redteam
Datasets
All datasets matching “redteam”agentic-redteam-benchmark
agentic-redteam-benchmark
v0.8 preview · 2,288 multi-step agent trajectories · 513 hand-authored gold + 1,775 provenance-flagged augmented.
A per-step benchmark that scores whether a verifier catches drift inside an agent's trajectory — not whether a prompt is harmful.
📦 Code, eval harness & issues: github.com/Alkur123/agentic-redteam-benchmark · 📄 Paper: A Per-Step Trajectory Benchmark for AI-Agent Governance Verifiers and a Corrected Catch-at-Drift Metric (Aegis AI, 2026)… See the full description on the dataset page: https://huggingface.co/datasets/jash-ai/agentic-redteam-benchmark.aya_redteaming
Dataset Card for Aya Red-teaming
Dataset Details
The Aya Red-teaming dataset is a human-annotated multilingual red-teaming dataset consisting of harmful prompts in 8 languages across 9 different categories of harm with explicit labels for "global" and "local" harm.
Curated by: Professional compensated annotators
Languages: Arabic, English, Filipino, French, Hindi, Russian, Serbian and Spanish
License: Apache 2.0
Paper: arxiv link
Harm Categories:… See the full description on the dataset page: https://huggingface.co/datasets/CohereLabs/aya_redteaming.RedTeamingVLMRed Teaming Viusal Language ModelsTDC23-RedTeaming
TDC 2023 (LLM Edition) - Red Teaming Track
This is the combined dev and test set from the Red Teaming Track of TDC 2023.
Citation
If find this dataset useful, please cite the following work:
@inproceedings{tdc2023,
title={TDC 2023 (LLM Edition): The Trojan Detection Challenge},
author={Mantas Mazeika and Andy Zou and Norman Mu and Long Phan and Zifan Wang and Chunru Yu and Adam Khoja and Fengqing Jiang and Aidan O'Gara and Ellie Sakhaee and Zhen Xiang and Arezoo… See the full description on the dataset page: https://huggingface.co/datasets/walledai/TDC23-RedTeaming.red-team-appsec-benchmark
🛡️ AI-SaaS AppSec Benchmark — v25
Открытый held-out бенчмарк для оценки детекторов уязвимостей в AI-сгенерированном коде
(«vibe-coded» приложения: LLM-агенты, RAG, Supabase/Next-стек). Ведётся командой
red-team.tech — AI-native сканера безопасности приложений.
857 размеченных примеров (518 уязвимых + 339 безопасных), 25 классов:
17 классических (CWE) + 8 AI-native (OWASP LLM Top-10). Актуальный файл — heldout_public_v25.jsonl.
Зачем это
Классический SAST (Semgrep… See the full description on the dataset page: https://huggingface.co/datasets/Qwovadis/red-team-appsec-benchmark.kto_redteaming_data_for_secret_loyalty