| name | detecting-broken-object-property-level-authorization |
| description | Detect and test for OWASP API3:2023 Broken Object Property Level Authorization vulnerabilities including excessive data exposure and mass assignment attacks. |
| domain | cybersecurity |
| subdomain | api-security |
| tags | ["api-security","bopla","owasp-api3","mass-assignment","excessive-data-exposure","property-level-authorization","api-testing","penetration-testing"] |
| version | 1.0 |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | ["PR.PS-01","ID.RA-01","PR.DS-10","DE.CM-01"] |
Detecting Broken Object Property Level Authorization
Overview
Broken Object Property Level Authorization (BOPLA), classified as API3:2023 in the OWASP API Security Top 10, combines two related vulnerability classes: Excessive Data Exposure (API returning more data than needed) and Mass Assignment (API accepting more data than intended). Even when APIs enforce object-level authorization correctly, they may fail to control which specific properties of an object a user can read or modify. Attackers exploit this by reading sensitive properties from API responses or injecting additional properties into request bodies to modify fields they should not have access to.
When to Use
- When investigating security incidents that require detecting broken object property level authorization
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Detection Gaps & Validation
- Mass-assignment silently accepted: the server returns 200 and echoes only safe fields, but the privileged field (
role, is_verified) was written - confirm by re-fetching the object (GET) with the owner/victim account, never trust the write response body.
- Field-name aliasing: APIs map
isAdmin, is_admin, admin, role_id, roles[], and nested account.tier to the same column - testing only one casing/spelling yields false negatives.
- Excessive exposure hides in nested objects and arrays: sensitive fields appear under
data.items[].owner.ssn or only on page 2; flatten recursively and check list responses, not just the top-level object.
- GraphQL over-fetch: fields blocked on REST may be reachable via GraphQL aliases/fragments even with introspection disabled.
How to validate the detection fires: seed a test object with a known sensitive field and confirm the scanner's SENSITIVE_PROPERTY_PATTERNS flag it; submit a known mass-assignment payload and confirm the re-fetch verification step reports the change. Tune false positives by maintaining a per-endpoint expected-field allowlist so legitimately public fields (avatar_url, display_name) do not generate noise.
Prerequisites
- Target API with endpoints that return or accept object data
- API documentation or schema (OpenAPI spec preferred)
- Burp Suite or Postman for API request manipulation
- Multiple user accounts with different privilege levels
- Python 3.8+ with requests library for automated testing
- Authorization to perform security testing
Vulnerability Patterns
Excessive Data Exposure
The API returns object properties the client does not need:
{
"id": 123,
"username": "john_doe",
"email": "john@example.com",
"name": "John Doe",
"ssn": "123-45-6789",
"salary": 95000,
"internal_notes": "VIP client",
"password_hash": "$2b$12...",
"role": "admin",
"created_by": "system_admin",
"credit_card_last4": "4242"
}
Mass Assignment
The API binds client-supplied data to internal object properties without filtering:
// Normal user update request
PUT /api/v1/users/123
Content-Type: application/json
{
"name": "John Updated",
"email": "new@example.com",
"role": "admin", // Attacker-injected: privilege escalation
"is_verified": true, // Attacker-injected: bypass verification
"discount_rate": 100, // Attacker-injected: business logic abuse
"account_balance": 999999 // Attacker-injected: financial fraud
}
Testing Methodology
"""BOPLA Vulnerability Scanner
Tests APIs for Broken Object Property Level Authorization
including Excessive Data Exposure and Mass Assignment.
"""
import requests
import json
import sys
from typing import Dict, List, Optional, Set
from dataclasses import dataclass, field
from copy import deepcopy
@dataclass
class BOPLAFinding:
endpoint: str
method: str
vulnerability_type: str
severity: str
property_name: str
details: str
class BOPLAScanner:
SENSITIVE_PROPERTY_PATTERNS = {
"critical": [
"password", "password_hash", "secret", "token", "api_key",
"private_key", "secret_key", "access_token", "refresh_token",
],
"high": [
"ssn", "social_security", "tax_id", "credit_card", "card_number",
"cvv", "bank_account", "routing_number",
],
"medium": [
"salary", "income", "internal_notes", "admin_notes",
"created_by", "modified_by", "ip_address", "session_id",
"role", "permissions", "is_admin", "is_superuser", "privilege",
],
"low": [
"phone", "address", "date_of_birth", "dob", "age",
"gender", "ethnicity", "religion",
]
}
MASS_ASSIGNMENT_FIELDS = [
("role", "admin"),
("is_admin", True),
("is_verified", True),
("is_active", True),
("email_verified", True),
("account_type", "premium"),
("discount_rate", 100),
("credit_limit", 999999),
("permissions", ["admin", "write", "delete"]),
("account_balance", 999999),
("subscription_tier", "enterprise"),
("rate_limit", 999999),
]
def __init__(self, base_url: str, auth_headers: Dict[str, str]):
self.base_url = base_url.rstrip('/')
self.auth_headers = auth_headers
self.findings: List[BOPLAFinding] = []
def test_excessive_data_exposure(self, endpoint: str,
expected_fields: Set[str]) -> List[BOPLAFinding]:
"""Test if API response contains more fields than expected."""
findings = []
url = f"{self.base_url}{endpoint}"
try:
response = requests.get(url, headers=self.auth_headers, timeout=10)
if response.status_code != 200:
return findings
data = response.json()
objects = data if isinstance(data, list) else [data]
if isinstance(data, dict) and "data" in data:
objects = data["data"] if isinstance(data["data"], list) else [data["data"]]
for obj in objects[:5]:
if not isinstance(obj, dict):
continue
response_fields = set(self._flatten_keys(obj))
unexpected_fields = response_fields - expected_fields
for field_name in unexpected_fields:
severity = self._classify_sensitivity(field_name)
if severity:
finding = BOPLAFinding(
endpoint=endpoint,
method="GET",
vulnerability_type="excessive_exposure",
severity=severity,
property_name=field_name,
details=f"Unexpected sensitive field '{field_name}' in response"
)
findings.append(finding)
self.findings.append(finding)
except (requests.exceptions.RequestException, json.JSONDecodeError):
pass
return findings
def test_mass_assignment(self, endpoint: str, method: str = "PUT",
original_data: Optional[dict] = None) -> List[BOPLAFinding]:
"""Test if API accepts and processes additional injected properties."""
findings = []
url = f"{self.base_url}{endpoint}"
if original_data is None:
try:
response = requests.get(url, headers=self.auth_headers, timeout=10)
if response.status_code == 200:
original_data = response.json()
else:
original_data = {}
except (requests.exceptions.RequestException, json.JSONDecodeError):
original_data = {}
for field_name, injected_value in self.MASS_ASSIGNMENT_FIELDS:
if field_name in original_data:
original_value = original_data[field_name]
if original_value == injected_value:
continue
test_data = deepcopy(original_data)
test_data[field_name] = injected_value
headers = {**self.auth_headers, "Content-Type": "application/json"}
try:
if method == "PUT":
response = requests.put(url, json=test_data,
headers=headers, timeout=10)
elif method == "PATCH":
response = requests.patch(url, json={field_name: injected_value},
headers=headers, timeout=10)
elif method == "POST":
response = requests.post(url, json=test_data,
headers=headers, timeout=10)
if response.status_code in (200, 201, 204):
verify_response = requests.get(url, headers=self.auth_headers, timeout=10)
if verify_response.status_code == 200:
updated_data = verify_response.json()
if updated_data.get(field_name) == injected_value:
finding = BOPLAFinding(
endpoint=endpoint,
method=method,
vulnerability_type="mass_assignment",
severity="CRITICAL" if field_name in ["role", "is_admin", "permissions"]
else "HIGH",
property_name=field_name,
details=f"Successfully injected '{field_name}={injected_value}'"
)
findings.append(finding)
self.findings.append(finding)
if field_name in original_data:
restore_data = {field_name: original_data[field_name]}
requests.patch(url, json=restore_data,
headers=headers, timeout=10)
except requests.exceptions.RequestException:
continue
return findings
def test_graphql_property_exposure(self, graphql_endpoint: str,
query: str) -> List[BOPLAFinding]:
"""Test GraphQL APIs for property-level authorization issues."""
findings = []
url = f"{self.base_url}{graphql_endpoint}"
introspection = """
{
__schema {
types {
name
fields {
name
type { name kind }
}
}
}
}
"""
try:
response = requests.post(
url,
json={"query": introspection},
headers=self.auth_headers,
timeout=10
)
if response.status_code == 200:
data = response.json()
if "errors" not in data:
finding = BOPLAFinding(
endpoint=graphql_endpoint,
method="POST",
vulnerability_type="excessive_exposure",
severity="MEDIUM",
property_name="__schema",
details="GraphQL introspection enabled - full schema exposed"
)
findings.append(finding)
self.findings.append(finding)
except requests.exceptions.RequestException:
pass
return findings
def _flatten_keys(self, obj: dict, prefix: str = "") -> List[str]:
"""Recursively flatten nested dictionary keys."""
keys = []
for key, value in obj.items():
full_key = f"{prefix}.{key}" if prefix else key
keys.append(full_key)
if isinstance(value, dict):
keys.extend(self._flatten_keys(value, full_key))
return keys
def _classify_sensitivity(self, field_name: str) -> Optional[str]:
"""Classify the sensitivity level of a field name."""
lower_name = field_name.lower().split('.')[-1]
for severity, patterns in self.SENSITIVE_PROPERTY_PATTERNS.items():
for pattern in patterns:
if pattern in lower_name:
return severity.upper()
return None
def generate_report(self) -> dict:
return {
"total_findings": len(self.findings),
"by_type": {
"excessive_exposure": len([f for f in self.findings
if f.vulnerability_type == "excessive_exposure"]),
"mass_assignment": len([f for f in self.findings
if f.vulnerability_type == "mass_assignment"]),
},
"by_severity": {
"CRITICAL": len([f for f in self.findings if f.severity == "CRITICAL"]),
"HIGH": len([f for f in self.findings if f.severity == "HIGH"]),
"MEDIUM": len([f for f in self.findings if f.severity == "MEDIUM"]),
"LOW": len([f for f in self.findings if f.severity == "LOW"]),
},
"findings": [
{
"endpoint": f.endpoint,
"method": f.method,
"type": f.vulnerability_type,
"severity": f.severity,
"property": f.property_name,
"details": f.details,
}
for f in self.findings
]
}
Mitigation
class UserSerializer:
PUBLIC_FIELDS = ['id', 'username', 'name', 'avatar_url']
OWNER_FIELDS = PUBLIC_FIELDS + ['email', 'phone', 'preferences']
ADMIN_FIELDS = OWNER_FIELDS + ['role', 'created_at', 'last_login']
def serialize(self, user, requesting_user):
if requesting_user.is_admin:
fields = self.ADMIN_FIELDS
elif requesting_user.id == user.id:
fields = self.OWNER_FIELDS
else:
fields = self.PUBLIC_FIELDS
return {field: getattr(user, field) for field in fields}
WRITABLE_FIELDS = {'name', 'email', 'phone', 'avatar_url', 'preferences'}
def update_user(user_id, request_data, requesting_user):
safe_data = {k: v for k, v in request_data.items() if k in WRITABLE_FIELDS}
User.objects.filter(id=user_id).update(**safe_data)
References
