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.
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.
Most Often Missed & How to Confirm
Nested and embedded objects: sensitive fields hide in order.buyer.ssn, comment.author.email, team.members[].last_login_ip - recursively flatten; don't stop at top-level keys.
Pagination and list-vs-detail drift: list endpoints sometimes return more (or different) fields than the detail view, and page 2+ may differ from page 1.
Alternate representations:?fields=, ?expand=, ?include=, .json vs .xml, mobile User-Agent, and API-version headers can unlock extra fields.
GraphQL field enumeration: request sensitive fields directly even with introspection off (guess passwordHash, mfaSecret, fragments on shared types).
Error/debug leakage: malformed input often returns stack traces, SQL, internal hostnames, and X-* debug headers.
How to confirm a hit (avoid false negatives): prove the field reaches the client by capturing the raw HTTP response in a proxy, not the rendered UI - the value must be present in the response body/headers and be data the account should not see. Don't conclude negative until you've tried: nested objects, list endpoints, all pagination pages, fields/expand params, GraphQL, error responses, and a second lower-privileged account to confirm it's truly excess exposure.
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 namesdefextract_fields(obj, prefix=""):
fields = []
ifisinstance(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))
elifisinstance(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 insorted(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"
]
defscan_response(endpoint, response_text):
findings = []
# Check for sensitive data patterns in valuesfor 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] + "..."iflen(matches[0]) > 20else matches[0]
})
# Check for sensitive field names
response_lower = response_text.lower()
for field in SENSITIVE_FIELD_NAMES:
iff'"{field}"'in response_lower orf"'{field}'"in response_lower:
findings.append({
"endpoint": endpoint,
"type": "sensitive_field",
"field_name": field
})
return findings
# Scan all endpoint responsesfor 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'))}")
Step 3: Compare UI Display vs API Response
# 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 returnsfor 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()
ifisinstance(data, list):
actual_fields = set(data[0].keys()) if data elseset()
elifisinstance(data, dict):
# Handle paginated responses
items_key = next((k for k in data ifisinstance(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 insorted(excess):
print(f" - {field}")
Step 4: Test User Object Exposure in Related Endpoints
# 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"]:
iff'"{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 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 == 200and"errors"notin resp.json():
print("[CRITICAL] GraphQL exposes sensitive user fields without restriction")
Step 6: Debug and Internal Data Leakage
# 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()
ifany(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")
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
