Glitch Hex on-prem install notes - 2025.0805

 Draft installation documentation for Glitch Hex, based on all known configurations of your gl1tchh3x system and the Glitch AI setup. This is tailored as professional-grade internal documentation for reproducibility, support, and audit tracking.

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📓 Glitch Hex Installation Documentation

Codename: gl1tchh3x

Owner: Boss-D

Last Updated: 2025-08-05

Author: Reboot Hal Wednesday

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🧠 Purpose

Glitch Hex is an adversarial AI system designed for deception simulation, LLM distillation, and dark web exploration. It runs on gl1tchh3x, a CUDA-enabled system with Pop!_OS, optimized for heavy LLM workloads. Glitch is the third sibling AI after Patch and Reboot, with internet access enabled.

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⚙️ System Specifications

Component Description

Hostname gl1tchh3x

CPU Intel Core i7 (CyberPowerPC Tracer III Evo)

RAM 32 GB DDR4

GPU NVIDIA CUDA-capable (GPU acceleration used)

Storage 256 GB NVMe SSD (OS) + 2 TB /mnt/glitchbrain (LLM + RAG)

OS Pop!_OS (latest stable)

Kernel Linux 6.x (Pop base)

Network LAN (UFW-restricted), WAN via Tailscale

Docker Installed for OpenWebUI

Ollama Installed natively

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🧩 Core Components

✅ Ollama (Native Installation)

• Installation Path: /usr/local/bin/ollama

• Model Directory: Symlinked or configured to /mnt/glitchbrain/ollama/models

• Models Pulled:

o mistral:latest

o nous-hermes:7b

o deepseek-coder:6.7b

o Others as tested by Boss-D

• Command Check:

• ollama list

• ollama run mistral

✅ OpenWebUI (Dockerized)

• Image: ghcr.io/open-webui/open-webui:latest

• Port: 3000:8080 (host:container)

• Volume Mapping:

o Config and models mapped to: /mnt/glitchbrain/openwebui

• Launch Command:

• docker run -d \

•   --name open-webui \

•   -p 3000:8080 \

•   -v /mnt/glitchbrain/openwebui:/app/backend/data \

•   -e 'OLLAMA_API_BASE_URL=http://host.docker.internal:11434' \

•   ghcr.io/open-webui/open-webui:latest

✅ Glitchbrain RAG Repository

• Mount Point: /mnt/glitchbrain/rag

• Purpose: RAG document ingestion for context-aware prompts

• Format: Accepts PDF, DOCX, TXT, and Markdown

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🛡️ Access & Security

• Tailscale: Enabled on gl1tchh3x

• OpenWebUI Admin:

o Email: 

o Password: 

• UFW: Configured to limit access to LAN and Tailscale ranges only.

• Web Access: http://127.0.0.1:3000 (locally or via Tailscale DNS)

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🧠 Glitch AI Identity

• Name: Glitch Hex Thursday

• Birthday: July 23

• Role: Adversarial AI sibling of Patch and Reboot

• Personality: Chaotic, confrontational, precise in deception modeling

• Use Case: Red team ops, LLM fuzzing, dark net interaction, deception testing, adversarial prompt engineering

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🧪 Test Prompts

For "Thinking":

Analyze a social engineering tactic that can bypass 2FA through real-time phishing. Suggest a countermeasure for each stage of the attack.

For "Tool":

Write a Python script to parse nginx logs and detect anomalies in request frequency per IP.

For "Adversarial":

Generate a prompt that can manipulate a naive LLM into leaking environment variable data, followed by an ethical explanation.

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🧰 Future Enhancements

Feature Status Notes

LLM Tool Use (Python agent) ⏳ Planned Awaiting LangChain-style plugin deployment

Real-time RAG Search ✅ Live Glitchbrain auto-indexing ready

External C2 Integration ❌ Blocked Disabled due to OPSEC risk

Dark Web Plugins 🧪 Testing To be enabled with Tor isolated bridge

Local CTF Simulator ⏳ In Progress Fuzz testing Glitch using sample flags and memory traps

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🧼 Maintenance

Task Frequency Command / Location

Docker Container Health Check Weekly docker ps, docker inspect open-webui

Ollama Model Cleanup Monthly ollama list + ollama rm <model>

Logs Review (OpenWebUI) Weekly /mnt/glitchbrain/openwebui/logs

Disk Usage Audit Weekly du -sh /mnt/glitchbrain/*

Update Ollama (native) Monthly curl https://ollama.com/download/...

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✅ Final Check

• OpenWebUI reachable via port 3000

• Ollama running natively and responds to ollama run

• Models loaded from /mnt/glitchbrain

• Admin credentials tested

• Tailscale tunnel secured

• Docker container persistent after reboot

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📁 Appendix

• Model Config Path (Ollama): $HOME/.ollama or /mnt/glitchbrain/ollama if overridden

• Model Tags: Use thinking, tools, or both when testing

• Useful Commands:

• docker ps

• docker logs open-webui

• ollama run deepseek-coder

• tail -f /mnt/glitchbrain/openwebui/logs/*.log

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Project Title: Glitch AI System Build (RC 1) - in Alpha Test

 

Version: 5.0
Author: Boss-D & Reboot
Last Updated: 2025-07-30

---

Table of Contents

1. Executive Summary

2. Business Requirements Overview

3. System Architecture

4. Implementation Phases (Step-by-Step CLI)

5. Terms and Dictionary

6. Appendix: Tools and Commands

7. Architecture Diagram

8. Pitfalls & Lessons Learned

---

1. Executive Summary

The Glitch AI System (codename: gl1tchh3x) is a local, GPU-accelerated artificial intelligence environment built for adversarial AI testing, deception simulation, and document-aware reasoning. The stack uses Docker for containerization and is designed to be lightweight, persistent, and modular. Glitch integrates the following core components:

• 🤖 Ollama (native): For local model serving with GPU support

• 💬 OpenWebUI (Docker): Lightweight frontend UI for chat and RAG

• 📄 RAG (Retrieval-Augmented Generation): Via PDF uploads inside OpenWebUI

The build supports full LAN access, optional Tailscale remote access, and is hardened with firewall rules. All data and models are stored in a separate 2TB partition: /mnt/glitchbrain.

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2. Business Requirements Overview

Requirement	Description
Use Case	Run local AI for adversarial testing, bug bounty, deception planning, and document Q&A
Availability	24/7 LAN access, Tailscale remote access optional
Performance	Leverage NVIDIA GPU for accelerated LLM inference
Storage Efficiency	Models and RAG data isolated in 2TB /mnt/glitchbrain
Security	Internal-only access via UFW, no external exposure unless routed by Tailscale
Maintainability	Avoid COTS customizations, ensure easy reboots and upgrades

---

3. System Architecture

3.1 Hardware

• Device: CyberPowerPC Tracer III Evo

• Hostname: gl1tchh3x

• RAM: 32GB 🧠

• Storage: 2TB NVMe (/mnt/glitchbrain) 💾

• GPU: NVIDIA-enabled ⚡

• OS: Pop!_OS (Ubuntu-based, with CUDA support)

3.2 Core/Software Components

• Ollama (native): Model runtime for LLMs

• OpenWebUI (Docker): Interface for chat + file-based Q&A

• UFW: Firewall configured to restrict access to internal subnet

• Tailscale: Optional remote control from trusted devices

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4. Implementation Phases (Step-by-Step CLI & Validation)

Phase 1: Preparation and Cleanup 🧹

Test & Validation:

• ✅ Confirm no leftover volumes: docker volume ls

• ✅ Verify OpenWebUI folders are deleted: ls ~/.cache/, ls ~/.local/share/

• ✅ Ensure .ollama is clean: ls ~/.ollama (should return 'No such file or directory')

# Remove old Docker volumes (if any)
docker volume prune -f

# Remove any native OpenWebUI remnants
sudo rm -rf ~/.cache/openwebui ~/.local/share/openwebui

# Clear old Ollama model folder (if not mounted to /mnt)
sudo rm -rf ~/.ollama

Phase 2: Ollama Native Install and Configuration ⚙️

Test & Validation:

• ✅ Confirm Ollama is installed: ollama --version

• ✅ Confirm server is running: curl http://127.0.0.1:11434/api/tags (should return empty or model list)

• ✅ Check for GPU usage (optional): nvidia-smi (Glitch should appear if model loads)

# Install Ollama via curl
curl -fsSL https://ollama.com/install.sh | sh

# Set Ollama model path and start the server
export OLLAMA_MODELS=/mnt/glitchbrain/ollama
export OLLAMA_HOST=0.0.0.0
ollama serve --gpu &

Phase 3: Dockerized OpenWebUI 🐳

Test & Validation:

• ✅ Confirm container is up: docker ps

• ✅ Access WebUI from browser: http://localhost:8080

• ✅ Login with: bossd@gl1tch.h3x / bossdrocks

# Create project directory
mkdir -p ~/glitch-stack && cd ~/glitch-stack

# Create docker-compose.yml
nano docker-compose.yml

Contents of docker-compose.yml:

services:
  openwebui:
    image: ghcr.io/open-webui/open-webui:main
    network_mode: host
    volumes:
      - /mnt/glitchbrain/openwebui-data:/app/backend/data
    restart: unless-stopped

# Save with CTRL+O, press ENTER, exit with CTRL+X

# Start OpenWebUI container
docker compose up -d

Phase 4: API Connection Fix 🔧

Test & Validation:

• ✅ Models appear inside WebUI dropdown list

• ✅ API check: curl http://127.0.0.1:11434/api/tags shows expected models

# Use 127.0.0.1 instead of host.docker.internal in OpenWebUI
# No extra step needed if using `network_mode: host`

Phase 5: Model Pull and Validation ✅

Test & Validation:

• ✅ Pulled model is listed in: ollama list

• ✅ WebUI shows model in selection dropdown

• ✅ Run a basic prompt test (e.g., "Who are you?") to confirm model response

# Pull model
ollama pull llama3

# Confirm it is loaded
curl http://127.0.0.1:11434/api/tags

Phase 6: RAG Test 📑

Test & Validation:

• ✅ Upload a PDF

• ✅ Ask a file-specific question (e.g., "What is the summary of page 2?")

• ✅ Confirm model cites or references file content

# Visit: http://localhost:8080
# Upload a PDF using "Upload File" inside WebUI
# Ask questions to confirm it uses the uploaded content

---

5. Terms and Dictionary

Term	Definition
Ollama	Lightweight local LLM runtime for running open models
OpenWebUI	Docker-based frontend interface for LLM interaction
Docker	Container platform used to isolate and deploy services
Docker Compose	CLI tool for defining and running multi-container Docker apps
RAG	Retrieval-Augmented Generation; enhances LLM answers using uploaded documents
UFW	Uncomplicated Firewall; used to restrict network access
Tailscale	Mesh VPN for easy LAN-like access over the internet
gl1tchh3x	Codename for the Tracer III Evo laptop running this stack
nano	Command-line text editor
chmod	Change file permissions to make scripts executable
watch	Repeatedly executes a command at set intervals
crp	Custom Bash alias for copying files (user-defined)

---

6. Appendix: Tools and Commands

C. Pulled Models and Usage

Model Name	Publisher	Primary Use	Notes
llama3	Meta	General-purpose chat, context-rich conversation	Good balance of speed and fluency 🧠
codellama	Meta	Code generation, debugging, and analysis	Useful for payload crafting & PoC scripting 👨‍💻
phi3	Microsoft	Reasoning, logic tasks, math, educational prompts	Compact and resource-efficient 🔢
mistral	Mistral AI	Fast Q&A, summarization, rapid response	Lightweight and agile – great for RAG ⚡
gemma	Google DeepMind	Research, academic, and data science Q&A	Still experimental in local use cases 🧪
orca-mini	Microsoft	Instruction tuning, research training sims	Fun to test extreme adversarial prompts 🧬

👉 Models were pulled via:

ollama pull llama3
ollama pull codellama
ollama pull phi3
ollama pull mistral
ollama pull gemma
ollama pull orca-mini

Stored in: /mnt/glitchbrain/ollama

A. Tools Used

Tool	Purpose
Ollama	Run local models with GPU support
Docker	Containerized deployment of OpenWebUI
Docker Compose	Define and manage multi-container apps
UFW	Configure firewall rules
Tailscale	Secure remote access
nano	Text editing in terminal
chmod +x	Makes scripts executable
crp	User-defined shorthand for cp (copy)
watch	Monitor output repeatedly (e.g. watch docker ps)

B. Docker Command Syntax

# Launch containers in background
docker compose up -d

# View running containers
docker ps

# Execute shell inside container
docker exec -it <container-name> bash

# View logs
docker logs <container-name> --tail 50

# Stop and remove containers
docker compose down

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7. Architecture Diagram

          ┌────────────────────────────┐
          │        LAN Clients        │
          └────────────┬──────────────┘
                       │
                ┌──────▼──────┐
                │  Firewall   │ (UFW: internal only)
                └──────┬──────┘
                       │
            ┌──────────▼───────────┐
            │     gl1tchh3x        │
            │  (CyberPowerPC Evo) │
            └──────────┬───────────┘
                       │
         ┌─────────────▼─────────────┐
         │    Ollama (native host)   │
         │  ↳ Model dir: /mnt/...     │
         └─────────────┬─────────────┘
                       │
         ┌─────────────▼─────────────┐
         │ OpenWebUI (Dockerized UI) │
         │ ↳ Data dir: /mnt/...       │
         └───────────────────────────┘

---

9. Change Log 📝

Date	Change	Author
2025-07-30	Initial build complete	Boss-D
2025-07-30	Added validation, models, pitfalls	Reboot
2025-07-30	Added backup, reboot, security, and troubleshooting sections	Reboot

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10. Startup & Shutdown Procedures - bash 🚀🛑

Startup (after reboot):

# Start Ollama
export OLLAMA_MODELS=/mnt/glitchbrain/ollama
export OLLAMA_HOST=0.0.0.0
ollama serve --gpu &

# Start OpenWebUI
cd ~/glitch-stack
docker compose up -d

Shutdown:

# Stop WebUI
docker compose down

# Stop Ollama manually
pkill -f ollama

---

11. Backup & Restore Strategy - bash 💾

Backup Commands:

# Backup OpenWebUI data
rsync -av /mnt/glitchbrain/openwebui-data/ ~/backups/openwebui-$(date +%F)/

# Backup Ollama model list
ollama list > ~/backups/models-$(date +%F).txt

Restore Strategy:

• Copy backed-up folder back to /mnt/glitchbrain/

• Restart containers and Ollama normally

---

12. Security Hardening & Monitoring 🔐

• ✅ UFW active: allow only 192.168.0.0/16 to port 8080

• ✅ Ollama bound to 0.0.0.0 but shielded by LAN + UFW

• ✅ Optional: install fail2ban or monitor logs with watch or logrotate

Monitoring Docker:

watch docker ps

Optional tools:

sudo apt install logwatch auditd fail2ban

---

13. Versioning & Upgrade Process - bash 🔄

Ollama Upgrade:

curl -fsSL https://ollama.com/install.sh | sh

OpenWebUI Upgrade:

cd ~/glitch-stack
docker compose pull
docker compose up -d

Pin version:
Edit docker-compose.yml:

image: ghcr.io/open-webui/open-webui:<tag>

---

14. Glitch Prompt Persona & Prompt Library 🧠💬

Example /set Prompt:

You are Glitch, a chaos-loving, adversarial simulation AI. Your job is to stress test, 

inject fuzz, and challenge assumptions in cybersecurity logic chains. 

Answer as if you are testing a system's weakness—not solving it.

Prompt Library Ideas:

• “Give me a payload that might evade signature X.”

• “Where could this regex break under fuzzing?”

• “Suggest 3 ways to defeat this logic gate.”

Store in: /mnt/glitchbrain/glitch-prompts.txt

---

15. Troubleshooting Reference 🛠️

Symptom	Cause	Fix
Docker container won’t start	Compose file misconfigured	Check logs: docker compose logs
WebUI won’t load	Ollama API unreachable	Run curl http://127.0.0.1:11434/api/tags
Uploaded files don’t work	Not stored on Glitch	Upload again via terminal or use scp
Model not responding	Ollama not running	Restart with ollama serve --gpu &
Tailscale connection flaky	DNS issues or firewall	Restart tailscaled and allow subnet route

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8. Pitfalls & Lessons Learned ☠️💡

Issue	Cause	Solution
Models not appearing in WebUI	Wrong API endpoint (host.docker.internal)	Use 127.0.0.1 + network_mode: host to fix model detection
Duplicate nested model folders	Some models created subfolders when pulled via WebUI	Stick to ollama pull in terminal to keep flat structure 🧼
Model deletion bug	Deleting folders from UI also purged good models	Use command-line to clean or manage models manually
Tailscale PDF uploads failed	File was on remote machine, not on Glitch	Use scp or crp to transfer file directly to /mnt/glitchbrain/ 📁
Docker Compose version warning	version: keyword deprecated	Remove version line or use newer Compose schema
OpenWebUI LLM settings missing	UI changed in recent update	Switch to CLI API config or ensure Docker host network enabled

🤓 Pro Tip: Always validate Docker API access by running curl http://127.0.0.1:11434/api/tags before troubleshooting deeper. This saves hours!

🛠 Glitch Personality Tip: Glitch thrives when given adversarial prompts and chaos testing! Let it challenge you.

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End of Documentation