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"}
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()
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", ,
]
():
findings = []
pattern_name, pattern SENSITIVE_PATTERNS.items():
matches = re.findall(pattern, response_text)
matches:
findings.append({
: endpoint,
: ,
: pattern_name,
: (matches),
: matches[][:] + (matches[]) > matches[]
})
response_lower = response_text.lower()
field SENSITIVE_FIELD_NAMES:
response_lower response_lower:
findings.append({
: endpoint,
: ,
: field
})
findings
method, path, body endpoints_to_test:
resp = requests.request(method, , headers=headers, json=body)
resp.status_code == :
findings = scan_response(, resp.text)
f findings:
()
Step 3: Compare UI Display vs API Response
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"},
}
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):
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())
:
actual_fields = (data.keys())
:
expected = ui_displayed_fields.get(path, ())
excess = actual_fields - expected
excess:
()
field (excess):
()
Step 4: Test User Object Exposure in Related Endpoints
endpoints_with_user_objects = [
"/orders",
"/comments",
"/reviews",
"/transactions",
"/team/members",
]
for path in endpoints_with_user_objects:
resp = requests.get(f"{BASE_URL}{path}", headers=headers)
if resp.status_code == 200:
text = resp.text
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}")
Step 5: GraphQL Over-Fetching Analysis
GRAPHQL_URL = f"{BASE_URL}/graphql"
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}]")
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")
Step 6: Debug and Internal Data Leakage
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)}")
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 [, , , ,
, , , , ]):
()
Key Concepts
| 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 |
Tools & Systems
- Burp Suite Professional: Intercept API responses and use the Comparer tool to diff expected vs actual response schemas
- mitmproxy: Scriptable proxy for automated response analysis with Python-based content inspection scripts
- OWASP ZAP: Passive scanner detects information disclosure in headers, error messages, and response bodies
- Postman: Compare documented response schemas against actual API responses using test scripts
- jq: Command-line JSON processor for extracting and analyzing specific fields from API responses
Common Scenarios
Scenario: Mobile Banking API Data Exposure Assessment
Context: A mobile banking application's API returns full account objects to the mobile client, which only displays account nickname and balance. The API is accessed by both iOS and Android apps and a web portal.
Approach:
- Configure mitmproxy on a test device and authenticate as the test user
- Capture all API responses during a complete user session (login, view accounts, transfer, logout)
- Analyze
GET /api/v1/accounts response: UI shows 4 fields but API returns 23 fields
- Discover that the API returns
routing_number, account_holder_ssn_last4, internal_risk_score, kyc_verification_status, and linked_external_accounts - none shown in UI
- Analyze
GET /api/v1/transactions response: API returns merchant_id, terminal_id, authorization_code, processor_response fields not needed by the client
- Check
GET /api/v1/users/me: API returns last_login_ip, mfa_backup_codes_remaining, account_officer_name, and credit_score_band
- Test error responses:
POST /api/v1/transfers with invalid payload returns SQL table name in error message
Pitfalls:
- Only checking top-level fields and missing sensitive data in deeply nested objects
- Not testing paginated responses where subsequent pages may include different fields
- Ignoring response headers that may leak server version, backend technology, or internal routing information
- Missing data exposure in error responses which often contain stack traces, SQL queries, or internal paths
- Assuming that HTTPS encryption prevents data exposure (it protects in transit, not from the authenticated client)
Output Format
## 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
