exploiting-excessive-data-exposure-in-api

Tests APIs for excessive data exposure where endpoints return more data than the client application needs, relying on the frontend to filter sensitive fields. The tester intercepts API responses and analyzes them for leaked PII, internal identifiers, debug information, or sensitive business data that the UI does not display but the API transmits. This maps to OWASP API3:2023 Broken Object Property Level Authorization. Activates for requests involving API data leakage testing, excessive data exposure, response filtering bypass, or API over-fetching.

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name	exploiting-excessive-data-exposure-in-api
description	Tests APIs for excessive data exposure where endpoints return more data than the client application needs, relying on the frontend to filter sensitive fields. The tester intercepts API responses and analyzes them for leaked PII, internal identifiers, debug information, or sensitive business data that the UI does not display but the API transmits. This maps to OWASP API3:2023 Broken Object Property Level Authorization. Activates for requests involving API data leakage testing, excessive data exposure, response filtering bypass, or API over-fetching.
domain	cybersecurity
subdomain	api-security
tags	["api-security","owasp","data-exposure","rest-security","pii-leakage"]
version	1.0.0
author	mahipal
license	Apache-2.0

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

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", , ),
]

 method, path, body  endpoints_to_test:
    resp = requests.request(method, , headers=headers, json=body)
     resp.status_code == :
        data = resp.json()
        
         ():
            fields = []
             (obj, ):
                 k, v  obj.items():
                    full_key =   prefix  k
                    fields.append(full_key)
                    fields.extend(extract_fields(v, full_key))
             (obj, )  obj:
                fields.extend(extract_fields(obj[], ))
             fields

        all_fields = extract_fields(data)
        ()
         f  (all_fields):
            ()

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),
                "sample": matches[0][:20] + "..." if len(matches[0]) > 20 else matches[0]
            })

    # Check for sensitive field names
    response_lower = response_text.lower()
    for field in SENSITIVE_FIELD_NAMES:
        if f'"{field}"' in response_lower or f"'{field}'" in response_lower:
            findings.append({
                "endpoint": endpoint,
                "type": "sensitive_field",
                "field_name": field
            })

    return findings

# Scan all endpoint responses
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:
        findings = scan_response(f"{method} {path}", resp.text)
        for f in findings:
            print(f"[FINDING] {f['endpoint']}: {f['type']} - {f.get('pattern', f.get('field_name'))}")

# Fields the UI shows (observed from the frontend application)
ui_displayed_fields = {
    "/users/me": {"name", "email", "avatar_url", "role"},
    "/users/me/orders": {"order_id", "date", "status", "total"},
    "/products": {"id", "name", "price", "image_url", "description"},
}

# Fields the API actually returns
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()
        if isinstance(data, list):
            actual_fields = set(data[0].keys()) if data else set()
        elif isinstance(data, dict):
            # Handle paginated responses
            items_key = next((k for k in data if isinstance(data[k], list)), None)
            if items_key and data[items_key]:
                actual_fields = set(data[items_key][0].keys())
            else:
                actual_fields = set(data.keys())
        else:
            continue

        expected = ui_displayed_fields.get(path, set())
        excess = actual_fields - expected
        if excess:
            print(f"\n{method} {path} - EXCESS FIELDS (not shown in UI):")
            for field in sorted(excess):
                print(f"  - {field}")

# Many APIs embed full user objects in responses for orders, comments, etc.
endpoints_with_user_objects = [
    "/orders",          # Each order may include full seller/buyer profile
    "/comments",        # Comments may include full author profile
    "/reviews",         # Reviews may expose reviewer details
    "/transactions",    # Transactions may include counterparty info
    "/team/members",    # Team listing may expose excessive member data
]

for path in endpoints_with_user_objects:
    resp = requests.get(f"{BASE_URL}{path}", headers=headers)
    if resp.status_code == 200:
        text = resp.text
        # Check for user data leakage in nested objects
        user_fields_found = []
        for field in ["password_hash", "last_login_ip", "mfa_enabled", "phone_number",
                      "date_of_birth", "ssn", "internal_notes", "salary", "address"]:
            if f'"{field}"' in text:
                user_fields_found.append(field)
        if user_fields_found:
            print(f"[EXCESSIVE] {path} exposes user fields: {user_fields_found}")

# GraphQL allows clients to request any available field
GRAPHQL_URL = f"{BASE_URL}/graphql"

# Introspection query to discover all fields on User type
introspection = {
    "query": """
    {
      __type(name: "User") {
        fields {
          name
          type {
            name
            kind
          }
        }
      }
    }
    """
}

resp = requests.post(GRAPHQL_URL, headers=headers, json=introspection)
if resp.status_code == 200:
    fields = resp.json().get("data", {}).get("__type", {}).get("fields", [])
    print("Available User fields via GraphQL:")
    for f in fields:
        sensitivity = "SENSITIVE" if f["name"] in SENSITIVE_FIELD_NAMES else "normal"
        print(f"  {f['name']} ({f['type']['name']}) [{sensitivity}]")

# Try to query sensitive fields
sensitive_query = {
    "query": """
    query {
      users {
        id
        email
        passwordHash
        socialSecurityNumber
        internalNotes
        lastLoginIp
        mfaSecret
        apiKey
      }
    }
    """
}
resp = requests.post(GRAPHQL_URL, headers=headers, json=sensitive_query)
if resp.status_code == 200 and "errors" not in resp.json():
    print("[CRITICAL] GraphQL exposes sensitive user fields without restriction")

# Test for debug information in responses
debug_headers_to_check = [
    "X-Debug-Token", "X-Debug-Info", "Server", "X-Powered-By",
    "X-Request-Id", "X-Correlation-Id", "X-Backend-Server",
    "X-Runtime", "X-Version", "X-Build-Version"
]

resp = requests.get(f"{BASE_URL}/users/me", headers=headers)
for h in debug_headers_to_check:
    if h.lower() in {k.lower(): v for k, v in resp.headers.items()}:
        print(f"[INFO LEAK] Header {h}: {resp.headers.get(h)}")

# Test error responses for stack traces
error_payloads = [
    ("GET", "/users/invalid-id-format", None),
    ("POST", "/orders", {"invalid": "payload"}),
    ("GET", "/users/-1", None),
    ("GET", "/users/0", None),
]

for method, path, body in error_payloads:
    resp = requests.request(method, f"{BASE_URL}{path}", headers=headers, json=body)
    if resp.status_code >= 400:
        text = resp.text.lower()
        if any(kw in text for kw in ["stack trace", "traceback", "at com.", "at org.",
                                      "file \"", "line ", "exception", "sql", "query"]):
            print(f"[DEBUG LEAK] {method} {path} -> {resp.status_code}: Contains stack trace or query info")

Term	Definition
Excessive Data Exposure	API returns more data fields than the client needs, relying on frontend filtering to hide sensitive information from users
Over-Fetching	Requesting or receiving more data than needed for a specific operation, common in REST APIs that return fixed response schemas
Response Filtering	Client-side filtering of API response data to display only relevant fields, which provides zero security since the full response is interceptable
Object Property Level Authorization	OWASP API3:2023 - ensuring that users can only read/write object properties they are authorized to access
PII Leakage	Unintended exposure of Personally Identifiable Information in API responses including names, emails, addresses, SSNs, or financial data
Schema Validation	Enforcing that API responses conform to a defined schema, stripping unauthorized fields before transmission

## Finding: Excessive Data Exposure in Account and Transaction APIs

**ID**: API-DATA-001
**Severity**: High (CVSS 7.1)
**OWASP API**: API3:2023 - Broken Object Property Level Authorization
**Affected Endpoints**:
  - GET /api/v1/accounts
  - GET /api/v1/transactions
  - GET /api/v1/users/me

**Description**:
The API returns full database objects to the client, including sensitive fields
that are not displayed in the mobile application UI. The mobile app filters
these fields client-side, but they are fully accessible by intercepting the
API response. This exposes SSN fragments, internal risk scores, and KYC
verification data for any authenticated user.

**Excess Fields Discovered**:
- /accounts: routing_number, account_holder_ssn_last4, internal_risk_score,
  kyc_verification_status, linked_external_accounts (18 excess fields total)
- /transactions: merchant_id, terminal_id, authorization_code,
  processor_response (12 excess fields total)
- /users/me: last_login_ip, mfa_backup_codes_remaining, credit_score_band

**Impact**:
An authenticated user can extract sensitive financial data, internal risk
assessments, and PII for their own account that the application is not
intended to reveal. Combined with BOLA vulnerabilities, this data could
be extracted for all users.

**Remediation**:
1. Implement server-side response filtering using DTOs/view models that only include fields needed by the client
2. Use GraphQL field-level authorization or REST response schemas per endpoint per role
3. Remove sensitive fields from API responses at the serialization layer
4. Implement response schema validation in the API gateway to strip undocumented fields
5. Add automated tests that verify response schemas match documentation