REST APIs, GraphQL & Modern API Architecture

This is Chapter 5 of the Web Fundamentals series — Notebook 6. The last two chapters were about how the browser carries identity (sessions/tokens) and how it isolates origins (SOP/CORS). Both increasingly serve one thing: an API. Modern applications are frequently a thin front-end (a single-page app or a mobile client) talking to a back-end API over HTTP/JSON. The HTML “page” is a shell; the data and the actions live behind API endpoints. That architectural shift moved the security-critical surface with it. The bugs are no longer only in server-rendered templates — they’re in the authorization checks on GET /api/users/1234, in whether PATCH /api/account lets you set fields you shouldn’t, in whether a GraphQL query can ask for data it was never meant to return.

This chapter builds the two dominant API styles — REST and GraphQL — from absolute scratch, adds a brief tour of gRPC and realtime APIs, and then walks the OWASP API Security Top 10: the specific, repeatable ways APIs get broken. If web app security in the 2000s was “find the SQL injection,” API security today is “find the object the server forgot to check you’re allowed to see.” That class — Broken Object Level Authorization — is the single most common serious API bug in existence, and by the end of this chapter you’ll test for it in your sleep.

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Part 1: What an API Is, and Why the App Is Now the Client

An API (Application Programming Interface) is a contract that lets one program talk to another. A web API exposes that contract over HTTP: a set of URLs (endpoints) that accept structured requests and return structured responses (almost always JSON today). Instead of the server building an HTML page, it returns data, and the client (browser JS, a mobile app, another backend) decides what to do with it.

flowchart LR
    subgraph Client
      A[SPA / Mobile app]
    end
    subgraph Server
      B[API endpoints] --> C[(Database)]
      B --> D[Auth / business logic]
    end
    A -->|"HTTP + JSON<br/>GET /api/orders"| B
    B -->|"200 [{...}]"| A

This “fat client, API back-end” split has three security consequences that frame the whole chapter:

1. The client is untrusted and fully visible. Anything the front-end “checks” (a hidden admin button, a disabled field, a client-side role check) is cosmetic. The attacker doesn’t use your JavaScript — they call the API directly with curl or Burp. Every security decision must be enforced server-side, on the endpoint. A control that lives only in the SPA does not exist.

2. The endpoints are enumerable. The front-end’s own JS reveals the API’s shape — routes, parameter names, ID formats. Open the browser network tab and you have the API map. Attackers read your bundle, your Swagger/OpenAPI doc, your GraphQL introspection, and enumerate from there.

3. Object IDs travel in the open. GET /api/invoices/8842 puts the object identifier right in the URL. If the server returns invoice 8842 without checking that this caller owns it, you change 8842 to 8843 and read someone else’s invoice. That’s BOLA/IDOR, and it’s endemic precisely because the API design surfaces the IDs.

The mental model for the rest of the chapter: an API is a set of doors (endpoints), each opening onto objects (data) and functions (actions). Security is entirely about whether each door checks (a) who you are (authentication), (b) whether you may open this particular door (function-level authz), and (c) whether you may touch this particular object behind it (object-level authz). Almost every API bug is a missing one of those three checks.

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Part 2: REST from Scratch — Resources, Verbs, Status Codes

REST (Representational State Transfer) is an architectural style, not a protocol. Its core idea: model everything as resources identified by URLs, and act on them with HTTP’s existing verbs. A resource is a noun (/users, /orders/42, /orders/42/items); the verb says what to do to it.

GET    /api/orders          # list orders (a collection)
POST   /api/orders          # create a new order
GET    /api/orders/42       # read one order
PUT    /api/orders/42       # replace order 42 wholesale
PATCH  /api/orders/42       # partially update order 42
DELETE /api/orders/42       # delete order 42

The verbs carry semantic promises (recall Chapter 2’s method section):

Verb	Meaning	Safe?	Idempotent?
GET	Read, no side effects	✅	✅
POST	Create / non-idempotent action	❌	❌
PUT	Replace entire resource	❌	✅
PATCH	Partial update	❌	❌ (usually)
DELETE	Remove resource	❌	✅

Safe = no state change; idempotent = doing it twice equals doing it once. These aren’t just trivia: a state-changing GET (a GET /orders/42/cancel) violates the “safe” promise and is directly CSRF-able even under SameSite=Lax (Chapter 3). If you see actions hidden behind GET, that’s a design smell and often a bug.

REST leans on HTTP status codes to signal outcome, and the codes matter for security testing because they’re an oracle — they leak whether things exist and whether you’re authorized:

Code family	Meaning	Security signal
2xx	Success	The action worked — did it work when it shouldn’t have?
400	Bad request	Input rejected — fuzz to learn validation rules
401	Unauthenticated	“Who are you?” — no/invalid credentials
403	Forbidden	“I know you, but no” — authorization boundary
404	Not found	Resource absent — or hidden to avoid leaking existence
429	Too many requests	Rate limit hit — probe its bounds
500	Server error	Unhandled case — often leaks stack traces / logic

The 401 vs 403 vs 404 distinction is a real information leak. If GET /api/users/9999 returns 403 for a user that exists and 404 for one that doesn’t, you can enumerate valid user IDs by watching the code. Well-designed APIs return a uniform 404 (or 403) for “you can’t have this,” regardless of existence, to avoid the oracle.

A note on “RESTful” maturity (the Richardson model). Real APIs sit at different levels of REST purity: Level 0 (one endpoint, everything POSTed — basically RPC over HTTP), Level 1 (resources but one verb), Level 2 (resources + proper verbs + status codes — where most good APIs live), and Level 3 (Level 2 + hypermedia/HATEOAS links in responses). You rarely need to police purity, but recognizing the level tells you how to enumerate: a Level-0 “API” is a single fat endpoint with an action parameter, and its bugs cluster in that parameter.

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Part 3: A REST Request End to End — Headers, Body, Auth

Let’s make a real authenticated REST call and name every part, because each part is a place a bug can live.

POST /api/orders HTTP/1.1
Host: api.shop.com
Authorization: Bearer eyJhbGciOiJIUzI1NiI...        # who you are (Chapter 3)
Content-Type: application/json                        # body format
Accept: application/json                              # desired response format
Idempotency-Key: 3f9c-...                             # dedupe retried POSTs
X-Request-ID: ...                                     # tracing

{"productId": 551, "quantity": 2}

• Authorization: Bearer <token> carries identity. The server verifies it (signature, exp, audience — Chapter 3) and derives who is calling. Bugs: accepting an unverified token, not re-checking exp, trusting a client-supplied X-User-Id header instead.
• Content-Type: application/json tells the server how to parse the body. Bugs: content-type confusion (server parses XML → XXE; server accepts application/x-www-form-urlencoded and skips CSRF checks meant only for JSON).
• Body as JSON carries the parameters. Bugs: mass assignment (Part 8) if the server binds unknown fields straight onto a model; type-juggling if it coerces "quantity": "-1".
• Idempotency-Key lets the client safely retry a POST without double-charging. Its absence is a reliability bug; its presence but weak validation can enable replay or race conditions.

A typical successful response:

HTTP/1.1 201 Created
Content-Type: application/json
Location: /api/orders/9931

{"id": 9931, "productId": 551, "quantity": 2, "status": "pending", "userId": 42}

Notice "userId": 42 in the response. That’s excessive data exposure (Part 9) if the client only needed the order status — the API returned an internal field the front-end happens to ignore, but an attacker reading the raw response does not ignore it. APIs constantly over-return, trusting the client to filter; the attacker never filters.

Authentication mechanisms you’ll meet on REST APIs:

Mechanism	How	Notes
Bearer token / JWT	Authorization: Bearer <jwt>	Most common for SPAs/mobile; see Chapter 3
API key	X-API-Key: <key> or ?api_key=	Static secret; key-in-URL leaks via logs
Session cookie	Cookie: session=...	Same-site web apps; needs CSRF defense
OAuth access token	Bearer, issued by an IdP	Delegated; validate aud/scope
mTLS	Client certificate	Machine-to-machine, high assurance

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Part 4: GraphQL from Scratch — One Endpoint, a Typed Graph

GraphQL is a query language for APIs and a runtime for fulfilling those queries. It inverts REST’s model. Instead of many endpoints each returning a fixed shape, GraphQL exposes one endpoint (/graphql) and lets the client specify exactly what data it wants, shaped how it wants, in a single request.

The server publishes a schema — a strongly-typed graph of the data and the operations:

type User {
  id: ID!
  name: String!
  email: String!
  orders: [Order!]!
}
type Order {
  id: ID!
  total: Float!
  items: [Item!]!
}
type Query {
  me: User
  user(id: ID!): User
  orders: [Order!]!
}
type Mutation {
  updateProfile(name: String, email: String): User
  deleteOrder(id: ID!): Boolean
}

The client sends a query (read) or mutation (write) asking for a precise sub-graph:

# Request only the fields you need, and traverse relationships in one round trip
query {
  me {
    name
    orders {
      id
      total
      items { name }
    }
  }
}

POST /graphql HTTP/1.1
Content-Type: application/json
Authorization: Bearer eyJ...

{"query": "query { me { name orders { id total } } }"}

The response mirrors the query’s shape exactly:

{ "data": { "me": { "name": "Alice", "orders": [{"id":"9931","total":42.0}] } } }

The wins are real: no over-fetching (ask for exactly the fields you need), no under-fetching (get related data in one round trip instead of N REST calls), and a self-documenting typed schema. But each of those strengths becomes an attack surface:

flowchart TD
    A[GraphQL strengths] --> B[Client picks fields<br/>-> excessive exposure if authz per-field missing]
    A --> C[Traverse the graph<br/>-> deep/nested queries = DoS]
    A --> D[Typed self-doc schema<br/>-> introspection maps everything]
    A --> E[One endpoint<br/>-> WAF/rate-limit rules built for REST miss it]

• The client chooses fields, so authorization can’t live on the endpoint — it must live on every field and object resolver. Miss one and that field leaks.
• The client traverses the graph arbitrarily deep (orders { items { product { seller { orders { ... } } } }), which is a denial-of-service lever (Part 10).
• The schema is introspectable — by default a client can ask GraphQL to describe its entire schema, handing an attacker the full map (Part 10).
• One endpoint means REST-oriented WAF rules, per-route rate limits, and logging often don’t understand GraphQL and miss abuse.

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Part 5: REST vs GraphQL vs gRPC — Choosing and Attacking Each

A quick comparative map, because the style determines where the bugs are and how you enumerate.

Dimension	REST	GraphQL	gRPC
Endpoints	Many (per resource)	One (/graphql)	Many (service methods)
Payload	JSON	JSON (typed query)	Protobuf (binary)
Client picks fields	No (fixed shapes)	Yes	No
Discovery	OpenAPI/Swagger, JS bundle	Introspection	.proto files / reflection
Over/under-fetch	Common	Solved	N/A
Transport	HTTP/1.1, HTTP/2	HTTP	HTTP/2
Typical bug locus	Per-endpoint authz, IDOR	Per-field authz, introspection, depth DoS	Method authz, proto fuzzing
Rate-limiting	Per route (easy)	Per query cost (hard)	Per method

gRPC in one paragraph. gRPC is a high-performance RPC framework using Protocol Buffers (a compact binary format) over HTTP/2. You define services and messages in a .proto file, and tooling generates client/server stubs. It’s dominant for internal microservice-to-microservice traffic. Security-wise: the binary payload isn’t human-readable (use grpcurl and the .proto to interact), auth is usually mTLS or a token in metadata, and the classic bugs are missing per-method authorization and trusting a service just because it’s “internal.” If you obtain the .proto (or server reflection is on), you can enumerate and call methods with grpcurl exactly like hitting REST endpoints.

WebSockets / realtime (Chapter 4’s CSWSH) and webhooks round out modern API surfaces. Webhooks — where a server POSTs events to a URL you registered — invert the trust direction and bring their own bug (SSRF via the webhook URL, and forged webhook deliveries if the signature isn’t verified). We flag them here and treat SSRF fully in a later chapter.

Concretely, when a gRPC server has reflection enabled (common in dev, sometimes left on in prod), grpcurl enumerates and calls methods with no .proto file at all:

# List services exposed via server reflection
grpcurl -plaintext api.internal:50051 list
# -> shop.OrderService
#    shop.AdminService        <-- an admin service you weren't supposed to see

# List methods on a service, then inspect one
grpcurl -plaintext api.internal:50051 list shop.AdminService
# -> shop.AdminService.PromoteUser

# Call a method (metadata carries the token, like Authorization in REST)
grpcurl -plaintext -H "authorization: Bearer $TOK" \
  -d '{"userId": 42}' api.internal:50051 shop.AdminService.PromoteUser

If AdminService.PromoteUser only checks that a token is present (not that it’s an admin’s), that’s BFLA over gRPC — identical bug, binary transport. Reflection-off + a leaked .proto gets you the same reach via grpcurl -proto shop.proto.

The practical upshot for a tester: identify the style first, because it dictates the recon. REST → enumerate routes (Swagger, JS, ffuf). GraphQL → run introspection, then reason about per-field authz and depth. gRPC → get the .proto/reflection, drive with grpcurl.

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Part 6: The OWASP API Security Top 10 — The Map of API Bugs

Before the deep dives, here is the terrain. OWASP maintains a dedicated API Security Top 10 because API bugs differ from the classic web Top 10 — they skew heavily toward authorization failures rather than injection.

ID	Name	One-line
API1	Broken Object Level Authorization (BOLA/IDOR)	You can access objects you don’t own by changing an ID
API2	Broken Authentication	Weak login/token handling, credential stuffing, JWT flaws
API3	Broken Object Property Level Authorization	Mass assignment + excessive data exposure (read/write the wrong fields)
API4	Unrestricted Resource Consumption	No rate/size limits → DoS and cost abuse
API5	Broken Function Level Authorization (BFLA)	You can call admin functions as a normal user
API6	Unrestricted Access to Sensitive Business Flows	Automating a flow (buying all stock, mass signups)
API7	Server-Side Request Forgery (SSRF)	API fetches an attacker-supplied URL
API8	Security Misconfiguration	Debug on, verbose errors, missing headers, permissive CORS
API9	Improper Inventory Management	Forgotten /v1/ endpoints, undocumented hosts, shadow APIs
API10	Unsafe Consumption of 3rd-Party APIs	Trusting upstream API data blindly

The three that dominate real findings — and that we lab in depth — are API1 (BOLA/IDOR), API5 (BFLA), and API3 (mass assignment / excessive exposure). They share a root cause: the server authenticated you but forgot to authorize the specific object, function, or field. Authentication answers “who are you”; authorization answers “may you do this, to that.” APIs overwhelmingly nail the first and botch the second.

flowchart LR
    A[Request arrives] --> B{Authenticated?<br/>valid token}
    B -->|No| Z[401]
    B -->|Yes| C{Authorized for this<br/>FUNCTION? API5}
    C -->|No| Y[403]
    C -->|Yes| D{Authorized for this<br/>OBJECT? API1}
    D -->|No| X[403]
    D -->|Yes| E{Only the FIELDS<br/>you may see/set? API3}
    E -->|No| W[leak / mass-assign]
    E -->|Yes| OK[Safe response]

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Part 7: API1 — Broken Object Level Authorization (BOLA / IDOR)

This is the king of API bugs. BOLA (Broken Object Level Authorization), historically called IDOR (Insecure Direct Object Reference), is when an endpoint uses a client-supplied identifier to fetch an object but fails to verify the caller is allowed that specific object.

The vulnerable pattern:

# VULNERABLE: fetches by id, never checks ownership
@app.route("/api/invoices/<int:iid>")
def get_invoice(iid):
    return db.invoices.find(id=iid)     # returns ANY invoice, to ANYONE authenticated

The attack is embarrassingly simple — change the number:

# You are authenticated as user 42, whose own invoice is 8842
curl -s https://api.shop.com/api/invoices/8842 -H "Authorization: Bearer $TOK"   # yours
curl -s https://api.shop.com/api/invoices/8843 -H "Authorization: Bearer $TOK"   # someone else's!
# -> returns invoice 8843 belonging to another user. That's BOLA.

The fix is one authorization check binding the object to the caller:

# FIXED: the query is scoped to the authenticated user
@app.route("/api/invoices/<int:iid>")
def get_invoice(iid):
    inv = db.invoices.find(id=iid)
    if inv is None or inv.user_id != current_user.id:
        abort(404)          # 404 (not 403) so you don't leak existence
    return inv

Where BOLA hides beyond the obvious:

• Nested/related objects: GET /api/orders/42/items/7 — the server checks you own order 42 but not that item 7 belongs to it.
• Non-numeric IDs give false comfort. UUIDs are harder to guess but not an access control — if a UUID leaks (in a URL, an email, another API response), the object is still readable. Never rely on ID unpredictability as authorization.
• State-changing BOLA is worse: DELETE /api/comments/999 or PATCH /api/users/1234 without ownership checks lets you delete or edit others’ data.
• Batch/GraphQL BOLA: asking for user(id: 1234) in GraphQL is the same bug if the resolver doesn’t check.

Bug-bounty angle — the standard test. Create two accounts (A and B). Do an action as B, capture the object ID. Replay the request as A with B’s object ID. If A gets B’s data, it’s BOLA. Burp’s Autorize and AuthMatrix extensions automate exactly this: they replay every request with a low-privileged (or other-user) token and flag responses that come back 200 with data. Also try parameter pollution and type juggling on the ID (id=8843, id=8843.0, id[]=8843, id=8843%00) to bypass naive checks.

Blue team detection: BOLA shows up as a single authenticated principal accessing an unusually wide range of object IDs, or sequential-ID sweeps. Log (user_id, object_id, object_owner) on sensitive reads and alert when user_id != object_owner succeeds, or when one user touches many distinct owners’ objects in a short window.

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Part 8: API3a — Mass Assignment (Writing Fields You Shouldn’t)

Mass assignment (a.k.a. autobinding, object injection) happens when an API takes the JSON body and binds all of its fields directly onto a data model — including fields the client was never supposed to control. The classic is a privilege field.

The vulnerable pattern (Node/Mongoose-style, but every ORM has its version):

// VULNERABLE: spreads the whole request body onto the user model
app.patch("/api/account", (req, res) => {
  User.findByIdAndUpdate(req.user.id, { ...req.body });   // binds EVERYTHING
});

The API expects {"name": "...", "email": "..."}. The attacker sends extra fields:

curl -s -X PATCH https://api.shop.com/api/account \
  -H "Authorization: Bearer $TOK" -H "Content-Type: application/json" \
  -d '{"name":"alice","role":"admin","emailVerified":true,"balance":999999}'
# If the model has role/emailVerified/balance and they're bound blindly -> privilege escalation

Now the attacker is admin, or has a verified email they never verified, or a fabricated balance. Mass assignment is a write-side authorization failure at the property level (OWASP API3).

How to find candidate fields: read a GET response for the same object — it reveals internal field names (role, isAdmin, accountType, verified, ownerId). Then try setting each via PATCH/PUT/POST. GraphQL introspection or the OpenAPI schema also hand you the field list.

The fixes:

// Allowlist the fields the client may set (the only robust approach)
const { name, email } = req.body;
User.findByIdAndUpdate(req.user.id, { name, email });

• Allowlist (bind only explicitly permitted fields) — not a denylist, which you’ll forget to update when a new sensitive field is added.
• Use separate DTOs/schemas for input vs output so internal fields are never bindable.
• Mark sensitive attributes non-writable at the ORM layer (readonly, guarded/fillable in Laravel, attr_readonly in Rails, exclude in serializers).

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Part 9: API3b — Excessive Data Exposure (Returning Fields You Shouldn’t)

The read-side twin of mass assignment. Excessive data exposure is when an API returns more data than the client needs, trusting the front-end to display only the safe parts. The attacker reads the raw response and gets the rest.

// GET /api/users/42 — the UI shows only name + avatar, but the API returns:
{
  "id": 42, "name": "Alice", "avatar": "/a.png",
  "email": "alice@corp.com",          // PII the UI never shows
  "passwordHash": "$2b$12$...",        // catastrophic
  "ssn": "***-**-1234",
  "isAdmin": false,
  "internalNotes": "flagged for review"
}

The UI renders name and avatar; the attacker curls the endpoint and harvests email, passwordHash, ssn, and internal flags. This is rampant because ORMs serialize whole objects by default (return user dumps every column) and developers filter in the template, not the API.

The fix mirrors Part 8: explicit output serialization — define exactly which fields each response includes, per audience.

# Return an explicit view, never the raw model
def public_user(u):
    return {"id": u.id, "name": u.name, "avatar": u.avatar}

Bug-bounty angle: always read the raw API response, not the rendered page. Diff what the UI shows against what the JSON contains. passwordHash, resetToken, mfaSecret, other users’ emails, internal role flags, and soft-deleted records hiding in a list endpoint are common finds. GraphQL makes this worse: if per-field authz is missing, you simply ask for the sensitive field (me { passwordHash }) and see if the resolver hands it over.

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Part 10: GraphQL-Specific Attacks — Introspection, Depth, Batching

GraphQL’s flexibility creates a distinct bug family beyond the shared authz issues.

10.1 Introspection — the free schema map

By default, GraphQL answers a special query describing its entire schema — every type, field, argument, and mutation. For an attacker that’s a complete map of the attack surface, including fields the UI never uses.

query { __schema { types { name fields { name } } } }

Tools like GraphQL Voyager, InQL (Burp extension), and graphql-cog/clairvoyance turn introspection into a browsable graph or reconstruct the schema even when introspection is disabled (by probing field suggestions in error messages). Disable introspection in production — but know that disabling it is obscurity, not authorization; per-field authz must still hold.

10.2 Deeply nested / recursive queries — DoS

Because the client controls traversal depth, a self-referential relationship (user → orders → buyer → orders → ...) lets a tiny query explode into a massive resolution tree:

query {
  me { orders { buyer { orders { buyer { orders { buyer { name } } } } } } }
}

Each level multiplies the work; a few kilobytes of query can pin the database and the server — Unrestricted Resource Consumption (OWASP API4). Defenses: query depth limiting, query complexity/cost analysis (assign each field a cost, cap the total), pagination requirements, and timeouts. Aliasing lets an attacker also request the same expensive field many times in one query (a: expensiveField b: expensiveField ...), so cost analysis must count aliases.

10.3 Batching to bypass rate limits and brute-force

GraphQL lets a single HTTP request carry many operations (via aliases or an array of queries). That’s a rate-limit bypass and a brute-force amplifier — instead of 1000 login requests (each rate-limited), send one request with 1000 aliased login mutations:

mutation {
  a: login(user:"admin", pass:"pass1") { token }
  b: login(user:"admin", pass:"pass2") { token }
  c: login(user:"admin", pass:"pass3") { token }
  # ...one HTTP request, thousands of guesses
}

If rate limiting counts HTTP requests rather than operations, this sails past it. Defenses: disable query batching where not needed, limit operations-per-request, and rate-limit at the resolver/operation level, not just the HTTP level.

flowchart TD
    A[GraphQL endpoint] --> B[Introspection ON?] --> B1[full schema map]
    A --> C[No depth limit?] --> C1[nested query DoS]
    A --> D[Batching ON?] --> D1[rate-limit bypass / brute force]
    A --> E[No per-field authz?] --> E1[ask for sensitive field directly]

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Part 11: API5 — Broken Function Level Authorization + Other Surface

BFLA (Broken Function Level Authorization) is BOLA’s sibling: instead of accessing an object you shouldn’t, you invoke a function (endpoint/operation) you shouldn’t — typically an admin action as a normal user.

# Normal user discovers the admin route (from JS, docs, or guessing) and just calls it
curl -s -X POST https://api.shop.com/api/admin/users/42/promote \
     -H "Authorization: Bearer $NORMAL_USER_TOK"
# If the endpoint only checks "are you logged in" and not "are you an admin" -> BFLA

BFLA also appears as verb tampering (the UI only exposes GET /api/users/42, but DELETE /api/users/42 is unprotected) and method/endpoint guessing (/api/v1/admin/... patterns). The fix is enforcing role/permission checks on every function, server-side, ideally via centralized middleware rather than per-handler if statements you’ll forget.

The rest of the Top 10, briefly, so the surface is complete:

• API2 Broken Authentication — everything from Chapter 3 (weak tokens, no rate limit on login, credential stuffing, JWT alg flaws). APIs frequently lack lockout/rate-limits that web login forms have.
• API4 Unrestricted Resource Consumption — no pagination caps, no upload size limits, no rate limits → DoS and cloud-bill abuse. Test with large ?limit=100000, huge payloads, and the GraphQL depth/batch tricks above.
• API6 Unrestricted Access to Sensitive Business Flows — the endpoint is authorized but the flow is automatable at scale: scalping all concert tickets, mass fake signups, coupon brute-forcing. Defenses are anti-automation (device fingerprinting, CAPTCHAs, per-flow limits), not just authz.
• API7 SSRF — an API that fetches a client-supplied URL (webhooks, “import from URL,” PDF render) can be pointed at internal services / cloud metadata (169.254.169.254). Full treatment in the SSRF chapter; flag any URL-accepting parameter.
• API8 Security Misconfiguration — verbose errors leaking stack traces, debug endpoints, missing security headers, permissive CORS (Chapter 4), default creds.
• API9 Improper Inventory Management — old /v1/ endpoints still live and unpatched, staging hosts, undocumented “shadow” APIs. Enumerate versions and hosts; the forgotten one is often the vulnerable one.
• API10 Unsafe Consumption of 3rd-Party APIs — blindly trusting data from an upstream API (following its redirects, rendering its HTML, deserializing its payload).

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Part 12: Hands-On Lab — Enumerate and Break a REST + GraphQL API

We stand up a tiny vulnerable API with both a REST and a GraphQL surface, then find BOLA, mass assignment, excessive exposure, and GraphQL introspection. Local and safe.

Tool from scratch: the API tester’s kit

• curl — scriptable HTTP; your baseline.
• Burp Suite — intercept, Repeater (replay/modify), Intruder (fuzz IDs), and extensions Autorize (auto-tests BOLA/BFLA by replaying with another user’s token) and InQL (GraphQL introspection → editable queries). Install: Burp → Extensions → BApp Store → Autorize / InQL.
• ffuf — content discovery for hidden routes: ffuf -u https://api/FUZZ -w api-wordlist.txt.
• graphw00f / clairvoyance / InQL — GraphQL fingerprinting and schema extraction.

12.1 Stand up the vulnerable API

# api.py — deliberately vulnerable REST + JSON
from flask import Flask, request, jsonify, abort
app = Flask(__name__)

USERS = {1: {"id":1,"name":"alice","email":"alice@x.com","role":"user","token":"tA"},
         2: {"id":2,"name":"bob","email":"bob@x.com","role":"admin","token":"tB"}}
INVOICES = {8842:{"id":8842,"user_id":1,"amount":100},
            8843:{"id":8843,"user_id":2,"amount":9000}}

def current_user():
    tok = request.headers.get("Authorization","").removeprefix("Bearer ")
    return next((u for u in USERS.values() if u["token"]==tok), None)

@app.route("/api/invoices/<int:iid>")
def invoice(iid):
    if not current_user(): abort(401)
    return jsonify(INVOICES.get(iid) or abort(404))     # BUG: no ownership check (BOLA)

@app.route("/api/users/<int:uid>")
def user(uid):
    if not current_user(): abort(401)
    return jsonify(USERS[uid])                            # BUG: returns token/email (excessive exposure)

@app.route("/api/account", methods=["PATCH"])
def account():
    u = current_user() or abort(401)
    u.update(request.get_json())                          # BUG: mass assignment (role bindable)
    return jsonify(u)

app.run(port=5003)

12.2 Exploit BOLA

python3 api.py &
# You are alice (token tA); your invoice is 8842. Try bob's invoice 8843:
curl -s localhost:5003/api/invoices/8843 -H "Authorization: Bearer tA"

{"amount": 9000, "id": 8843, "user_id": 2}

You just read another user’s $9000 invoice. BOLA confirmed.

12.3 Exploit excessive data exposure

curl -s localhost:5003/api/users/2 -H "Authorization: Bearer tA"

{"email": "bob@x.com", "id": 2, "name": "bob", "role": "admin", "token": "tB"}

The response leaked bob’s email and, catastrophically, his session token tB — which you can now replay to become admin bob. Excessive exposure chained into account takeover.

12.4 Exploit mass assignment

curl -s -X PATCH localhost:5003/api/account -H "Authorization: Bearer tA" \
  -H "Content-Type: application/json" -d '{"role":"admin"}'

{"email": "alice@x.com", "id": 1, "name": "alice", "role": "admin", "token": "tA"}

Alice is now admin — she set a field the API never meant to expose for writing. Mass assignment → privilege escalation.

12.5 The fixes, side by side

# BOLA fix — scope to owner:
inv = INVOICES.get(iid)
if not inv or inv["user_id"] != current_user()["id"]: abort(404)

# Excessive-exposure fix — explicit output view:
return jsonify({"id":u["id"], "name":u["name"]})

# Mass-assignment fix — allowlist writable fields:
body = request.get_json(); u["name"] = body.get("name", u["name"])

Re-run each attack against the fixed handlers: the invoice sweep returns 404, the user endpoint no longer leaks the token, and the role injection is ignored. Three one-line authorization checks close three critical bugs — which is the entire lesson of API security in miniature.

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Part 13: Detection & Defense Angle — Building APIs That Hold

Consolidated defensive guidance across the Top 10.

Authorization (the core):

• Enforce object-level checks on every read/write: bind the object to the authenticated principal (WHERE owner_id = :me), don’t just look it up by ID. Prefer scoping the query over post-hoc if checks.
• Enforce function-level checks centrally (middleware/policy layer), default-deny, so a new endpoint isn’t public by accident.
• Enforce property-level control both ways: allowlist writable fields (anti mass-assignment) and define explicit output schemas per audience (anti excessive-exposure).
• Return uniform 404/403 so status codes don’t become an existence oracle.

Resource & abuse control:

• Rate-limit per user/token and per operation, not just per IP/HTTP-request. For GraphQL, add depth limits, complexity/cost caps, and batch limits; disable introspection in prod.
• Cap pagination, payload, and upload sizes. Add timeouts. Add anti-automation on sensitive flows.

Configuration & inventory:

• Turn off debug/verbose errors in prod; strip stack traces. Set security headers; lock down CORS (Chapter 4). Rotate/scope API keys; never accept keys in URLs.
• Maintain an API inventory: know every version and host; retire old /v1/ endpoints; scan for shadow APIs. API9 is a governance problem as much as a code one.

Detection (blue team):

flowchart LR
    A[API gateway + app logs] --> B{Signal?}
    B -->|One user, many owners' object IDs| C[BOLA sweep]
    B -->|Normal token hitting /admin routes| D[BFLA attempt]
    B -->|PATCH/PUT with unexpected fields| E[Mass-assignment probe]
    B -->|Huge/nested GraphQL, aliased logins| F[DoS / brute force]
    C --> G[Alert, block, revoke token]
    D --> G
    E --> G
    F --> G

• Log (principal, endpoint, object_id, object_owner, decision); alert on authorized-but-cross-owner successes and on sequential-ID enumeration.
• Watch for normal-role tokens hitting admin/function endpoints (BFLA), PATCH/PUT bodies containing fields the schema doesn’t expose (mass-assignment probing), and GraphQL queries with abnormal depth, size, or alias counts.
• Instrument GraphQL with per-operation cost logging so a single “one HTTP request” abuse is still visible as thousands of operations.

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Part 14: Final Revision / Summary

• Modern apps are fat client + API back-end. The client is untrusted and visible; every security decision must be enforced server-side on the endpoint. Endpoints and object IDs are enumerable by design.
• REST = resources (URLs) + verbs (GET/POST/PUT/PATCH/DELETE) + status codes. Verbs carry safe/idempotent promises (state-changing GET = smell). Status codes are an oracle: uniform 404/403 to avoid leaking existence.
• GraphQL = one endpoint, a typed schema, client-chosen fields via queries/mutations. Its strengths (field selection, graph traversal, introspection, single endpoint) are each an attack surface (per-field authz, depth DoS, schema mapping, WAF blind spots).
• gRPC = binary Protobuf over HTTP/2 for internal services; drive it with grpcurl + the .proto; bugs are missing per-method authz and “internal = trusted.”
• The OWASP API Security Top 10 skews toward authorization, not injection. The big three: API1 BOLA/IDOR (change an ID, read others’ objects), API5 BFLA (call functions you shouldn’t), API3 (mass assignment writes + excessive-exposure reads). Root cause of all three: authenticated but not properly authorized for the object/function/field.
• GraphQL-specific: disable introspection, limit depth/complexity, limit batching (aliased logins bypass HTTP rate limits).
• Fixes are small and repeatable: scope queries to the owner, centralize function authz, allowlist writable fields, define explicit output views, and rate-limit per operation.

If you can look at any endpoint and immediately ask “who / which function / which object / which fields — and is each checked server-side?”, you have the API-security instinct this chapter exists to build.

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Part 15: Cheat Sheet / Quick Reference

REST verbs

GET (safe,idempotent) · POST (create) · PUT (replace,idempotent) · PATCH (partial) · DELETE (idempotent)

Status-code oracle: 401=who are you · 403=no · 404=absent/hidden · 429=rate limit · 500=leak.

OWASP API Top 10 (memorize the big 3)

ID	Bug	Test
API1	BOLA/IDOR	change object ID, replay as other user
API5	BFLA	call admin/other-verb endpoint as normal user
API3	mass assign / over-expose	send extra fields; read raw response
API4	resource consumption	huge limit/payload, GraphQL depth/batch
API7	SSRF	point URL params at internal/metadata

BOLA test: two accounts, use B’s object ID as A → 200 with data = bug.

GraphQL recon

query { __schema { types { name fields { name } } } }   # introspection
mutation { a: login(...) {token} b: login(...) {token} } # batch brute force

Mass-assignment probe

curl -X PATCH .../account -d '{"role":"admin","isAdmin":true,"balance":9e9}'

Tooling: Burp (Autorize=BOLA/BFLA, InQL=GraphQL, Intruder=ID fuzz) · ffuf (routes) · graphw00f/clairvoyance (schema) · grpcurl (gRPC).

Defense one-liner: authenticate once, then authorize the object, the function, and the fields — every time, server-side.

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Part 16: Common Pitfalls

• Trusting the client. Hidden buttons, disabled fields, client-side role checks are cosmetic. Attackers call the API directly.
• Looking up by ID without an ownership check. The #1 API bug (BOLA). Scope the query to the caller.
• Relying on UUID unpredictability as authorization. Unguessable ≠ access-controlled; leaked UUIDs still work.
• Spreading req.body onto a model. Mass assignment → privilege escalation. Allowlist fields.
• Returning the raw model. Excessive data exposure — tokens, hashes, PII, internal flags leak. Serialize explicitly.
• Status codes that leak existence. 403 vs 404 differences enumerate valid IDs. Be uniform.
• GraphQL introspection on in prod, no depth/complexity/batch limits. Free schema map + DoS + rate-limit bypass.
• Rate-limiting HTTP requests, not operations. GraphQL batching and aliasing defeat per-request limits.
• Function authz per-handler instead of centralized. You’ll forget one endpoint; default-deny middleware won’t.
• Forgotten /v1/ endpoints and shadow/staging APIs. API9 — the old, unpatched surface is often the way in.
• State-changing GETs. Violate the safe promise and are CSRF-able even under SameSite=Lax.

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Part 17: Practice Labs & Resources

• PortSwigger Web Security Academy — the API testing and GraphQL API vulnerabilities topics are the gold standard: BOLA/IDOR, mass assignment, server-side parameter pollution, GraphQL introspection, and depth/alias abuse labs, all browser-based and free.
• OWASP crAPI (“completely ridiculous API”) — a purpose-built vulnerable API training app covering the full API Top 10 (BOLA, mass assignment, JWT, SSRF). Run it in Docker and work each.
• VAmPI and DVGA (Damn Vulnerable GraphQL Application) — small deliberately-vulnerable REST and GraphQL targets for BOLA, auth, and GraphQL-specific attacks.
• OWASP Juice Shop — has REST and some GraphQL-flavored challenges alongside the classic web bugs; good for chaining exposure → takeover.
• TryHackMe / HackTheBox — API-focused rooms/boxes (e.g. IDOR and GraphQL rooms) and web boxes whose foothold is an API misconfig.
• Tooling to master: Burp Suite with Autorize (BOLA/BFLA automation) and InQL (GraphQL), Postman/curl for crafting requests, ffuf for route discovery, graphw00f/clairvoyance for GraphQL fingerprint/schema, and grpcurl for gRPC.

Practice questions / mini-labs to self-test:

1. Given GET /api/orders/1001 returning your order, describe the exact steps (two accounts, which requests) to prove BOLA, and write the one-line server fix.
2. A PATCH /api/profile accepts {name,email}. You suspect mass assignment. List the candidate fields you’d try and how you’d discover them, then write the allowlist fix.
3. A GraphQL endpoint has introspection enabled. Show the query that dumps the schema, then explain two distinct abuses (data and DoS) that follow.
4. Explain precisely why GraphQL query batching can bypass a login rate limit that a REST login form would enforce, and give the fix.
5. Design authorization for DELETE /api/comments/{id} so that it resists BOLA and BFLA. Name both checks and where they live.