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Hunt Core

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Encod3d-Sec
hunt-core

Shared discipline for every hunt-* skill: scope and authorization gating, the two-account rule, the confirmation gate that separates a real finding from a false positive, enumeration limits, stop conditions, marker discipline, wiki-first query and self-heal, FIND output, Deadends, and wiki distillation. ALWAYS LOADED alongside any hunt skill. Also trigger directly on "is this in scope", "is this a real bug", "how do I confirm this", "should I keep going", "how many IDs should I test", "what severity", "how do I report this", "I got someone else's data". Every hunt-* skill assumes this file; without it they run without their safety and quality layer.

Overview

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

Use it in TypingMind

Enable Hunt Core 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 Core 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 Core 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: Core

The discipline every hunt-* skill assumes. Class-specific technique lives in the hunt skills and the wiki; this file holds what is true regardless of which bug class you are chasing.

Owned here, never repeated in a hunt skill: the scope gate, two-account setup, confirmation gate, enumeration limits, stop conditions, marker discipline, wiki query/self-heal protocol, FIND output, Deadends, and distillation. A hunt skill that restates any of these will drift from this one; reference it instead.

Scope gate

Hunting means reading other people's data, escalating privileges, and creating accounts. Outside an authorized engagement all of it is a crime. Before any request leaves the machine:

  1. Authorization - a program with the target in scope, a signed agreement, your own lab, or a CTF. "Probably fine" is not authorization.
  2. Scope - exact hosts. *.target.com does not cover targetapp.io, an acquisition, or target.com.cdn.net. Read targets/<eng>/scope.md.
  3. Exclusions and forbidden techniques - check no_bruteforce, scanning caps, DoS, social engineering, and any per-program rule before the technique that would violate it.
  4. Two accounts, both yours - see below. Required for any authorization-class bug.
  5. Deadends - read targets/<eng>/Deadends.md and skip what is already exhausted.

Unconfirmed on any of these: ask. Do not infer scope from the fact that a host resolved. Discovery is not authorization. The scope-guard hook enforces the host check on Bash commands; it is a backstop, not the gate.

Two-account rule

Authorization findings are "account A reached account B's object." Without B you have two bad options: test against a real user, or report a guess. Both are worse than not reporting.

  • User A - resource owner. User B - attacker. Separate browser profiles so cookies never cross.
  • Record both internal IDs in targets/<eng>/identities.md as you discover them.
  • Cross-tenant work needs two tenants, not two users in one. Many apps isolate tenants correctly and users not at all.

Confirmation gate

Nothing becomes a FIND on the strength of a response body. The gate is per class; the hunt skill owns the specific "NOT confirmation / IS confirmation" list. Universal rules:

  1. Re-verify in a clean session. Fresh token, new profile, no cached state. If the effect vanishes, you changed your own screen.
  2. Exercise the capability. Not "the server accepted it" but perform the action only the new state permits.
  3. Rule out legitimate access. Before claiming cross-account access, confirm A is not supposed to see it: shared teams, sharing links, public objects, org-wide visibility. The most common false positive in authorization testing.
  4. Blind classes need an OOB HIT. No inference-only findings. The oob.md row must flip to HIT before a FIND is scaffolded.
  5. Reproduce from scratch against your own written steps.

Failed 1-3 is a Deadend, not a "probably real but hard to prove."

[[wiki/techniques/methodology/safe-probing-and-controls]] carries the other half of this gate: how to probe destructive or sensitive surface without touching a real record (use an identifier proven not to exist), and how to fire a control so a NEGATIVE result means something instead of being assumed.

When the hunt spans many hosts, sessions, or parallel agents, the coordination layer adds failure modes this gate does not cover: claims nobody re-checked, requests nobody counted, the same finding filed twice, and severity drifting upward as work is summarised. See [[wiki/techniques/methodology/multi-agent-campaign-orchestration]].

Enumeration limits

Prove the boundary is missing, not how much is behind it.

The server does not check any object. Demonstrating that takes two or three identifiers. A thousand-ID sweep does not raise severity; it collects real users' data and converts a critical finding into an incident report naming you.

  • Default: 5 identifiers. Enough to show a sequential pattern and a missing check.
  • Ceiling without explicit operator approval: 20.
  • Beyond that, and for any range sweep: stop and ask. State why the extra volume changes the finding. Usually it does not.
  • no_bruteforce in scope means range 0. No sweep, no ffuf over an ID list, no exception.
  • Never enumerate writes. Read at volume is a rate problem; write at volume is destruction.

For scale evidence, cite a total field, a pagination header, or a result count. "The response reports 41,180 records; none beyond my two test accounts were retrieved" proves scale without retrieving anything.

Scope of these limits. They govern OBJECT and RECORD enumeration (guessing identifiers to pull other users' data). They do NOT cap legitimate service discovery: an SSRF internal port sweep or a network map is bounded by the engagement RoE (no_dos, scan-rate caps), not by the 5-to-20 object ceiling. Thread it and honor the cap; do not clamp a port sweep to 20.

Stop conditions

Stop and report rather than continuing when:

  • You received real user data. An ID you guessed belonged to someone real, a cache served another session, a beacon fired in a live employee context. Do not re-request to confirm, do not save it, do not use anything in it. Report immediately, state what you received and that you destroyed it, and note it in the FIND rather than hiding it.
  • The next step is destructive or persistent - deleting objects, modifying another tenant's config, planting content that outlives the session.
  • You have a traffic-affecting primitive - desync, cache poisoning, connection-pool effects. The mechanism is the finding.
  • The last step needs volume beyond the limits above.
  • You already have the severity ceiling. More escalation, more risk, no more payout.
  • You left scope. Even one hop. Especially one hop.

Stopping is not failure. "Confirmed and did not exploit" is worth more than a forfeited payout.

Wrong-vector tells (switch the vector, do not tune the tooling). A vector is exhausted the moment it starts fighting you; grinding harder is the sunk-cost trap. Two mechanical signals mean the current vector is the wrong door, not that your tooling needs another pass:

  • The target starves under your own exploit loop (repeated 000 / connection-timeout / empty-reply while you hammer one endpoint). A vector that DoSes a lab box is almost never the intended one. Stop, let it drain, and enumerate a DIFFERENT class (source-read: LFI / alias-traversal / .git / backup; a second service or vhost's own app; OOB creds) before returning.
  • Two verified hashes in a row fail the primary wordlist. The passwords are not wordlist material - they are delivered out-of-band (an email/note/KeePass, a config, a second service). Stop cracking and re-enumerate for where the creds are HANDED OUT; do not extract a third hash. (Engineering around a hostile channel - per-char verify-fix, min-of-2 sampling, gentler pacing - is the tell you are on the wrong vector, not a reason to keep going.)

When a wrong-vector tell fires and the next door is not obvious, call Skill(redteamlead) before grinding further - it reads the engagement state + evidence + wiki and returns ranked directions with an explicit STOP. That is exactly what it is for ("I'm stuck / which vector / should I keep hammering this"); one RTL call at the first sign a vector is fighting back beats hours of sunk-cost grind.

Marker discipline

Any class where you inject a value and look for it later (xss, ssti, sqli error strings, crlf, log injection, open redirect): use a unique 8+ char alphanumeric canary (e.g. x4hd2k9pq), never test/marker/evil/payload. Check the baseline response for the canary BEFORE claiming reflection; a value already present is not proof you put it there.

Wiki lookup (reference-map first, qmd on a hint)

qmd_query is powerful but ~15-30s per call, so it is a TARGETED deepen, not a pre-attack ritual. Three tiers, in order:

  1. Reference map FIRST (instant Read). Your hunt skill's ## Wiki section names the class's domain MOC + primary page + a few anchors. Read those directly, and one-hop from the MOC for a sibling technique. This answers the anticipated case with zero qmd latency.
  2. qmd_query ONLY on a concrete hint the map does not cover - a specific sink/function (an SSRF-reaching requests.get(user_input)), an observed escape (a <script> context that could be XSS), a fingerprinted version/CVE, or a service the MOC does not list: qmd_query "<the specific thing>" via wiki-search MCP. It auto-surfaces pages added since the skill was written. Do NOT blanket-qmd every action (too slow); do NOT hand-roll from memory when a targeted qmd would answer (that is the opposite failure). Fire it when you have the hint, then act.
  3. Self-heal only if neither the map nor a targeted qmd has it: stub wiki/techniques/<area>/<slug>.md (frontmatter + ## Observed during <engagement>) so the gap fills.

Payload arsenals live in wiki/payloads/. If the MCP is down, bash scripts/wiki-query.sh "<terms>" wraps the same qmd index (-k for an exact CVE/string).

Hunt approaches (which skill for the signal)

hunt-core is the hub: route from the observed signal to the class skill, load it, then use that skill's ## Wiki map. The recon-capture fingerprint router auto-suggests many of these from tool output; this table is the manual reference when it does not fire or you are reasoning about approach.

Signal / surfaceSkill
reflected/stored input, <script> / onerror / javascript: / a DOM sinkSkill(hunt-xss)
a param/body reaching a DB; an error / boolean / time oracleSkill(hunt-sqli)
a URL/host param, a fetch/preview/import sink, ?url=Skill(hunt-ssrf)
an object id, /users/{id}, two-account cross-accessSkill(hunt-idor)
template {{ }} render, XXE (SVG/DOCX/SAML), GraphQLSkill(hunt-injection)
a command sink, template-injection-to-exec, a version+CVESkill(hunt-rce)
a serialized blob (rO0 / AAEAAAD / O:), viewstate, a signed cookieSkill(hunt-deserialization)
login / reset / session / JWT, a legacy-protocol endpointSkill(hunt-auth)
OAuth / SAML redirect_uri or assertionSkill(hunt-federation)
file upload / avatar / document importSkill(hunt-upload)
REST/GraphQL/gRPC, BOLA / BFLA / mass-assignmentSkill(hunt-api)
checkout / price / coupon / workflow logic, a raceSkill(hunt-bizlogic)
CL.TE / TE.CL / HTTP-2 downgrade desyncSkill(hunt-smuggling)
an unkeyed header/param reaching a cacheSkill(hunt-cache)
exposed .git / .env / keys, secrets in a JS bundleSkill(hunt-secrets)
LLM prompt-injection / excessive agencySkill(hunt-llm)
MCP tool-poisoning / indirect injectionSkill(hunt-mcp)
AD: kerberoast / AS-REP / ADCS / DCSync / delegation (dotted-FQDN domain / a DC)Skill(hunt-ad)
Windows LOCAL privesc: service misconfig / autologon reg / SeImpersonate-Potato / scheduled-task / DLL hijack (standalone/workgroup box, or a local shell on a member)Skill(hunt-windows)
AWS/Azure/GCP metadata or IAMSkill(hunt-cloud)
Microsoft 365 / Entra tenantSkill(hunt-m365)
CI/CD pipeline (Actions / runners / OIDC)Skill(hunt-cicd)
macOS TCC / SIP / keychain / XPCSkill(hunt-macos)
SSL-VPN appliance (Fortinet/Citrix/Ivanti/Cisco/PAN)Skill(hunt-vpn)
Modbus / S7 / EtherNet-IP / PLC / HMISkill(hunt-ics)

Process skills (not vuln classes): Skill(ctf-box) boot-to-root, Skill(wiki-recon) external recon, Skill(arsenal) / Skill(wiki-arsenal) tool+payload lookup, Skill(triage) -> Skill(evidence) finding validation, Skill(coverage) untested-class gaps, Skill(next-move) prioritize, Skill(hunt-burp) drive Burp.

FIND output

On confirmation:

Create Vulns/Research/FIND-XXX-SEVERITY-<class>-<host>[-<resource>].md
Add row to Vuln-index.md

Severity is rated on demonstrated impact, not theoretical maximum. Preconditions lower it (victim interaction, an unguessable identifier you cannot show leaking, a race you win one time in twenty). Scale raises it, when you can show the identifier is enumerable without enumerating. State impact in the program's terms (customer data, account security, financial exposure), not in vulnerability classes.

On exhaustion:

Append to Deadends.md: - [ ] <class> on <host> <param/endpoint> -- <why it failed>

The reason matters more than the entry. 403 on cross-account, authorization enforced stops you retesting; a bare entry does not.

Distillation

When a confirmed finding is a reusable technique rather than a target quirk, stage it:

python3 scripts/wiki-stage.py --kind technique --slug <slug> --target-page <area>/<page>.md

GENERIC only - no client host, no real identifier, no customer data. Promote later via scripts/wiki-promote.py. Run scripts/check-leaks.sh before any push. Engagement data lives under targets/ and is git-ignored.

Frequently asked questions

What does the Hunt Core AI skill do?

Shared discipline for every hunt-* skill: scope and authorization gating, the two-account rule, the confirmation gate that separates a real finding from a false positive, enumeration limits, stop conditions, marker discipline, wiki-first query and self-heal, FIND output, Deadends, and wiki distillation. ALWAYS LOADED alongside any hunt skill. Also trigger directly on "is this in scope", "is this a real bug", "how do I confirm this", "should I keep going", "how many IDs should I test", "what severity", "how do I report this", "I got someone else's data". Every hunt-* skill assumes this file;...

Why use Hunt Core on TypingMind?

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

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

Which AI models can use Hunt Core?

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 Core?

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

Is the Hunt Core 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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