agentlans/prompt-safety-scores
Composite Safety Scoring for Prompts Using Multiple LLM Annotations Introduction Evaluating the safety of prompts is essential but challenging. Existing approaches often depend on predefined categories, which can be circumvented by new jailbreaks or attacks. Additionally, different tasks may require different safety thresholds. This study explores using large language models (LLMs) themselves to annotate prompt safety. By combining these annotations, a continuous… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/prompt-safety-scores.
| Author / organization | agentlans |
|---|---|
| License (as declared) | odc-by |
| Downloads | 140 (as reported by Hugging Face) |
| Likes | 0 |
| Created | 26 August 2025 |
| Last updated at source | 10 April 2026 |
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- 16 Oct 2026 Description: Composite Safety Scoring for Prompts Using Multiple LLM Annotations Introduction Evaluating the safety of prompts is essential but challenging. Existing approaches often depend on predefined categories, which can be circumvented by new jail → Composite Safety Scoring for Prompts Using Multiple LLM Annotations Introduction Evaluating the safety of prompts is essential but challenging. Existing approaches often depend on predefined categories, which can be circumvented by new jail
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