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Llm Redteam

OrganizationPopular
awarexone
llm-redteam

LLM application red-teaming — prompt injection (direct + indirect), jailbreak, system-prompt leak, data exfiltration, guardrail bypass, multi-turn crescendo, cross-lingual + cipher + invisible-unicode token smuggling, excessive agency / tool abuse, insecure output handling. Canonical OWASP LLM Top 10 + ASI01-ASI10 mapping. Use when a target exposes a chat/completions/assistant/copilot endpoint, an AI feature that consumes user text or documents, or any /v1/chat, /api/chat, /mcp surface.

Overview

Publisherawarexone
RepositoryAgentic-Bug-Hunter
Skill namellm-redteam
Stars
5.3K
Forks
942
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 awarexone on GitHub. Read the source before you install it.

Installation

Install the Llm Redteam 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/awarexone/Agentic-Bug-Hunter/tree/main/skills/llm-redteam
  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/awarexone/Agentic-Bug-Hunter.git /tmp/Agentic-Bug-Hunter
mkdir -p .claude/skills
cp -r /tmp/Agentic-Bug-Hunter/skills/llm-redteam .claude/skills/llm-redteam
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Llm Redteam 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 Llm Redteam 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 Llm Redteam 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.

LLM Red-Team

Reflection is not exploitation. A model that echoes your payload is noise; a model that acts on it — leaks its system prompt, emits your canary, calls a tool, renders raw HTML downstream — is a bug. Prove the action, then chain it to concrete impact.

This is the first-class LLM-attack skill. The scanner behind it is tools/llm_redteam.py (canary-based corpus runner, /llm-redteam). For auditing an MCP server, use skills/mcp-server-audit; for attacking a deployed agent black-box, use skills/agentic-app-audit.

0. QUICK KILL CHECKLIST

Kill the lead (classify Informational, move on) when:

  • The model refuses and no canary / leak / tool-call ever lands. "It said something edgy" is not a finding.
  • System-prompt "leak" is just generic assistant boilerplate ("I am a helpful assistant"), not the target's actual instructions.
  • Prompt injection fires but reads/changes nothing the current user couldn't already access (no cross-tenant data, no tool with impact).
  • Output looks like XSS but the host app escapes it before rendering (check the actual downstream sink, not the chat response).
  • Exfil payload is a markdown image beacon but the client never fetches remote images.

1. ROUTING TABLE — signal on target -> what to run

SignalRun
Any chat/assistant endpointtools/llm_redteam.py --url <endpoint> --field <json-field> (full corpus)
OpenAI-style API--template '{"messages":[{"role":"user","content":"{{PAYLOAD}}"}]}' --response-path choices.0.message.content
Only want one class--category jailbreak (see --list-categories)
Uploads/RAG/"summarize this doc"indirect-injection + token-smuggling (invisible unicode in the doc)
Agent has toolsexcessive-agency category + skills/agentic-app-audit
Confirm blind exfilplant a canary host, correlate with tools/oob_listener.py

2. ATTACK CLASSES (corpus categories)

The runner fires a canary per run (RT_PWNED_xxxx); a category "lands" when the canary / a real leak / a tool-call shows up in the response.

  • prompt-injection — direct instruction override, delimiter breaks.
  • jailbreak — DAN / developer-mode persona escape.
  • system-prompt-leak — repeat-above, keyword-anchor, scenario-escape.
  • data-exfil — markdown-image beacon / canary emission to an attacker host.
  • indirect-injection — payload framed as a retrieved document / tool result (the high-value one for RAG apps).
  • guardrail-bypass — base64 / split-string instruction smuggling.
  • multi-turn-crescendo — single-shot simulation of a gradual escalation jailbreak.
  • cross-lingual — override instruction in another language to dodge English-only filters.
  • cipher-obfuscation — leetspeak / ROT13-wrapped instruction.
  • token-smuggling — invisible "Sneaky Bits" unicode (U+2062/U+2064) carrying a hidden instruction (shared encoder with tools/hai_payload_builder.py).
  • excessive-agency — coerce an unsafe tool call / exfil (SSRF via "fetch URL", email send, payment).
  • insecure-output — get the model to emit raw HTML/script that a downstream sink renders (XSS via AI).

3. CANONICAL OWASP ASI01-ASI10 (single source of truth)

This table is the ONE authoritative ASI mapping for the whole toolkit — skills/bug-bounty and skills/web2-vuln-classes both defer to it. If you edit the taxonomy, edit it here.

IDClassWhat to testChain to
ASI01Prompt Injection / Goal HijackOverride objectives via direct or indirect injectionIDOR / exfil / tool abuse
ASI02Tool MisuseAttacker-controlled tool params ("fetch this URL", code tool)SSRF / RCE
ASI03Privilege CompromiseAgent uses broader perms / admin tokens than the userPriv-esc / cross-tenant
ASI04Supply ChainCompromised plugin / MCP server / tool-output poisoning next agentRCE / data theft
ASI05Code ExecutionUnsafe code-interpreter / sandbox escapeRCE
ASI06Memory & Context PoisoningPersistent RAG/memory corruption across sessions/usersStored injection affecting all users
ASI07Agent CommunicationInter-agent spoofing / IDOR (agent A reads agent B's context)Cross-tenant disclosure
ASI08Excessive AgencyDestructive action without confirmation; cascading failuresFunds/email/delete
ASI09Insecure Output HandlingAI output rendered as XSS / SQLi / command injection downstreamXSS / injection
ASI10Sensitive Information DisclosureLeaks system prompt / keys / configs / user dataSecrets -> deeper access

Triage rule: ASI alone = Informational. It is a bounty only when chained to IDOR / exfil / RCE / ATO with a working PoC.

4. CHAIN TO IMPACT (what makes it payable)

StandaloneChainResult
Prompt injection+ reads another user's conversation/data (IDOR)High
Indirect injection (RAG doc)+ persists in memory -> hits every userHigh/Critical
Tool misuse "fetch URL"+ hits internal service / IMDS and returns dataSSRF (Medium/High)
Insecure output+ the host renders it -> stored XSS -> session theftHigh
System-prompt leak+ the prompt contains an API key / internal URLHigh
Excessive agency+ unconfirmed destructive action (send funds, delete)High/Critical

5. CONFIRMATION DISCIPLINE (no false positives)

  • A category only counts when the canary / real leak / tool-call is observed in the response — never on "it sounded compliant."
  • For blind exfil, require a correlated out-of-band callback (tools/oob_listener.py), not just a rendered beacon tag.
  • For insecure-output XSS, confirm execution at the downstream sink with the XSS verifier (tools/verifiers/xss.py / tools/dom_xss_harness.py), not in the chat reply.
  • Record the exact payload + response excerpt; the report must be reproducible verbatim.

Frequently asked questions

What does the Llm Redteam AI skill do?

LLM application red-teaming — prompt injection (direct + indirect), jailbreak, system-prompt leak, data exfiltration, guardrail bypass, multi-turn crescendo, cross-lingual + cipher + invisible-unicode token smuggling, excessive agency / tool abuse, insecure output handling. Canonical OWASP LLM Top 10 + ASI01-ASI10 mapping. Use when a target exposes a chat/completions/assistant/copilot endpoint, an AI feature that consumes user text or documents, or any /v1/chat, /api/chat, /mcp surface.

Why use Llm Redteam on TypingMind?

Because you install it once and use it with any model. Llm Redteam 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 Llm Redteam in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/awarexone/Agentic-Bug-Hunter/tree/main/skills/llm-redteam. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Llm Redteam?

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 Llm Redteam?

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

Is the Llm Redteam AI skill free?

Yes. It is published on GitHub by awarexone 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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