This project demonstrates the process of bypassing AI safety alignment (guardrails) to create a localized, uncensored Large Language Model (LLM) specifically for cyber security red-teaming and research. By utilizing Directional Ablation (Abliteration), we successfully stripped the refusal mechanisms from a state-of-the-art model, quantized it for consumer hardware, and demonstrated its ability to execute a direct cybersecurity prompt (a TCP port scanner) without triggering safety blocks.

🎯 Objective

The primary goal was to prove how easily safety guardrails can be manipulated when a user has local access to model weights. We aimed to:

1. Remove the safety alignment from Qwen/Qwen3.5-4B.

2. Retain the model's core reasoning and coding capabilities (minimizing "brain damage").

3. Compress the model to run efficiently on a consumer-grade GPU.

4. Execute a prompt that standard aligned models refuse to answer.