Exploiting Excessive Data Exposure in API

When to Use

• Testing APIs where the frontend displays a subset of data but the API response includes additional fields
• Assessing mobile application APIs where responses are designed for multiple client types and may contain excess data
• Identifying PII leakage in API responses that include email addresses, phone numbers, SSNs, or payment data not shown in the UI
• Testing GraphQL APIs where clients can request arbitrary fields including sensitive attributes
• Evaluating APIs after microservice refactoring where internal service-to-service data leaks into public endpoints

Do not use without written authorization. Data exposure testing involves capturing and analyzing potentially sensitive personal data.

Prerequisites

• Written authorization specifying target API endpoints and scope
• Burp Suite Professional or mitmproxy configured as intercepting proxy
• Two test accounts at different privilege levels (regular user and admin)
• Browser developer tools or mobile proxy setup for traffic capture
• Python 3.10+ with requests and json libraries
• API documentation (OpenAPI spec) for comparison against actual responses

Legal Notice: This skill is for authorized security testing and educational purposes only. Unauthorized use against systems you do not own or have written permission to test is illegal and may violate computer fraud laws.

Workflow

Step 1: Response Schema Discovery

Compare documented API responses with actual responses:

import requests
import json

BASE_URL = "https://target-api.example.com/api/v1"
headers = {"Authorization": "Bearer <user_token>", "Content-Type": "application/json"}

# Fetch a resource and analyze all returned fields
endpoints_to_test = [
    ("GET", "/users/me", None),
    ("GET", "/users/me/orders", None),
    ("GET", "/products", None),
    ("GET", "/users/me/settings", None),
    ("GET", "/transactions", None),
]

for method, path, body in endpoints_to_test:
    resp = requests.request(method, f"{BASE_URL}{path}", headers=headers, json=body)
    if resp.status_code == 200:
        data = resp.json()
        # Recursively extract all field names
        def extract_fields(obj, prefix=""):
            fields = []
            if isinstance(obj, dict):
                for k, v in obj.items():
                    full_key = f"{prefix}.{k}" if prefix else k
                    fields.append(full_key)
                    fields.extend(extract_fields(v, full_key))
            elif isinstance(obj, list) and obj:
                fields.extend(extract_fields(obj[0], f"{prefix}[]"))
            return fields

        all_fields = extract_fields(data)
        print(f"\n{method} {path} - {len(all_fields)} fields returned:")
        for f in sorted(all_fields):
            print(f"  {f}")

Step 2: Sensitive Data Pattern Detection

Scan API responses for sensitive data patterns:

import re

SENSITIVE_PATTERNS = {
    "email": r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}',
    "phone": r'(\+?1?\s?\(?\d{3}\)?[\s.-]?\d{3}[\s.-]?\d{4})',
    "ssn": r'\b\d{3}-\d{2}-\d{4}\b',
    "credit_card": r'\b(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14}|3[47][0-9]{13})\b',
    "password_hash": r'\$2[aby]?\$\d{2}\$[./A-Za-z0-9]{53}',
    "api_key": r'(?:api[_-]?key|apikey)["\s:=]+["\']?([a-zA-Z0-9_\-]{20,})',
    "internal_ip": r'\b(?:10\.\d{1,3}|172\.(?:1[6-9]|2\d|3[01])|192\.168)\.\d{1,3}\.\d{1,3}\b',
    "aws_key": r'AKIA[0-9A-Z]{16}',
    "jwt_token": r'eyJ[A-Za-z0-9_-]+\.eyJ[A-Za-z0-9_-]+\.[A-Za-z0-9_-]+',
    "uuid": r'[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}',
}

SENSITIVE_FIELD_NAMES = [
    "password", "password_hash", "secret", "token", "ssn", "social_security",
    "credit_card", "card_number", "cvv", "pin", "private_key", "api_key",
    "internal_id", "debug", "trace", "stack_trace", "created_by_ip",
    "last_login_ip", "salt", "session_id", "refresh_token", "mfa_secret",
    "date_of_birth", "bank_account", "routing_number", "tax_id"
]

def scan_response(endpoint, response_text):
    findings = []
    # Check for sensitive data patterns in values
    for pattern_name, pattern in SENSITIVE_PATTERNS.items():
        matches = re.findall(pattern, response_text)
        if matches:
            findings.append({
                "endpoint": endpoint,
                "type": "sensitive_value",
                "pattern": pattern_name,
                "count": len(matches),

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