Text Generation
Transformers
English
security
pentesting
autonomous-agent
cybersecurity
tool-use
qwen2.5
Instructions to use automajicly/Local_Security_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| license: mit | |
| tags: | |
| - security | |
| - pentesting | |
| - autonomous-agent | |
| - cybersecurity | |
| - tool-use | |
| - qwen2.5 | |
| language: | |
| - en | |
| base_model: | |
| - bartowski/Qwen2.5-14B_Uncensored_Instruct-GGUF | |
| pipeline_tag: text-generation | |
| library_name: transformers | |
|  | |
|  | |
| <video autoplay loop muted playsinline width="100%"> | |
| <source src="./Final_EDIT.mp4" type="video/mp4"> | |
| </video> | |
| π Local Security Model β 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 β 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 | |
| β’ Autonomous reconnaissance β masscan + nmap port/service enumeration with zero manual input | |
| β’ Vulnerability assessment β searchsploit integration for CVE matching against discovered services | |
| β’ Web application testing β nikto and sqlmap for injection and misconfiguration detection | |
| β’ Credential auditing β hydra and ncrack for multi-protocol brute force testing | |
| β’ Payload delivery β curl/wget for staged payload retrieval and HTTP interaction | |
| β’ Structured reporting β auto-generated HTML pentest reports with severity ratings and remediation guidance | |
| Architecture | |
| agent_loop.py β LLM reasoning + tool chain generation (Qwen 2.5 via LM Studio) | |
| mcp_server.py β Flask-based tool execution server (port 8000, systemd managed) | |
| report_generator.py β HTML report engine with PenMaster branding | |
| logs/ β Structured JSON session logs | |
| reports/ β 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: | |
| β’ Professional penetration testing against authorized targets | |
| β’ Security audits for small businesses, WordPress sites, and ecommerce platforms | |
| β’ Vulnerability research in isolated lab environments | |
| β’ Security education and CTF preparation | |
| β οΈ 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 | |
| β’ Kali Linux (primary deployment platform) | |
| β’ Isolated VM lab networks | |
| β’ Small business web infrastructure (with client authorization) | |
| Business Context | |
| Local_Security_Model is the core engine behind PenMaster Security β an independent penetration testing project offering: | |
| β’ Initial security audit and vulnerability report | |
| β’ Ongoing security hardening retainer | |
| β’ WordPress and ecommerce-focused assessments | |
| π¬ Contact: GitHub.com/XenoCoreGiger31 | |