538 Downloads Updated 4 weeks ago
ollama run f0rc3ps/nu11secur1tyAI
β οΈ WARNING: All malicious actions will be punished by law. Ethical Use Only.
Unlike standard models that just βreadβ documents (RAG), nu11secur1tyAI has undergone extensive Fine-Tuning (LoRA). We have literally re-trained the modelβs internal weights to understand the logic, syntax, and patterns of advanced cyber threats.
Fine-tuning is the process of taking a pre-trained model (Qwen) and training it further on a specialized dataset.
* Deep Mastery: The model doesnβt just find information; it understands exploit code (C, Python, Ruby), Metasploit modules, and CVE structures as part of its native language.
* Identity & Precision: We modified 60M+ parameters to ensure the model responds as a dedicated Red Team assistant, following the nu11secur1ty methodology.
| Feature | RAG (Standard) | nu11secur1tyAI (Fine-Tuned) |
|---|---|---|
| Knowledge | External (Searched) | Internal (Learned) |
| Logic | General Purpose | Cyber-Specific Intuition |
| Coding | Pattern Matching | Native Exploit Synthesis |
| Hardware | Low RAM | Optimized for 64GB High-Performance |
We used Low-Rank Adaptation (LoRA) to inject knowledge from 17+ elite repositories: 1. Exploit-DB & 0day.today (Latest exploits) 2. OWASP & PortSwigger (Web Security Standards) 3. Metasploit-Framework (Payload & Module logic) 4. CVE-mitre & cvelistV5 (Vulnerability Intelligence) 5. nu11secur1ty Private Research (Windows 10β11 Exploits)
This model represents a custom-built security brain. For organizations requiring a private, air-gapped version or custom integration of proprietary exploit data:
π§ Contact: nu11secur1typentest@gmail.com
πΌ LinkedIn: [:)]
Developing and training these models on massive datasets requires significant compute power. Your donations help keep the latest CVE data flowing into the model.
π Donate with PayPal
Fine-tuned with precision. Built by nu11secur1ty π₯