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AI EngineeringPython

LLM AI Hunting

by H-mmer

LLM AI Hunting is an AI Engineering skill for Claude Code, published by H-mmer in pentest-agents.

804 stars156 forkson H-mmer/pentest-agentsAdded 2026/08/22Repository updated 2026/06/12
agentsbug-bountybugcrowdclaude-codehackeronemcppentestingsecurity-tools
Install in seconds
Install LLM AI Hunting
Copy LLM AI Hunting into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/H-mmer/pentest-agents/tree/main/skills/hunt-llm-ai ~/.claude/skills/hunt-llm-ai

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/H-mmer/pentest-agents.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
skills/hunt-llm-ai/SKILL.md in H-mmer/pentest-agents
Installs to
~/.claude/skills/hunt-llm-ai
Collection
One of 25 skills cataloged from this repository
Category
AI Engineering2451 skills

What LLM AI Hunting does

LLM AI Hunting targets prompt injection, agent tool abuse, output-handling bugs, and model-server RCE in AI features. Use it when testing chatbots, RAG systems, coding agents, MCP servers, or other LLM-integrated surfaces.

LLM AI Hunting is cataloged under AI Engineering on DirSkills. LLM AI Hunting comes from a repository tagged agents, bug-bounty, bugcrowd, claude-code and hackerone.

Documentation

README

Crown Jewel Targets

LLM and Agentic AI is the fastest-growing paying surface in 2024-2026. Every SaaS shipping an "AI feature" is a candidate; most ship with the LLM06:2025 Excessive Agency / LLM05:2025 Improper Output Handling / LLM01:2025 Prompt Injection problems unsolved by design. The 24-month meta has crystallized around six asset types. All CVEs below are NVD-verified.

This is the opening of the README. Read the full README on GitHub.

Commands LLM AI Hunting provides

Slash commands named in this skill’s SKILL.md, listed in the order they first appear.

  • /summarize
  • /invoke
  • /agent
  • /runs

Frequently asked about LLM AI Hunting

  • What else does H-mmer publish alongside LLM AI Hunting?

    LLM AI Hunting is one of 25 skills that DirSkills catalogs from H-mmer/pentest-agents, the repository it ships in. Its siblings there include Analyze, Autopilot and Brain. Each one is a separate skill with its own page in this directory, installs the same way LLM AI Hunting does, and is maintained by H-mmer in that same repository. The rest of the collection is listed on the H-mmer/pentest-agents page.

  • How does LLM AI Hunting compare to other AI Engineering skills?

    LLM AI Hunting ranks #1597 by stars among the 2451 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of LLM AI Hunting against them. Open each page to compare what they document and how they install.

More from H-mmer/pentest-agents

LLM AI Hunting is one of 25 skills cataloged on DirSkills from H-mmer/pentest-agents.

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Analyze

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Brain

Brain manages pentest engagement state with init, brief, status, exhausted, and record subcommands. Use it to capture targets, evidence, dead ends, and next actions during an assessment.
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Business Logic Hunting

Business Logic Hunting finds logic flaws in payment, auth, and subscription flows such as price manipulation, race conditions, MFA bypass, and trial abuse. Use it when testing for workflow skipping or client-side state trust issues.
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⛓️
1w ago

Chain

Chain builds deep exploit chains from a confirmed bug and dispatches a chain-builder agent to find escalation paths. Use it when you have a validated issue and want end-to-end impact or a reportable chain.
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Correlate

Correlate runs a finding correlation engine to turn individual findings into attack chains. Use it after collecting bugs to identify multi-step paths, document reproduction steps, and surface high-impact chains.
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