- Indicators of compromise (IOCs) extracted and documented

## Tools & Resources
- **Microsoft Entra ID Protection**: Risk-based Conditional Access
- **Azure AD Sign-in Logs**: Authentication event analysis
- **Okta ThreatInsight**: AiTM proxy detection at IdP level
- **Sekoia TDR**: AiTM campaign tracking and intelligence
- **Evilginx (defensive)**: Understanding attack mechanics for detection

## Validation
- Phishing-resistant MFA blocks AiTM session capture in test scenario
- Conditional Access denies session replay from different device/IP
- SIEM alerts fire on simulated AiTM sign-in patterns
- Web proxy blocks connections to known PhaaS infrastructure
- Post-compromise rules detect inbox rule creation after suspicious auth

## Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality

## 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. |