hunt-fintech-graphql skill

by elementalsouls·elementalsouls/Claude-BugHunter·4.7k stars·MIT

Hunt fintech-specific GraphQL vulnerabilities: money-movement mutations (transfers, redemptions, withdrawals, card top-ups), ledger/balance/portfolio query IDOR, decimal-precision and rounding abuse, idempotency-key bypass enabling double-spend, KYC/PII field-level authorization gaps, and admin-override mutations reachable via mass assignment. Distinct from hunt-graphql, which owns generic GraphQL discovery and IDOR/mutation methodology — this skill owns the delta introduced when a GraphQL layer sits in front of a ledger, wallet, payments, banking, brokerage, or lending backend, where a resolver bug moves real money instead of just leaking data. Use when hunting a fintech, banking, payments, wallet, neobank, brokerage, or lending target that exposes a GraphQL API, or when a schema/response includes balance, transfer, ledger, redeem, quote, KYC, or account-linking fields.

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Install the hunt-fintech-graphql skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/elementalsouls/Claude-BugHunter.git /tmp/Claude-BugHunter
mkdir -p ~/.claude/skills
cp -r /tmp/Claude-BugHunter/skills/hunt-fintech-graphql ~/.claude/skills/hunt-fintech-graphql

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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:

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.

Money-movement mutations are almost always resolvers over a double-entry ledger. A single

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.

Decimals are attacker-controlled input, not display formatting. Amounts, exchange rates,

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.

KYC/PII fields sit next to routine account fields in the same type. User or Account

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.

Step-by-Step Hunting Methodology

transfer/withdraw — also redeemRewards, applyCoupon, upgradeTier, closeAccount (often refunds a balance), disputeTransaction (often provisionally credits).

Map every mutation that touches balance, whether directly or as a side effect. Not just

produce one ledger entry or several (debit sender, credit receiver, fee entry)? Multi-entry writes are the ones worth racing — see Stage 4.

For each money-movement mutation, identify the ledger write shape. Does one mutation call

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.

Test idempotency-key handling. Send the identical mutation (same idempotencyKey /

Confirm server-side rounding matches client-displayed rounding; a mismatch is directly monetizable.

Test decimal/precision edge cases on every amount-accepting argument — see Payload section.

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).

Probe cross-account IDOR on account/portfolio node IDs, same as hunt-idor/hunt-graphql,

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.

Check field-level authorization on KYC/PII fields by querying the shared User/Account

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: "..."})).

Look for admin-tier mutations reachable via mass assignment, not just a missing auth

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.

Test currency-argument consistency. Send a transfer/quote mutation with mismatched

hunt-race-condition for the parallel-HTTP escalation once alias batching alone confirms the resolver isn't serializing writes per-account.

Combine alias batching with money-movement mutations to test for double-spend — see

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.

Common Root Causes

the resolver trusts whatever the GraphQL client actually sends, because "the app always sends the right value."

Client-side amount/fee validation only. The UI computes and displays the correct amount;

separate sequential statements instead of inside a single transaction/lock — the race window this creates is exactly what alias batching + parallel HTTP exploits.

Non-atomic multi-entry ledger writes. Debit, credit, and fee entries are written as

(scientific notation, oversized strings), reintroducing floating-point rounding error into a system that was supposed to guarantee fixed-point precision.

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