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Hunt Cache

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Encod3d-Sec
hunt-cache

Web cache poisoning + cache deception hunting - unkeyed input poisoning, cache-key analysis, path-confusion deception, header/parameter cloaking. Wiki-first, FIND schema output.

Overview

PublisherEncod3d-Sec
RepositoryTORCH
Skill namehunt-cache
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 Cache 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-cache
  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-cache .claude/skills/hunt-cache
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hunt Cache 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 Cache 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 Cache 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: Web Cache Attacks

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 "web cache poisoning deception unkeyed input cache-key path confusion" via wiki-search MCP

Hub: [[web-moc]] (live web index). Primary page: [[web-cache-poisoning]]. Payload arsenal: wiki/payloads/web-cache.md. Anchors: [[web-cache-deception]], [[web-cache-attacks]]. Related: [[http-host-header-attacks]], [[http-request-smuggling]].

Attack surface

Needs a cache in front (CDN / Varnish / Cloudflare / Akamai / Fastly, or an app-layer cache). Signals: Age, X-Cache: hit/miss, Cache-Control, CF-Cache-Status headers; static-ish responses; responses that reflect a header or param.

Rank before testing:

  • Unkeyed headers that reflect - X-Forwarded-Host, X-Forwarded-Scheme, X-Host, X-Forwarded-For, plus custom headers a page reflects into links/scripts. Highest hit-rate poisoning vector; discover unkeyed inputs with Param Miner.
  • Path / parameter cloaking - static-looking suffixes and delimiter tricks (/account/profile.css, /account/profile/nonexistent.js, path-parameter ;, encoded %2f, fat GET) that desync what the cache keys on from what the origin serves. Primary deception vector.
  • CDN edges and normalization gaps - cache-key normalization (case, trailing slash, duplicate params) differing from origin routing; multi-CDN or origin-vs-edge disagreement.

Methodology

Drive load-bearing requests through Burp Repeater for operator visibility; use Param Miner to enumerate unkeyed headers/params. curl is fine for the quick keyed-vs-unkeyed loop.

  1. Identify the cache + cache key. Compare X-Cache/Age across requests; determine what is keyed (usually method + host + path + some query) vs unkeyed (most headers, some params). Always attach a unique cache-buster while probing so you never touch a shared key.
  2. Cache poisoning (unkeyed input -> harmful response, then cached for others).
    • Find an unkeyed input that affects the response (reflected header/param): X-Forwarded-Host, X-Forwarded-Scheme, X-Host, X-Forwarded-For, custom headers (Param Miner to discover).
    • Make it produce harm (XSS / redirect / resource swap), then confirm the cached poisoned response is served to a fresh request (cache-buster off) - and cross-session (see confirmation gate).
    • Fat GET, parameter cloaking, and cache-key normalization gaps as variants.
  3. Cache deception (trick the cache into storing a victim's private page).
    • Request a private page with an appended static-looking suffix/path: /account/profile.css, /account/profile/nonexistent.js, path-parameter ;, encoded %2f.
    • If the origin returns the private content but the cache stores it as static -> retrieve another user's data unauthenticated.
  4. Confirm impact crosses a trust boundary - served to other users / discloses private data, not just your own session.
  5. Distill when confirmed (reusable unkeyed-header or deception-path trick, GENERIC, no client host): python3 scripts/wiki-stage.py --kind technique --slug <slug> --target-page techniques/web/web-cache-poisoning.md (deception-path findings: --target-page techniques/web/web-cache-deception.md).

Chaining

An unkeyed input reflected into HTML/JS turns poisoning into stored XSS served to every client hitting that key - escalate the payload with hunt-xss. A reflected X-Forwarded-Host in a redirect or absolute link gives open redirect / resource swap to all users. Both raise impact from self-only to mass; prove reach on a benign key, then stop (see stop condition).

Evasion

When the input looks keyed or filtered: try header-name variants (X-Forwarded-Host vs X-Host vs Forwarded), duplicate/pollute the param so the cache keys one occurrence and the origin reads another, exploit cache-key normalization (case, trailing slash, %2f), and use a fat GET (body params on a GET) to smuggle the value past a keyed query string.

Confirmation gate

Web cache poisoning is a blind / OOB-capable class: the win is a response served to OTHER clients, which you cannot observe from your own session alone.

NOT confirmation: your own cached response reflected back to you alone; a single response that might be per-user; an Age / X-Cache: hit change with no cross-session retrieval; the payload echoed in your own request.

IS confirmation: a poisoned or deceived response served to a DIFFERENT session and reproduced in a clean session (fresh profile, no cached state, cache-buster off); or an OOB callback to your unique Burp Collaborator / interactsh subdomain from a resource you injected into the cached page - a Collaborator-pointed unkeyed header confirms the poisoning reaches the cache and is loaded by other clients.

When you plant a blind/OOB payload, append a row to targets/<eng>/oob.md: | <token> | <sink url+param> | cache | <date> | waiting | | (columns: token | sink | class | planted | status | source; token = your unique Burp Collaborator / interactsh label). The recon-capture hook auto-correlates incoming callbacks to flip the row to HIT and SessionStart surfaces HITs; a HIT row in targets/<eng>/oob.md is the gate to scaffold the FIND. Do NOT claim a blind cache poisoning without cross-session proof or a HIT row.

Stop condition (traffic-affecting)

Per hunt-core, cache poisoning is a traffic-affecting primitive: poisoning the shared/production cache can serve malicious content to every real user who hits that key. Demonstrate on a benign, self-scoped cache key (a unique cache-buster or your own path) and STOP at proof. Do not mass-poison a shared key, and do not leave a live payload sitting in the production cache.

Severity

HIGH (stored XSS / redirect to all users, or PII disclosure via deception); CRITICAL if it yields mass account takeover; MEDIUM if self-only / weak impact.

Deadends

Append: - [ ] web-cache <host> -- key includes host+all reflective params; deception suffixes not cached (Cache-Control: private)

Frequently asked questions

What does the Hunt Cache AI skill do?

Web cache poisoning + cache deception hunting - unkeyed input poisoning, cache-key analysis, path-confusion deception, header/parameter cloaking. Wiki-first, FIND schema output.

Why use Hunt Cache on TypingMind?

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

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

Which AI models can use Hunt Cache?

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 Cache?

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

Is the Hunt Cache 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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