detecting-broken-object-property-level-authorization▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Detect and test for OWASP API3:2023 Broken Object Property Level Authorization vulnerabilities including excessive data exposure and mass assignment attacks.
| 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
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": "[email protected]",
"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": "[email protected]",
"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
How to use detecting-broken-object-property-level-authorization on Cursor
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Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add detecting-broken-object-property-level-authorization
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches detecting-broken-object-property-level-authorization from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate detecting-broken-object-property-level-authorization. Access the skill through slash commands (e.g., /detecting-broken-object-property-level-authorization) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
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✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
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- 4Build expertise through regular use and experimentation
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Ratings
4.6★★★★★36 reviews- ★★★★★Sophia Zhang· Dec 20, 2024
Solid pick for teams standardizing on skills: detecting-broken-object-property-level-authorization is focused, and the summary matches what you get after install.
- ★★★★★Kofi Smith· Dec 16, 2024
detecting-broken-object-property-level-authorization fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★William Ghosh· Nov 11, 2024
detecting-broken-object-property-level-authorization has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Aisha Jackson· Nov 7, 2024
Useful defaults in detecting-broken-object-property-level-authorization — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Yusuf Reddy· Oct 26, 2024
detecting-broken-object-property-level-authorization is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Michael Thomas· Oct 2, 2024
Keeps context tight: detecting-broken-object-property-level-authorization is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Yash Thakker· Sep 13, 2024
Keeps context tight: detecting-broken-object-property-level-authorization is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ira Perez· Sep 9, 2024
I recommend detecting-broken-object-property-level-authorization for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Aisha Park· Sep 5, 2024
detecting-broken-object-property-level-authorization reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Fatima Park· Aug 28, 2024
detecting-broken-object-property-level-authorization reduced setup friction for our internal harness; good balance of opinion and flexibility.
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