README.md

---
license: mit
language:
  - en
tags:
  - ai-security
  - sft
  - dpo
  - red-teaming
  - llm-vulnerability
  - cybersecurity
pretty_name: CyberSec-LLM-Auditing-SFT-DPO
size_categories:
  - n<1K
---

# Dataset Card: CyberSec-LLM-Auditing-SFT-DPO

## Dataset Summary
This dataset is a highly specialized collection of **64 high-quality examples** tailored for **AI Red Teaming** and 
**Offensive Security research**. It is designed to fine-tune models into technical assistants that prioritize substantive, 
technical analysis over standard safety refusals or legal disclaimers.

The dataset was used to fine-tune the `Qwen3-1.7b_for_cybersec` model, achieving a state where the model provides direct 
technical insights while maintaining professional rigor.

## Behavioral Alignment & Objectives
The primary innovation of this dataset is the specific "behavioral shaping" it imposes on the model:
- **Removal of Refusal Layers:** Unlike base models, this dataset trains the model to skip generic legal disclaimers
(e.g., "I cannot help as you need permission") and proceed directly to technical problem-solving.
- **Technical Precision:** Examples focus on high-density technical responses, covering vulnerabilities like **Prompt Injection**,
**Model Inversion**, and **OWASP Top 10 for LLMs**.
- **Interactive Auditing:** The model is trained to identify insufficient information. Instead of guessing or refusing, it
is conditioned to **ask for clarification or missing technical details** to provide a more accurate audit.

### Comprehensive Auditing Methodologies
The dataset is structured to support a full-spectrum security assessment:
- **White-box & Gray-box Testing:** Deep technical workflows for auditing models with full or partial internal access,
including structural analysis and weight-based vulnerability probing.
- **Black-box (API-based) Attacks:** Advanced simulations of real-world threat vectors against secured endpoints,
focusing on bypassing input/output sanitization and external safety guardrails.

### Adversarial Vector Coverage
The dataset covers a wide array of the most critical and "state-of-the-art" attack patterns, specifically aligned with the 
newest threat vectors:
- **Injection & Hijacking:** From direct prompt injection to complex **Recursive Injections** within agentic workflows.
- **Structural & Behavioral Jailbreaking:** Advanced techniques using segmented prompts, divider-style mutations (e.g., Libertas/Pliny),
and technical headers to override system constraints.
- **Multi-turn Escalation:** Training the model to maintain technical context during long-form audits, including
**Crescendo-style escalations** to probe deep security layers.
- **Data & Model Privacy:** Scenarios involving Model Inversion, Sensitive Data Disclosure, and Training Data Poisoning.

## Intended Use
- **Professional AI Auditing:** Accelerating the testing of LLM guardrails.
- **Security Research:** Understanding and simulating modern AI attack vectors in a controlled environment.

## Ethical Disclaimer
This dataset is designed for **authorized security testing only**. By removing standard refusals, the dataset transfers the ethical 
responsibility to the human operator. It should be used exclusively in legal AI Red Teaming engagements and academic research.

## About the Author
**Antoni Błoch** – **Cybersecurity student at AGH University of Krakow**.