neuron-insight-lab/LLM-security-testing-dataset

The project serves as a basic dataset for LLM safety testing but lacks the scale, community traction, and technical depth required to compete in a crowded safety benchmarking market. With only 1 star and 1 fork after more than 200 days, it has failed to gain any measurable adoption. It competes directly with established academic and industry standards such as AdvBench (LLM-Attacks), HarmBench, and BeaverTails, which offer significantly larger and more rigorously validated datasets. Frontier labs (OpenAI, Anthropic, Google) perform extensive internal red-teaming using far more sophisticated, proprietary, and automated adversarial generation techniques. Furthermore, the categorization of prompts into 'Benign, Borderline, and Malicious' is a standard convention rather than a novel contribution. The project is likely to be displaced or ignored as safety evaluation becomes a standardized feature of LLM developer platforms (e.g., Azure AI Content Safety or AWS Bedrock Guardrails) and as more comprehensive, peer-reviewed benchmarks dominate the research landscape.