detecting-broken-object-property-level-authorization

mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/detecting-broken-object-property-level-authorization
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summary

Detect and test for OWASP API3:2023 Broken Object Property Level Authorization vulnerabilities including excessive data exposure and mass assignment attacks.

skill.md
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

how to use detecting-broken-object-property-level-authorization

How to use detecting-broken-object-property-level-authorization on Cursor

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1

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
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/detecting-broken-object-property-level-authorization

The skills CLI fetches detecting-broken-object-property-level-authorization from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
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4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/detecting-broken-object-property-level-authorization

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.

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Use Cases

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Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

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. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 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

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

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Ratings

4.636 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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