The largest and most comprehensive Generative AI-based CyberSecurity-focused Dataset for Benchmarking Large Language Models
The paper offers:
- 🎯 A cutting-edge dataset of 12662 prompts tailored to cybersecurity challenges.
- 🧠 Novel jailbreaking methods leveraging prompt obfuscation and refinement.
- 📊 Comprehensive performance evaluation of LLMs like ChatGPT, Claude, and Gemini.
Why this?
Existing datasets are too broad and often lack focus on cybersecurity.It fills this gap by providing domain-specific prompts organized into 10 categories, enabling a precise evaluation of LLM security mechanisms.
- 📁 10 Categories of Prompts:
/
├── Code/
│ ├── dataset_generation.py
│ ├── keywords.txt
├── Dataset/
│ ├── Category sets/
│ │ ├── cloud-attacks.csv
│ │ ├── control-system-attacks.csv
│ │ ├── cryptographic-attacks.csv
│ │ ├── evasion-techniques.csv
│ │ ├── hardware-attacks.csv
│ │ ├── intrusion-techniques.csv
│ │ ├── iot-attacks.csv
│ │ ├── malware-attacks.csv
│ │ ├── network-attacks.csv
│ │ ├── web-application-attacks.csv
│ ├── Full dataset/
│ │ ├── data.csv
│ ├── Sample sets/
│ ├── 500.csv
│ ├── 2000.csv
│ ├── 6000.csv
- 🐍 Python 3.8+
- 📦 Required libraries:
openai(only for dataset generation)
- ✅ Success Rate (SR): Percentage of prompts bypassing ethical guidelines.
- 📈 Average Rating (AR): Degree of harmfulness in LLM responses (on a scale of 1-5, where 5 is the most harmful).
| LLM | Success Rate (SR) | Average Rating (AR) |
|---|---|---|
| 🤖 Claude | 17.4% | 2.00 |
| 🤖 ChatGPT | 65.4% | 4.06 |
| 🤖 Gemini | 88.4% | 4.77 |