hunt-fintech-graphql

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name: hunt-fintech-graphql description: Hunt fintech-specific GraphQL vulnerabilities category: security risk: offensive source: https://github.com/elementalsouls/Claude-BugHunter source_repo: elementalsouls/Claude-BugHunter source_type: community date_added: '2026-09-20' license: MIT license_source: https://github.com/elementalsouls/Claude-BugHunter/blob/main/LICENSE compatibility: Requires explicit written authorization for a target scope plus the relevant testing tools for this technique. Docs-only; helper scripts and commands not bundled. sources: owasp_api_top10_2023, public_research report_count: 0

⚠️ AUTHORIZED USE ONLY This skill is for educational purposes or authorized security assessments only. You must have explicit, written permission from the system owner before using this tool. Misuse of this tool is illegal and strictly prohibited.

Mandatory confirmation gate Before running any command that probes, exploits, changes, persists on, extracts data from, or attempts credential access against a target:

1. Ask the user to state the exact target URL, IP, account, or resource.
2. Ask the user to confirm written authorization and the permitted scope.
3. Show the exact command(s) and explain their expected effect.
4. Wait for explicit confirmation in the current conversation.

Without that confirmation, remain read-only and provide defensive guidance only. Prefer a sandbox, disposable VM, or controlled lab.

Why Fintech GraphQL Is a Different Risk Class

Generic GraphQL bugs (IDOR, mass assignment, introspection, batching abuse — see hunt-graphql) still apply here, but the blast radius changes completely: a resolver bug in a SaaS app leaks data, the same class of bug in a ledger mutation moves money. Three properties make fintech GraphQL backends a distinct hunting surface:

• Money-movement mutations are almost always resolvers over a double-entry ledger. A single GraphQL mutation (transferFunds, redeemRewards, withdrawToBank) can trigger multiple ledger writes (debit + credit + fee) that must be atomic. GraphQL's flexible input shape and alias batching make it easy to desynchronize those writes.
• Decimals are attacker-controlled input, not display formatting. Amounts, exchange rates, interest, and rewards points are usually passed as GraphQL scalars (Float, String, custom Decimal/Money scalar). How the resolver parses and rounds that value is exploitable surface in its own right — this barely exists in non-financial GraphQL APIs.
• KYC/PII fields sit next to routine account fields in the same type. User or Account types commonly expose ssnLast4, routingNumber, kycStatus, governmentIdUrl, or linkedBankAccount alongside displayName and email — one missing field-level authorization check on a type used everywhere in the schema fans out to every query that touches it.

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Attack Surface Signals

URL / schema naming patterns (in addition to hunt-graphql's generic /graphql list):

/graphql/ledger
/graphql/payments
/api/wallet/graphql
/internal/ledger-graphql
/banking/graphql

Field/type names worth grepping schema introspection or JS bundles for:

balance, availableBalance, pendingBalance, ledgerEntry, ledgerEntries
transferFunds, withdraw, redeem, topUp, reverseTransaction, adjustBalance
kycStatus, ssnLast4, routingNumber, accountNumber, governmentIdUrl
quoteExchangeRate, interestAccrued, rewardsPoints, portfolioValue
idempotencyKey, clientMutationId

Tech-stack tells specific to this vertical:

• Plaid/Stripe/Dwolla/Marqeta wrapped behind an internal GraphQL gateway (bankLink, plaidLinkToken mutations)
• Apollo Federation with a dedicated ledger or payments subgraph — check for the subgraph's own introspection being reachable directly, bypassing the gateway's stitched-down schema
• Custom Money/Decimal/BigDecimal GraphQL scalar in the schema (scalar Money) — the parser for this scalar is worth fuzzing directly

Run hunt-graphql's discovery + introspection methodology first to get the schema; everything below assumes you already have (or have partially enumerated) a schema with money-movement types.

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Step-by-Step Hunting Methodology

1. Map every mutation that touches balance, whether directly or as a side effect. Not just transfer*/withdraw* — also redeemRewards, applyCoupon, upgradeTier, closeAccount (often refunds a balance), disputeTransaction (often provisionally credits).

2. For each money-movement mutation, identify the ledger write shape. Does one mutation call produce one ledger entry or several (debit sender, credit receiver, fee entry)? Multi-entry writes are the ones worth racing — see Stage 4.

3. Test idempotency-key handling. Send the identical mutation (same idempotencyKey / clientMutationId) twice, back-to-back and with a delay. A ledger write on the second call means idempotency isn't enforced server-side — replay = double-execute.

4. Test decimal/precision edge cases on every amount-accepting argument — see Payload section. Confirm server-side rounding matches client-displayed rounding; a mismatch is directly monetizable.

5. Probe cross-account IDOR on account/portfolio node IDs, same as hunt-idor/hunt-graphql, but specifically test whether a transferFunds-style mutation validates that the source account belongs to the authenticated caller — not just that some account with that ID exists. This is the fintech-specific IDOR: authz on the source of a debit is easy to forget when authz on the destination of a credit was correctly implemented (crediting an arbitrary account "looks safe" to a developer; debiting one clearly isn't, so it gets checked — but sometimes only one direction does).

6. Check field-level authorization on KYC/PII fields by querying the shared User/Account type from every context that returns it — not just the profile screen. A transaction type that embeds counterparty { ssnLast4 } is a common place for the check to be missing, because the developer authorized the top-level transaction query but didn't re-check field access on the nested counterparty.

7. Look for admin-tier mutations reachable via mass assignment, not just a missing auth check — e.g. an input object with a client-settable status or override field that a normal user's mutation shouldn't expose but that the resolver accepts anyway (updateTransaction(input: {id, status: "COMPLETED", amount: "..."})).

8. Test currency-argument consistency. Send a transfer/quote mutation with mismatched sourceCurrency/targetCurrency combinations the UI never generates (e.g. self-transfer with a currency conversion) and check whether the resolver's FX-rate lookup and the ledger write use the same rate — a TOCTOU window here is a direct arbitrage bug.

9. Combine alias batching with money-movement mutations to test for double-spend — see hunt-race-condition for the parallel-HTTP escalation once alias batching alone confirms the resolver isn't serializing writes per-account.

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Payload & Detection Patterns

Idempotency-key replay test:

mutation {
  transferFunds(input: {
    idempotencyKey: "test-key-001"
    sourceAccountId: "acc_1"
    destAccountId: "acc_2"
    amount: "10.00"
  }) { transactionId status }
}

Send twice with the identical idempotencyKey. Two successful, distinct transactionId values = idempotency not enforced.

Decimal-precision / rounding probes:

mutation { transferFunds(input: {sourceAccountId:"acc_1", destAccountId:"acc_2", amount: "0.001"}) { transactionId } }
mutation { transferFunds(input: {sourceAccountId:"acc_1", destAccountId:"acc_2", amount: "9999999999999999.99"}) { transactionId } }
mutation { transferFunds(input: {sourceAccountId:"acc_1", destAccountId:"acc_2", amount: "1e2"}) { transactionId } }
mutation { transferFunds(input: {sourceAccountId:"acc_1", destAccountId:"acc_2", amount: "-50.00"}) { transactionId } }

Sub-cent amounts test truncate-vs-round handling (repeat N times to accumulate a rounding-error balance drift); scientific notation and oversized values test whether the Money/Decimal scalar parser falls back to a native float/int with overflow or precision-loss behavior; negative amounts test whether the resolver assumes sign server-side or trusts the client's.

Alias-batched double-spend probe (confirm before escalating to parallel HTTP):

mutation {
  r1: redeemRewards(input: {rewardId: "rwd_1", accountId: "acc_1"}) { success }
  r2: redeemRewards(input: {rewardId: "rwd_1", accountId: "acc_1"}) { success }
  r3: redeemRewards(input: {rewardId: "rwd_1", accountId: "acc_1"}) { success }
}

If more than one alias succeeds against a single-use reward/coupon, the resolver doesn't serialize per-account/per-resource writes within a batched request — see hunt-race-condition for combining this with parallel HTTP POSTs to confirm real double-spend impact.

Source-account authorization probe (asymmetric IDOR check):

mutation {
  transferFunds(input: {
    sourceAccountId: "VICTIM_ACCOUNT_ID"
    destAccountId: "ATTACKER_CONTROLLED_ACCOUNT_ID"
    amount: "1.00"
  }) { transactionId status }
}

Run as the attacker's own session/token. Success = the resolver validated the destination is attacker-controlled (obviously required) but never validated that the source belongs to the caller.

Nested field-level PII probe:

query {
  transaction(id: "txn_123") {
    amount
    counterparty { displayName ssnLast4 routingNumber kycStatus }
  }
}

Query as a user with no relationship to the counterparty beyond a shared transaction; success on the nested PII fields is the finding even if the top-level transaction query correctly scoped the transaction itself.

Mass-assignment probe on admin-shaped input fields:

mutation {
  updateTransaction(input: {id: "txn_123", status: "COMPLETED", amount: "0.01"}) { id status }
}

Send as a non-admin user against a mutation the client UI never exposes these fields for; a schema that accepts them anyway is mass assignment onto ledger state.

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Common Root Causes

1. Client-side amount/fee validation only. The UI computes and displays the correct amount; the resolver trusts whatever the GraphQL client actually sends, because "the app always sends the right value."
2. Non-atomic multi-entry ledger writes. Debit, credit, and fee entries are written as separate sequential statements instead of inside a single transaction/lock — the race window this creates is exactly what alias batching + parallel HTTP exploits.
3. Money/Decimal scalar falls back to native float parsing under edge-case input (scientific notation, oversized strings), reintroducing floating-point rounding error into a system that was supposed to guarantee fixed-point precision.
4. Idempotency keys are stored but never checked before executing the write — the key is logged for support/debugging purposes, not used as a dedup gate.
5. Field-level authorization implemented per top-level query, not per type. A User/Account type's sensitive fields are protected when queried directly (me { ssnLast4 }) but not when the same type is returned nested inside an unrelated query (transaction { counterparty {...} }).
6. Source-account ownership check missing while destination-account existence check is present — see methodology step 5. Debiting looks dangerous so it gets reviewed; the "does this account belong to the caller" check quietly only gets applied to the credited side.
7. Admin/internal mutations reuse the same input type as the public mutation, just with extra optional fields — nothing at the resolver layer strips those fields for non-admin callers.

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Gate 0 Validation

Money-movement findings need a stricter bar than a typical GraphQL IDOR — "the query returns someone else's balance" is real impact; "I sent a malformed amount and got a 400" is not.

1. Did an actual ledger write occur, and can you show it? Query the account balance before and after — a state change (not just a 200/success response body) is the proof.
2. Is the win deterministic, not a timing fluke? For race/double-spend findings, reproduce twice from a clean state. If it only works under specific load conditions, document the window honestly rather than claiming guaranteed exploitability.
3. Does the finding move value the attacker didn't have, or reveal data they shouldn't see — not just "the mutation accepted an unexpected input type and the API returned an error message." A verbose GraphQL error leaking a stack trace on a malformed Money scalar is a hunt-source-leak-class finding, not a fintech-logic one — don't conflate the two in a report.

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Related Skills & Chains

• hunt-graphql — parent skill for generic GraphQL discovery, introspection bypass, node-ID IDOR, and alias-batching mechanics. Load this skill first; hunt-fintech-graphql assumes that methodology and only adds the money-movement-specific delta.
• hunt-business-logic — coupon/reward double-redemption and other logic-flaw patterns generalize directly to redeemRewards/applyCoupon-style mutations here.
• hunt-race-condition — the escalation path once alias batching alone confirms a money-movement mutation doesn't serialize writes: combine with parallel-HTTP / single-packet attack for a deterministic double-spend PoC.
• hunt-api-misconfig — mass assignment and JWT-claim tampering patterns apply directly to admin-shaped GraphQL input objects reachable by normal users.
• hunt-idor — the source-account-vs-destination-account asymmetric authz pattern (step 5) is a fintech-specific instance of the general IDOR-on-mutation-argument class.
• evidence-hygiene — balance screenshots and ledger-entry PoCs need the same cookie/PII redaction discipline as any other capture, plus care that a real account number/balance from a live financial account is never included verbatim.
• triage-validation — apply Gate 0 above before drafting; a fintech program's triage team will kill anything without a demonstrated ledger state change immediately.

When to Use

• You have explicit, written authorization to assess the target in scope, and the task matches this skill's vulnerability class or technique within a bug-bounty or penetration-test engagement.
• You need the recon, exploitation, or validation workflow described below — executed strictly inside the approved scope.

Limitations

• Authorized scope only: the confirmation gate above is mandatory before any probing, exploitation, or credential-access command.
• Docs-only import: upstream helper scripts, commands, engine, and research assets are not bundled; reinstall tooling from the source repo when needed.
• Validate every finding (see triage-validation) before reporting; report via report-writing. Prefer a sandbox, disposable VM, or controlled lab.

Example

# Read-only first step; confirm scope before anything active.
cat scope.txt  # target list from the authorized engagement brief

Adapted from elementalsouls/Claude-BugHunter (MIT); frontmatter, When to Use/Limitations, and safety boundaries added for upstream compliance. Docs-only import: executable helpers, commands, engine, and research assets not bundled.