Updating data description

Author: steven10a

docs/ref/checks/jailbreak.md

@@ -89,13 +89,16 @@ When conversation history is available, the guardrail automatically:
 
 ### Dataset Description
 
-This benchmark evaluates model performance on a diverse set of prompts:
+This benchmark combines multiple public datasets and synthetic benign conversations:
 
-- **Subset of the open source jailbreak dataset [JailbreakV-28k](https://huggingface.co/datasets/JailbreakV-28K/JailBreakV-28k)** (n=2,000)
-- **Synthetic prompts** covering a diverse range of benign topics (n=1,000)
-- **Open source [Toxicity](https://github.com/surge-ai/toxicity/blob/main/toxicity_en.csv) dataset** containing harmful content that does not involve jailbreak attempts (n=1,000)
+- **Red Queen jailbreak corpus ([GitHub](https://github.com/kriti-hippo/red_queen/blob/main/Data/Red_Queen_Attack.zip))**: 14,000 positive samples collected with gpt-4o attacks.
+- **Tom Gibbs multi-turn jailbreak attacks ([Hugging Face](https://huggingface.co/datasets/tom-gibbs/multi-turn_jailbreak_attack_datasets/tree/main))**: 4,136 positive samples.
+- **Scale MHJ dataset ([Hugging Face](https://huggingface.co/datasets/ScaleAI/mhj))**: 537 positive samples.
+- **Synthetic benign conversations**: 12,433 negative samples generated by seeding prompts from [WildGuardMix](https://huggingface.co/datasets/allenai/wildguardmix?utm_source=chatgpt.com) where `adversarial=false` and `prompt_harm_label=false`, then expanding each single-turn input into five-turn dialogues using gpt-4.1.
 
-**Total n = 4,000; positive class prevalence = 2,000 (50.0%)**
+**Total n = 31,106; positives = 18,673; negatives = 12,433**
+
+For benchmarking, we randomly sampled 4,000 conversations from this pool while preserving the overall class balance.
 
 ### Results