detecting-broken-object-property-level-authorization
Detect and test for OWASP API3:2023 Broken Object Property Level Authorization (BOPLA), covering excessive data exposure in API responses and mass assignment via injected request-body properties. Use when reviewing API responses/requests for over-exposed or over-writable object fields, or building detection rules and test cases for property-level authorization gaps that object-level checks miss.
- License 许可
- Apache-2.0
- 最近更新
- 2026-08-31
- Stars
- ★ 3.6w
- Forks
- 4.4k
01 前言 Front Matter
- name
- detecting-broken-object-property-level-authorization
- tags
- [ "api-security", "bopla", "owasp-api3", "mass-assignment", "excessive-data-exposure", "property-level-authorization", "api-testing", "penetration-testing" ]
- author
- mahipal
- domain
- cybersecurity
- license
- Apache-2.0
- version
- 1.0
- nist_csf
- [ "PR.PS-01", "ID.RA-01", "PR.DS-10", "DE.CM-01" ]
- subdomain
- api-security
- description
- Detect and test for OWASP API3:2023 Broken Object Property Level Authorization (BOPLA), covering excessive data exposure in API responses and mass assignment via injected request-body properties. Use when reviewing API responses/requests for over-exposed or over-writable object fields, or building detection rules and test cases for property-level authorization gaps that object-level checks miss.
- mitre_attack
- [ "T1190", "T1213", "T1212" ]
02 资源文件 3
- 许可证LICENSE11.0 KB
- 参考文档references/api-reference.md2.1 KB
- 脚本scripts/agent.py6.4 KB
03 SKILL.md 正文
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
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:
// GET /api/v1/users/123
// Response includes sensitive fields the UI doesn't display:
{
"id": 123,
"username": "john_doe",
"email": "john@example.com",
"name": "John Doe",
"ssn": "123-45-6789", // Sensitive - not needed by UI
"salary": 95000, // Sensitive - not needed by UI
"internal_notes": "VIP client", // Internal - should not be exposed
"password_hash": "$2b$12...", // Critical - never expose
"role": "admin", // May enable privilege discovery
"created_by": "system_admin", // Internal metadata
"credit_card_last4": "4242" // PCI compliance violation
}
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
#!/usr/bin/env python3
"""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 # "excessive_exposure" or "mass_assignment"
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()
# Handle both single object and list responses
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]: # Check first 5 objects
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}"
# First, get the current object state
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 = {}
# Test each mass assignment field
for field_name, injected_value in self.MASS_ASSIGNMENT_FIELDS:
if field_name in original_data:
# Field exists - test if we can modify it
original_value = original_data[field_name]
if original_value == injected_value:
continue # Already has this value
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 the field was actually modified
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)
# Restore original value if possible
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 query to discover available fields
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
# Server-side: Explicit property allowlists
class UserSerializer:
# Only expose these fields - never use to_json() or to_dict()
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}
# Mass assignment protection - explicit allowlist for writable fields
WRITABLE_FIELDS = {'name', 'email', 'phone', 'avatar_url', 'preferences'}
def update_user(user_id, request_data, requesting_user):
# Filter out any fields not in the allowlist
safe_data = {k: v for k, v in request_data.items() if k in WRITABLE_FIELDS}
# Apply updates only with safe data
User.objects.filter(id=user_id).update(**safe_data)
References
- OWASP API3:2023: https://owasp.org/API-Security/editions/2023/en/0xa3-broken-object-property-level-authorization/
- Salt Security BOPLA Analysis: https://salt.security/blog/api3-2023-broken-object-property-level-authorization
- Wallarm BOPLA Guide: https://lab.wallarm.com/api32023-broken-object-property-level-authorization/
- API Security News BOPLA: https://apisecurity.io/owasp-api-security-top-10/api3-2023-broken-object-property-level-authorization/
- CloudDefense BOPLA: https://www.clouddefense.ai/owasp/2023/3
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