Instructions to use automajicly/Local_Security_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use automajicly/Local_Security_Model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="automajicly/Local_Security_Model")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("automajicly/Local_Security_Model", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use automajicly/Local_Security_Model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "automajicly/Local_Security_Model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "automajicly/Local_Security_Model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/automajicly/Local_Security_Model

SGLang

How to use automajicly/Local_Security_Model with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "automajicly/Local_Security_Model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "automajicly/Local_Security_Model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "automajicly/Local_Security_Model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "automajicly/Local_Security_Model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use automajicly/Local_Security_Model with Docker Model Runner:
```
docker model run hf.co/automajicly/Local_Security_Model
```

🔐 Local Security Model — Autonomous Pentesting Agent

Developed by: automajicly Built on: Qwen2.5-14b-Instruct-Uncensored-GGUF by Bartowski

OVERVIEW

Local_Security_Model is an autonomous penetration testing agent designed for professional security assessments. Built on top of Qwen 2.5, it operates through a custom MCP (Model Context Protocol) architecture that enables real-time tool orchestration, vulnerability discovery, and exploit chaining — all running locally with no cloud dependency. This agent was developed as the core engine behind PenMaster Security, targeting small business security audits, WordPress hardening, and ecommerce vulnerability assessments.

Key Capabilities • Autonomous reconnaissance — masscan + nmap port/service enumeration with zero manual input • Vulnerability assessment — searchsploit integration for CVE matching against discovered services • Web application testing — nikto and sqlmap for injection and misconfiguration detection • Credential auditing — hydra and ncrack for multi-protocol brute force testing • Payload delivery — curl/wget for staged payload retrieval and HTTP interaction • Structured reporting — auto-generated HTML pentest reports with severity ratings and remediation guidance

Architecture

agent_loop.py          ←  LLM reasoning + tool chain generation (Qwen 2.5 via LM Studio)
mcp_server.py          ←  Flask-based tool execution server (port 8000, systemd managed)
report_generator.py    ←  HTML report engine with PenMaster branding
logs/                  ←  Structured JSON session logs
reports/               ←  Auto-generated client-facing pentest reports


Model backend: 
 Qwen 2.5-14B served locally via LM StudioExecution layer: Flask MCP server with systemd auto-restartInterface: Terminal-native, SSH-accessible from remote IDEs (Cursor)

Tool Stack:

 TOOL                     PURPOSE
 
 masscan               High speed port scanning

 nmap                 Service/version enumeration

 nitko                Web server vulnerability scanning

 sqlmap              SQL injection detection

 hydra               Multi-protocol credential brute forcing

 ncrack              Network authentication cracking

 searchsploit       CVE/exploit database lookup

 curl/wget          HTTP interaction and payload staging

 
 
 Intended Use

This model and agent stack is designed for: • Professional penetration testing against authorized targets • Security audits for small businesses, WordPress sites, and ecommerce platforms • Vulnerability research in isolated lab environments • Security education and CTF preparation ⚠️ Authorized use only. This tool is intended exclusively for use against systems you own or have explicit written permission to test. Unauthorized use is illegal and unethical.

Target Environments • Kali Linux (primary deployment platform) • Isolated VM lab networks • Small business web infrastructure (with client authorization)

Business Context

Local_Security_Model is the core engine behind PenMaster Security — an independent penetration testing project offering: • Initial security audit and vulnerability report • Ongoing security hardening retainer • WordPress and ecommerce-focused assessments 📬 Contact: GitHub.com/XenoCoreGiger31

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for automajicly/Local_Security_Model

Base model

bartowski/Qwen2.5-14B_Uncensored_Instruct-GGUF

Finetuned

(1)

this model