
Detecting Broken Object Property Level Authorization
FreeIdentify and test for API property-level authorization issues.
Free · Opens the source repo
What Detecting Broken Object Property Level Authorization does
The Detecting Broken Object Property Level Authorization skill is designed for security professionals who need to assess APIs for vulnerabilities related to property-level authorization. This skill specifically targets the OWASP API3:2023 Broken Object Property Level Authorization (BOPLA) category, which encompasses two critical issues: Excessive Data Exposure and Mass Assignment. These vulnerabilities can allow attackers to access sensitive information or manipulate data in ways that compromise the integrity of the application.
When using this skill, users can automate the detection of excessive data exposure, where an API returns more information than necessary, potentially leaking sensitive data. Additionally, it tests for mass assignment vulnerabilities, where an API accepts unexpected data fields, allowing unauthorized changes to user roles or other critical properties. This is particularly useful during security assessments, code reviews, or when developing security monitoring rules.
To effectively utilize this skill, users should have access to the target API's documentation, various user accounts with differing privilege levels, and tools like Burp Suite or Postman for testing. The skill requires Python 3.8+ and the requests library for executing automated tests. It provides a structured approach to identifying and documenting vulnerabilities, making it an essential tool for security analysts, penetration testers, and developers focused on API security.
By integrating this skill into your security testing workflow, you can enhance your ability to identify and remediate property-level authorization gaps that traditional object-level checks may overlook. This proactive approach helps ensure that your APIs are resilient against common attack vectors, ultimately safeguarding sensitive data and maintaining application integrity.
When to use it
Use this skill when conducting security assessments of APIs to uncover property-level authorization issues, especially during penetration testing or threat hunting.
When not to use it
This skill is not suitable for general application development or non-security related tasks, as its focus is strictly on identifying specific API vulnerabilities.
What you can build with it
API Security Assessment
Use this skill to evaluate APIs for property-level authorization vulnerabilities during a security audit.
Developing Security Monitoring Rules
Incorporate findings from this skill to create effective security monitoring rules for detecting BOPLA issues.
Penetration Testing
Utilize this skill during penetration tests to identify and document excessive data exposure and mass assignment vulnerabilities.
How to install Detecting Broken Object Property Level Authorization
View source1. Install with the skills CLI
npx skills add mukul975/anthropic-cybersecurity-skills/detecting-broken-object-property-level-authorization --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by mukul975Detecting 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
Frequently asked questions about Detecting Broken Object Property Level Authorization
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