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Hunt Api

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Encod3d-Sec
hunt-api

API attack hunting (REST / GraphQL / gRPC) - BOLA/IDOR, BFLA, mass assignment, excessive data exposure, auth/JWT, introspection + batching, rate-limit abuse. OWASP API Top 10. Wiki-first, FIND schema output.

Overview

PublisherEncod3d-Sec
RepositoryTORCH
Skill namehunt-api
Stars
334
Forks
48
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Encod3d-Sec on GitHub. Read the source before you install it.

Installation

Install the Hunt Api 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.
    https://github.com/Encod3d-Sec/TORCH/tree/main/skills/hunt/hunt-api
  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/Encod3d-Sec/TORCH.git /tmp/TORCH
mkdir -p .claude/skills
cp -r /tmp/TORCH/skills/hunt/hunt-api .claude/skills/hunt-api
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hunt Api 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 Hunt Api 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 Hunt Api 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.

Hunt: API Security

Assumes hunt-core for the scope gate, two-account rule, confirmation gate, enumeration limits, stop conditions, wiki protocol, FIND output, and Deadends. Do not re-derive any of that here.

Wiki

qmd_query "API REST GraphQL gRPC BOLA BFLA mass assignment excessive data exposure OWASP API Top 10" via wiki-search MCP

Hub: [[web-moc]] (live web index). Primary page: [[api-security]]. Payload arsenal: wiki/payloads/api.md. Anchors: [[api-testing]], [[graphql-attacks]]. Variants: [[grpc-web-attacks]] (gRPC-Web / protobuf transcoder abuse), [[rsql-injection]] with the [[rsql]] payload (RSQL/FIQL filter-query injection, e.g. Spring Data REST), [[rate-limit-bypass]] (header/race/distributed-source throttle bypass), [[redos]] payload (catastrophic-backtracking regex DoS in an input validator), [[jwt-attacks]] (token flaws).

For object-level authorization (BOLA/IDOR) see hunt-idor.

Attack surface signals

/api/, /v1/, /graphql, /rest/, gRPC (application/grpc, HTTP/2), Swagger UI (/swagger, /api-docs, /openapi.json), mobile/SPA backends.

Rank before testing. Not all surface is equally likely to be broken:

  • Undocumented endpoints present in the spec (Swagger/introspection) but never called by the UI - the classic BFLA surface; nobody tested the route the client does not exercise.
  • GraphQL introspection and the mutations/fields it reveals that the client never invokes.
  • Batching and alias endpoints - one request, many operations; per-item authorization and rate limiting are frequently applied to the request, not each operation.
  • Bulk and export endpoints - one call, many objects; much higher severity per finding.
  • Non-GET verbs on read-looking routes - GET /orders/123 authorized, PATCH/DELETE not.
  • Newest features - authorization middleware lags new code; a route written outside the conventions misses it.

Methodology

Setup: two accounts per hunt-core (A owns, B attacks, separate profiles). Get the spec if any - Swagger/OpenAPI, GraphQL introspection, .proto.

Drive it through Burp for operator visibility. Push the load-bearing requests (the BOLA cross-account swap, the mass-assignment body, the BFLA verb/route call) into Repeater via Skill(hunt-burp) / the native Burp MCP (mcp__burp__*) so the operator can replay and inspect them; brute/fuzz belongs in Intruder (send_to_intruder), not a hand-rolled loop. A quick throwaway curl per account for the writeup PoC is fine.

  1. Enumerate. Parse Swagger/OpenAPI for every endpoint + param; GraphQL introspection (__schema); gRPC server reflection (grpcurl -plaintext <h> list, then list <svc> / describe). No spec? Discover endpoints with ffuf -w <api-wordlist> -u https://HOST/FUZZ and fingerprint hosts with httpx, not a hand curl loop. This is service / endpoint DISCOVERY, not object enumeration - it is bounded by the engagement RoE (no_dos, scan-rate caps), NOT by the 5-to-20 object cap. Do not clamp the wordlist to 20.
  2. BOLA / IDOR (API #1). Swap object IDs across accounts (numeric, UUID, in body/path/header) - the dominant API bug. Swapping IDs to pull other accounts' records IS object enumeration: bounded to 5 identifiers by default, 20 ceiling with operator approval, 0 under no_bruteforce, per hunt-core. Two or three adjacent IDs prove the sequential pattern; cite a total/pagination count for scale, never a sweep. Full bounded-sample loop and the trusted-identifier test live in hunt-idor. -> overlaps hunt-idor.
  3. Broken function-level auth (BFLA). Call admin/privileged methods as a low-priv user; swap the HTTP verb (GET -> PUT/DELETE); hit the undocumented endpoints from the spec.
  4. Mass assignment. Add fields the client never sends (role, isAdmin, verified, balance) to JSON bodies; look for the privilege/state change to actually take effect (confirm from a fresh read, step below).
  5. Excessive data exposure. The API returns more than the UI shows (full objects, other users' fields, internal flags) - inspect the raw response, not the rendered page.
  6. Auth. JWT flaws ([[jwt-attacks]]: alg:none, weak secret, kid injection), API-key reuse, missing auth on some routes, OAuth scope creep.
  7. GraphQL specifics. Introspection, batching/aliases (rate-limit/brute bypass), nested-query DoS, field suggestion leaks. Payloads: [[graphql-attacks]].
  8. gRPC specifics. Reflection to enumerate; grpcurl to call methods; tamper protobuf fields; run the same authz tests as REST.
  9. Rate limit / resource. Unbounded pagination, no throttle on sensitive actions (OTP/login). Prove the throttle is absent with a bounded burst; honor no_bruteforce/no_dos - do not actually brute credentials or OTPs and never run this at volume against a live auth endpoint. See [[rate-limit-bypass]].

When a direct call 403s or the extra field is stripped, it is not closed. Parameter pollution (?id=A&id=B - check and fetch may read different occurrences), array/nested wrapping of the field ({"user":{"role":"admin"}}), casing and separator variants of a mass-assign key (isAdmin/is_admin/admin), alternate content-type (form vs JSON vs XML), verb-override headers (X-HTTP-Method-Override), version downgrade (/v1/ predates the middleware /v2/ has - the most reliable), and batching to skip per-item authorization.

Chain: mass assignment -> privilege escalation. A role:admin/isAdmin:true that actually takes effect turns the whole BFLA admin surface reachable - re-run step 3 as the escalated principal. For the broader workflow/state-tampering angle hand off to hunt-bizlogic.

Distill a confirmed, GENERIC pattern (product + endpoint + impact, no client host): python3 scripts/wiki-stage.py --kind technique --slug <slug> --target-page techniques/web/api-security.md

Confirmation gate

NOT confirmation: a 200 echoing your own request back; an empty or shell response; B seeing an object that is shared, public, or org-visible; a body you have not compared against A's baseline; a BFLA endpoint returning 200 without the privileged action actually performed; a mass-assignment write returning 200 with the extra field accepted but the privileged field never verified in effect; any result not re-verified in a clean session.

IS confirmation:

  • BOLA - B's low-priv session returns A's actual data, matching A's own baseline response, with A's legitimate/shared access ruled out, reproduced in a clean session.
  • BFLA - a low-priv token reaching AND executing an admin/privileged function (perform the action, do not just get a 200).
  • Mass assignment - the privileged field (role/isAdmin/balance) verified in effect from a fresh authenticated read, not merely accepted in the request body.

Severity

  • CRITICAL - unauthenticated admin action, or cross-tenant data access.
  • HIGH - BOLA/BFLA reaching other users' data or functions; mass-assignment privilege escalation.
  • MEDIUM - excessive data exposure; missing or weak rate limiting.

Deadends

Append: - [ ] API on <host> <endpoint> -- BOLA/BFLA/mass-assign all enforced;
              extra fields ignored; JWT validated; introspection off

Record what you tried (pollution/array/verb/version-downgrade/batch), not just that it failed.

Frequently asked questions

What does the Hunt Api AI skill do?

API attack hunting (REST / GraphQL / gRPC) - BOLA/IDOR, BFLA, mass assignment, excessive data exposure, auth/JWT, introspection + batching, rate-limit abuse. OWASP API Top 10. Wiki-first, FIND schema output.

Why use Hunt Api on TypingMind?

Because you install it once and use it with any model. Hunt Api 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 Hunt Api in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Encod3d-Sec/TORCH/tree/main/skills/hunt/hunt-api. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hunt Api?

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 Hunt Api?

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

Is the Hunt Api AI skill free?

Yes. It is published on GitHub by Encod3d-Sec under the MIT 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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