--- name: exploiting-excessive-data-exposure-in-api-7e333a5fea4f 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 nist_csf: - PR.PS-01 - ID.RA-01 - PR.DS-10 - DE.CM-01 mitre_attack: - T1190 - T1059.007 - T1552.001 - T1027 - T1070 --- # 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: ```python import requests import json BASE_URL = "https://target-api.example.com/api/v1" headers = {"Authorization": "Bearer ", "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: ```python 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'))}") ``` ### Step 3: Compare UI Display vs API Response ```python # 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}") ``` ### Step 4: Test User Object Exposure in Related Endpoints ```python # 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}") ``` ### Step 5: GraphQL Over-Fetching Analysis ```python # 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") ``` ### Step 6: Debug and Internal Data Leakage ```python # 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") ``` ## 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**: 1. Configure mitmproxy on a test device and authenticate as the test user 2. Capture all API responses during a complete user session (login, view accounts, transfer, logout) 3. Analyze `GET /api/v1/accounts` response: UI shows 4 fields but API returns 23 fields 4. 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 5. Analyze `GET /api/v1/transactions` response: API returns `merchant_id`, `terminal_id`, `authorization_code`, `processor_response` fields not needed by the client 6. Check `GET /api/v1/users/me`: API returns `last_login_ip`, `mfa_backup_codes_remaining`, `account_officer_name`, and `credit_score_band` 7. 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 ``` --- Source attribution: 26zl Original project: https://github.com/26zl/cybersec-toolkit/blob/2ad2519d437cda1f8a822ef7adc336993601a5ac/.claude/skills/exploiting-excessive-data-exposure-in-api/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Dave AI. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. 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See the License for the specific language governing permissions and limitations under the License. # Dave AI resource metadata Creator / publisher: 26zl Original project: https://github.com/26zl/cybersec-toolkit/blob/2ad2519d437cda1f8a822ef7adc336993601a5ac/.claude/skills/exploiting-excessive-data-exposure-in-api/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering Roles: Data Analyst, QA / Test Engineer, Security Engineer Specialties: API Tools: Burp Suite, Mitmproxy Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 9c656bec66506c0b99180f78403ee72baa5929a37caad95fcc3284a687777c4f