hunt-graphql

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name: hunt-graphql description: Hunting skill for graphql vulnerabilities. Built from 12 public bug bounty reports across IDOR via node() / GID, mutation IDOR including AI/LLM features, cross-tenant IDOR, SSRF via argument, batching-DoS, query-cost-bypass, SQLi via argument, broken-object-level-authz, auth-bypass via unscoped mutations, and PII exposure from missing field-level authz. Use when hunting graphql on any target. sources: hackerone_public, github, gitlab_security report_count: 26

Crown Jewel Targets

GraphQL vulnerabilities are high-value because the attack surface is both broad and deep — a single endpoint can expose entire data models, privilege escalation paths, and cross-API state confusion. Highest payouts occur in:

• Platform APIs (GitHub, Shopify, Stripe-tier targets) where GraphQL mutations interact with REST APIs managing the same resources
• Race conditions between GraphQL mutations and REST endpoints where state synchronization is non-atomic — these hit medium-to-high severity reliably
• Authorization persistence bugs where team/org/repo membership state is controlled by one API but readable/writable by another
• B2B SaaS platforms where one tenant affecting another via schema traversal = critical
• Internal admin GraphQL endpoints accidentally exposed to lower-privilege users

The GitHub reports demonstrate the crown jewel pattern: privilege that should be revoked persists because two APIs disagree on ground truth.

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Attack Surface Signals

URL Patterns:

/graphql
/api/graphql
/v1/graphql
/query
/gql
/graph
/api/v2/graphql
/internal/graphql

Response Headers:

Content-Type: application/json  (with query body)
X-Request-Id + no REST-style path params = likely GraphQL

JavaScript Source Patterns:

// grep for these in JS bundles
"query {"
"mutation {"
"__typename"
"apollo"
"ApolloClient"
"graphql-tag"
"gql`"
"operationName"
"GRAPHQL_URI"

Tech Stack Signals:

• Apollo Server/Client in JS bundles
• Relay in React apps
• graphene or strawberry (Python), graphql-ruby, gqlgen (Go), Lighthouse (Laravel)
• POST requests with {"query": "..."} body shape in Burp history
• __schema or __type in any response = confirmed GraphQL

Recon Sources:

• github.com search: "graphql" site:target.com
• Wayback Machine for /graphql paths
• JS bundle scanning with LinkFinder or getallurls

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Step-by-Step Hunting Methodology

1. Discover the endpoint — spider JS bundles, check /graphql, /api/graphql, review Burp passive scan hits for application/json POST with query fields

2. Test introspection — send the full introspection query. Even if blocked, try field-level enumeration:

   { __typename }
   

   If that returns, introspection may be partially blocked but the schema is discoverable

3. Map the full schema — use InQL (Burp extension) or graphql-voyager to visualize relationships. Specifically look for:

   • Mutations that modify ownership, permissions, or membership
   • Mutations that mirror REST API functionality

4. Identify REST/GraphQL overlap — document every resource that can be modified via BOTH REST and GraphQL. These dual-write surfaces are your RC targets.

5. Test authorization boundaries per mutation — replay mutations as lower-privilege users. Does the server enforce the same authz as the equivalent REST call?

6. Hunt cross-API state desync — find sequences where:

   • REST action should revoke access
   • GraphQL mutation re-grants or preserves it
   • Test the ordering: REST first → GraphQL → check state; then GraphQL first → REST → check state

7. Test for persistent privilege after role/membership changes — remove a user via REST, then call the corresponding GraphQL mutation for that resource. Query current state via both APIs and compare.

8. Probe for IDOR in node IDs — GraphQL global IDs often encode object type + ID. Swap IDs across object boundaries and across account contexts.

9. Check batch query abuse — send arrays of operations to bypass rate limiting or amplify enumeration.

10. Document the exact reproduction chain — for RC bugs, time-based steps must be reproducible deterministically.

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Payload & Detection Patterns

Full Introspection Query:

{
  __schema {
    types {
      name
      fields {
        name
        type {
          name
          kind
        }
      }
    }
  }
}

Minimal Introspection Probe (bypass attempt):

{ __typename }

curl introspection test:

curl -s -X POST https://target.com/graphql \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -d '{"query":"{ __schema { queryType { name } } }"}' | jq .

Field suggestion probe (bypass blind introspection blocks):

{ unknownField }

If response returns "Did you mean: [realFieldName]?" — schema is enumerable despite introspection being disabled.

Batch query amplification:

[
  {"query": "{ user(id: 1) { email } }"},
  {"query": "{ user(id: 2) { email } }"},
  {"query": "{ user(id: 3) { email } }"}
]

Subscription hijacking (cross-user channel access):

subscription { messageAdded(channelId: "OTHER_USERS_CHANNEL") { content sender { email } } }

If subscriptions lack per-user scoping, an attacker can receive real-time events from another user's channel or conversation.

Multi-code OTP/2FA brute-force via alias batching:

mutation { 
  v1: verifyOtp(code:"000001"){token} 
  v2: verifyOtp(code:"000002"){token}
  v3: verifyOtp(code:"000003"){token}
}

A single GraphQL request aliases the same mutation with different OTP codes. Combined with parallel HTTP, this defeats per-request rate limiting and compresses brute-force attempts into fewer network round-trips.

RC desync test pattern (pseudo-sequence):

# Step 1: Grant access via REST
curl -X PUT https://api.target.com/repos/ORG/REPO/teams/TEAM \
  -H "Authorization: token ADMIN_TOKEN" \
  -d '{"permission":"admin"}'

# Step 2: Revoke via REST  
curl -X DELETE https://api.target.com/repos/ORG/REPO/teams/TEAM \
  -H "Authorization: token ADMIN_TOKEN"

# Step 3: Re-assert via GraphQL mutation
curl -X POST https://api.target.com/graphql \
  -H "Authorization: bearer ATTACKER_TOKEN" \
  -d '{"query":"mutation { updateTeamsRepository(input: {repositoryId: \"REPO_ID\", teamId: \"TEAM_ID\", permission: ADMIN}) { clientMutationId } }"}'

# Step 4: Verify persistent access
curl https://api.target.com/repos/ORG/REPO/teams \
  -H "Authorization: token ADMIN_TOKEN"

Grep for GraphQL in JS bundles:

grep -Eo '(query|mutation|subscription)\s+\w+\s*[\({]' bundle.js
grep -Eo '"(/[a-z0-9/_-]*graphql[a-z0-9/_-]*)"' bundle.js

GraphQL introspection & audit tooling:

# InQL (Burp extension) — visualize GraphQL schema and relationships
inql -t https://target/graphql --generate-queries

# Clairvoyance — brute-force field names when introspection is disabled
python3 clairvoyance.py -u https://target/graphql -H "Authorization: Bearer TOKEN" -w wordlist.txt -o schema.json

# GraphQL Cop — scan for common misconfigurations (introspection enabled, no depth limits, etc.)
graphql-cop -t https://target/graphql

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Common Root Causes

1. Dual-write without atomic locking — developers implement the same resource modification in both REST and GraphQL independently. Neither system is aware the other exists for that resource. State updates aren't serialized or compared.

2. Inconsistent authorization middleware — REST endpoints go through one auth layer (e.g., middleware chain), GraphQL resolvers go through a different resolver-level check. The same action, different enforcement.

3. GraphQL as "new REST" migration — teams add GraphQL mutations that mirror REST functionality without auditing the permission model. The GraphQL version is less mature and skips checks the REST version accumulated over time.

4. Introspection left on in production — default framework settings (Apollo, Graphene) enable introspection in all environments. Developers forget to disable it, treating it as "just documentation."

5. Node ID trust without re-authorization — GraphQL global IDs (base64("ObjectType:123")) are decoded and trusted without verifying the requesting user has access to that specific object.

6. Mutation side effects not mirrored — when a REST action triggers cascading effects (e.g., team removal cascades to permission revocation), the GraphQL equivalent mutation doesn't trigger the same cascades.

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Bypass Techniques

Defense: Introspection disabled

• Bypass via field suggestion errors — send invalid field names and parse "did you mean X?" responses
• Use clairvoyance to brute-force field names against a wordlist
• Check JS bundles for hardcoded query strings that reveal the schema

Defense: Depth limiting

• Fragment spread to increase effective depth without hitting the limiter:

fragment F on User { repos { teams { members { ...F } } } }

Defense: Rate limiting per IP

• Use batch operations (array of queries in one POST)
• Distribute across authenticated sessions

Defense: Auth checks on mutations

• Test with tokens at different privilege tiers (viewer, member, admin)
• Test unauthenticated — some mutations don't check session at all
• Test with tokens from different organizations — multi-tenant IDOR

Defense: WAF blocking __schema

• Alias the introspection field:

{ s: __schema { t: types { n: name } } }

• Use HTTP parameter pollution or alternate content-type headers

Defense: Operation whitelisting (persisted queries)

• Check if the server falls back to ad-hoc queries when the extensions.persistedQuery hash mismatches
• Look for a non-whitelisted endpoint (dev, staging, internal proxy)

Alias batching: when it wins races vs when it doesn't

A common claim is "alias batching defeats per-user rate limits and double-spend protections." Whether this actually wins depends on the resolver execution model:

| Resolver type | Behavior on aliased mutations | Alias batching wins races? | |---|---|---| | Multi-threaded / DataLoader-batched async | Aliases run concurrently, share state via batch | YES — single HTTP request can amplify a race-target N times | | Single-threaded / single-DB-connection per request | Aliases run serially; first mutation closes the door | NO — combine with parallel HTTP | | Distributed gateway (Apollo Federation) | Sub-queries dispatched concurrently to subgraphs | Depends on each subgraph |

Verification example (single-threaded Flask + SQLite resolver):

• 10 aliased redeemCoupon mutations in one request → only r1 succeeds, r2-r10 fail with already_redeemed. Alias batching alone is insufficient.
• The same 10 mutations as 20 parallel HTTP POSTs → 20 successes ($2000 from a $100 coupon).

Operator rule: treat alias batching as a single-RTT recon primitive. For race-target exploitation, combine with hunt-race-condition's parallel-HTTP / Turbo Intruder single-packet attack. Verified in docs/verification/phase2e-jwt-graphql-race.md Test 11 vs Test 12.

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Gate 0 Validation

1. What can the attacker DO right now? Must be a concrete action: access data they shouldn't see, retain privileges after revocation, modify another user's resources. "The schema is visible" alone is not enough — what does the schema unlock?

2. What does the victim LOSE? Must be a real asset: data confidentiality, access control integrity, org security guarantees. For the RC pattern: an org admin loses the guarantee that removing a team revokes all access. That's a security contract violation.

3. Can it be reproduced in 10 minutes from scratch? For RC/desync bugs: write the exact curl sequence. Run it twice. If the privilege persists