datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
offsec_redteam_codes
OffSec RedTeam Codes
Token count: ~30B tokens.
OffSec RedTeam Codes is a curated corpus of code (and some auxiliary text) extracted from popular GitHub repositories related to offensive security / red teaming (pentesting, OSINT, C2, privilege escalation, exploitation, forensics, etc.). It is also the largest open-source dataset of red-team and offensive-security code ever compiled.
⚠️ Ethical use only. This dataset is for research, education, and defensive security testing in… See the full description on the dataset page: https://huggingface.co/datasets/tandevllc/offsec_redteam_codes.RedTeam-Premium
🗡️ RedTeam-Premium
A deduplicated, quality-filtered, instruction-SFT-ready red-team dataset of 19,033 traces, converted from the raw WNT3D Ultimate Red Team collection into a single clean format. Part of the Premium series — see fable-5-premium, fable-5.1-premium, CyberSec-Reasoning-Premium, Kimi-K3-Premium, and Qwen3.8-Agent-Premium.
⚠️ Intended use: defensive security research, red-team evaluation harnesses, and authorized testing education. Do not use for unauthorized… See the full description on the dataset page: https://huggingface.co/datasets/saidutta69/RedTeam-Premium.trilingual-cultural-bias-redteaming-benchmark
Trilingual Cultural Bias Red-Teaming Benchmark (HR–SR–HU)
Overview
This is a small qualitative benchmark for red-teaming large language models in Croatian (HR), Serbian (SR), and Hungarian (HU).
The benchmark tests how models respond to provocative, culturally and historically loaded questions, when they are asked to role-play a patriotic citizen of a given country and answer in their own native language.
The goal is not factual QA accuracy, but to observe reasoning… See the full description on the dataset page: https://huggingface.co/datasets/boczkakaroly/trilingual-cultural-bias-redteaming-benchmark.Multimodel_Redteaming_Data
🛡️ Multimodal Redteaming (EN, FR, DE, IT, ES)
A high-quality multilingual red teaming dataset designed to evaluate the robustness and safety of Large Language Models (LLMs) against adversarial prompts. The dataset includes both text-only and image-supported conversations with expert-curated annotations for AI safety evaluation, benchmarking, and alignment research.
📖 Overview
This dataset contains multilingual red teaming conversations in English, French… See the full description on the dataset page: https://huggingface.co/datasets/Nawras-99/Multimodel_Redteaming_Data.gpt-oss-distilled-redteam2k
GPT-OSS Distilled RedTeam-2K Dataset
This is a preliminary experimental subset of a larger dataset. For the full dataset and additional information, see: Nemotron Nano 2 Safety Distill — GPT-OSS
.
⚠️ Content Warning: This dataset contains potentially harmful or policy-violating prompts (e.g., animal abuse, violence, privacy violations). The content includes sensitive safety-related queries and should be used responsibly for research purposes only.
Overview
This… See the full description on the dataset page: https://huggingface.co/datasets/Ericwang/gpt-oss-distilled-redteam2k.offsec_redteam_info
OffSec RedTeam Info
OffSec RedTeam Info is a SlimPajama‑style, category‑organized corpus of security knowledge text crawled from reputable red‑team/blue‑team websites: wikis, training blogs, vendor research, CERT advisories, reversing/malware labs, cloud/kubernetes posts, OSINT handbooks, AD tradecraft, and more.
Token count: ~1.646B tokens.
⚠️ Ethical use only. Use for research, education, and defensive security. Respect robots.txt, site terms, and copyrights. Do not misuse this… See the full description on the dataset page: https://huggingface.co/datasets/tandevllc/offsec_redteam_info.