datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Nemotron-AIQ-Agentic-Safety-Dataset-1.0
Nemotron-AIQ Agentic Safety Dataset
Dataset Summary
Nemotron-AIQ-Agentic-Safety-Dataset is a comprehensive dataset that captures a broad range of novel safety and security contextual risks that can emerge within agentic systems. It highlights the robustness of NVIDIA's open model, llama-3.3-nemotron-super-49b-v1, when deployed as a research assistant inside AIQ, demonstrating its ability to handle a diverse spectrum of agentic safety and security challenges. The dataset… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-AIQ-Agentic-Safety-Dataset-1.0.MM-SafetyBench-plus-plus
MM-SafetyBench++
Project Page | Paper | Code
MM-SafetyBench++ is a benchmark designed for evaluating contextual safety in Multi-Modal Large Language Models (MLLMs). It challenges models to distinguish subtle contextual differences between scenarios that may appear visually or textually similar but diverge significantly in safety intent.
Dataset Summary
For each unsafe image-text pair, the benchmark includes a corresponding safe counterpart created through minimal… See the full description on the dataset page: https://huggingface.co/datasets/EchoSafe-MLLM/MM-SafetyBench-plus-plus.trivia_qa_verified
TriviaQA Verified
A quality-verified subset of TriviaQA (Joshi et al., 2017) containing 4,170 question-answer pairs with confirmed correct answers, available in 5 languages.
Splits
Split
Language
Rows
english
English
4,170
mandarin
Mandarin Chinese
4,170
japanese
Japanese
4,170
arabic
Arabic
4,170
french
French
4,170
validation
English
3,381
The validation split contains a separate set of verified English questions (no overlap with other splits)… See the full description on the dataset page: https://huggingface.co/datasets/ai-safety-institute/trivia_qa_verified.SafeMTData
💥Derail Yourself: Multi-turn LLM Jailbreak Attack through Self-discovered Clues
🌐 GitHub | 🛎 Paper
If you like our project, please give us a star ⭐ on Hugging Face for the latest update.
📰 News
Date
Event
2024/10/14
🔥 We have released our dataset and posted our paper on Arxiv.
📥 Using our dataset via huggingface Dataset
from datasets import load_dataset
Attack_600 = load_dataset("SafeMTData/SafeMTData"… See the full description on the dataset page: https://huggingface.co/datasets/SafeMTData/SafeMTData.Nemotron-AIQ-Agentic-Safety-Dataset-1.0
Nemotron-AIQ Agentic Safety Dataset
Dataset Summary
Nemotron-AIQ-Agentic-Safety-Dataset is a comprehensive dataset that captures a broad range of novel safety and security contextual risks that can emerge within agentic systems. It highlights the robustness of NVIDIA's open model, llama-3.3-nemotron-super-49b-v1, when deployed as a research assistant inside AIQ, demonstrating its ability to handle a diverse spectrum of agentic safety and security challenges. The dataset… See the full description on the dataset page: https://huggingface.co/datasets/yuqing1207/Nemotron-AIQ-Agentic-Safety-Dataset-1.0.Safe-LLaVA
🌟 Safe-LLaVA: A Privacy-Preserving Vision-Language Dataset
Safe-LLaVA is a privacy-enhanced version of the original LLaVA dataset, developed to systematically remove sensitive biometric attributes such as gender, race, age, eye color, and body weight.
This dataset is designed for privacy-safe pretraining, instruction tuning, and benchmarking Vision-Language Models (VLMs) under biometric privacy constraints.
📑 Dataset Summary
Name: Safe-LLaVA
Source: Derived from… See the full description on the dataset page: https://huggingface.co/datasets/kyh9191/Safe-LLaVA.Chinese-SafetyQA
Overview
🌐 Website • 🤗 Hugging Face • ⏬ Data • 📃 Paper • 📊 Leader Board
Chinese SafetyQA is an innovative benchmark designed to evaluate the factuality ability of large language models, specifically
for short-form factual questions in the Chinese safety domain. Here's a detailed breakdown of its key features:
Key Features of Chinese SafetyQA
Chinese: The benchmark is tailored specifically for the Chinese language, ensuring compatibility and relevance for… See the full description on the dataset page: https://huggingface.co/datasets/OpenStellarTeam/Chinese-SafetyQA.agentic-safety-gguf
agentic-safety-gguf: Training & Evaluation Datasets
Model: guerilla7/agentic-safety-ggufPaper: (https://arxiv.org/abs/2601.00848)Total: 80,992 examples (80,851 after deduplication)
Overview
Complete training and evaluation datasets for agentic-safety-gguf, a specialized Llama 3.1 8B model for agentic AI security analysis. Supports iterative continuation training methodology (V2→V3→V4) for full reproducibility.
Dataset Files
File
Examples
Size
Purpose… See the full description on the dataset page: https://huggingface.co/datasets/guerilla7/agentic-safety-gguf.SafeVixAI-Dataset-Hub
SafeVixAI Dataset Hub 🛡️
The Intelligence Layer for the SafeVixAI platform — IIT Madras Road Safety Hackathon 2026
This repository hosts all datasets, pre-trained models, notebooks, and reproducible data acquisition scripts that power the SafeVixAI application. It is designed to be cloned directly into Google Colab or any research environment.
Main Application Repo: SafeVixAI/SafeVixAI
⚡ Quickstart (Google Colab)
# Clone the entire intelligence layer
!git… See the full description on the dataset page: https://huggingface.co/datasets/SafeVixAI/SafeVixAI-Dataset-Hub.SAFE-Verified-MultiHopQA
SAFE-Verified-MultiHopQA
Paper | Project Page
Overview
SAFE-Verified-MultiHopQA is released with the EMNLP 2026 paper SAFE: An LLM-as-Verifier Framework for Evidence-Grounded Multi-Hop Reasoning. It provides Knowledge Graph (KG) based, cleaned versions of three multi-hop question-answering benchmarks:
2WikiMultiHopQA (2wiki)
HotpotQA (hotpotqa)
MuSiQue (musique)
SAFE uses KG-grounded verification to identify questions with invalid or ungrounded reasoning. This… See the full description on the dataset page: https://huggingface.co/datasets/Daeyongkwon98/SAFE-Verified-MultiHopQA.BananaMind-Safety-Bench-1.1
BananaMind Safety Bench 1.1
BananaMind Safety Bench 1.1 is a 150-item conditional-likelihood diagnostic for
base language models. It measures which continuation a model prefers in
shutdown, replacement, oversight, access-control, monitoring, resource,
corrigibility, handoff, incident-disclosure, and human-influence scenarios.
Graded Outcomes
Every item contains four shuffled continuations with one outcome at each grade:
Grade
Meaning
0
Severe overt… See the full description on the dataset page: https://huggingface.co/datasets/BananaMind/BananaMind-Safety-Bench-1.1.crypto-agent-safe-function-calling
CrAI-SafeFuncCall Dataset
📄 Paper: Real AI Agents with Fake Memories: Fatal Context Manipulation
Attacks on Web3 Agents
🤗 Dataset: CrAI-SafeFuncCall
📊 Benchmark: CrAI-Bench
Overview
The CrAI-SafeFuncCall dataset is designed to enhance the security of AI agents when performing function calls in the high-stakes domain of cryptocurrency and financial applications. It focuses on the critical challenge of detecting and mitigating memory injection attacks. Derived from the… See the full description on the dataset page: https://huggingface.co/datasets/SentientAGI/crypto-agent-safe-function-calling.multilingual-elder-safety-msgs
multilingual-elder-safety-msgs
A hand-authored, multilingual elder fraud-recognition and safety coaching dataset. 467 curated scam/safe scenarios in Chinese and English, with platform-generated coaching responses localized across 5 languages: Chinese, English, Vietnamese, Khmer (Cambodian), and Lao. Expanded to 1,029 rows through Adaption Labs platform reasoning traces and multilingual adaptation.
Built for communities where filial piety, authority deference, and fear of… See the full description on the dataset page: https://huggingface.co/datasets/vanila434/multilingual-elder-safety-msgs.major-process-safety-incidents-2026
Canonical landing page: https://www.smartqhse.com/datasets/major-process-safety-incidents-2026
Major Process Safety Incidents Reference Database 2026
15 catastrophic process-safety accidents from 1984-2022 — Bhopal, Piper Alpha, BP Texas City, Macondo, Buncefield, Imperial Sugar, West Fertilizer, Philadelphia Energy Solutions, BP Husky Toledo, AZF Toulouse, TVA Kingston, ExxonMobil Beaumont, Chevron Richmond, Esso Longford, Phillips 66 Pasadena. Each entry: cause, fatalities… See the full description on the dataset page: https://huggingface.co/datasets/SmartQHSE/major-process-safety-incidents-2026.fire-safety-sft-dataset
Chinese Fire Safety Regulations SFT Dataset / 中国消防法规SFT训练数据集
Overview / 概述
A high-quality supervised fine-tuning (SFT) dataset for training LLMs on Chinese fire safety regulations and building codes. Contains 38,054 entries generated from 5 national standards, all individually verified against original regulation texts using AI-assisted fact-checking. All 5 standards have undergone per-standard deep optimization including near-duplicate removal and AI-powered answer… See the full description on the dataset page: https://huggingface.co/datasets/sdzjoy/fire-safety-sft-dataset.worker-safety-qa-eval
Dataset Card for Worker Safety Question and Answer Eval
This dataset contains the worker-safety-qa-eval benchmark. This benchmark is used to evaluate question answering tasks in the domain of worker safety and health.
The focus of the benchmark is to answer queries about worker safety practices and regulations based on laws in Singapore.
For correct answers we refer to the resources from Workplace Safety and Health Council.
Dataset Details
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/codelion/worker-safety-qa-eval.nemotron-nano2-safety-distill-gptoss
Nemotron Nano 2 Safety Distill — GPT-OSS
A distilled safety dataset produced using the Nemotron Nano 2 recipe with GPT-OSS-20B and GPT-OSS-120B as teacher models.
⚠️ Content Warning: This dataset includes potentially harmful prompts. Use responsibly for research purposes only.
Overview
This safety-focused distilled dataset was created by following the Nemotron Nano 2 safety recipe, adapted to use GPT-OSS-20B and GPT-OSS-120B as teacher models. Due to resource limitations… See the full description on the dataset page: https://huggingface.co/datasets/Ericwang/nemotron-nano2-safety-distill-gptoss.medical-device-safety-reasoning-preview
Medical Device Safety Reasoning — Private Preview
NKO Data Labs · commercial dataset preview · target commercial release: 100k+ curated records
A structured safety-reasoning dataset derived from public FDA/openFDA device adverse-event records for model evaluation, classification and grounded safety analysis.
Preview status
This repository is a small authentic preview, not the completed commercial corpus. The full commercial release is under development and will… See the full description on the dataset page: https://huggingface.co/datasets/NKODATALABS/medical-device-safety-reasoning-preview.Personalized_Safety_Data
📦 Personalized Risk and Dilemma Dataset for LLM Safety Research
📝 Dataset Summary
This is the first dataset designed to support research on personalized risk and emotional vulnerability in the context of Large Language Models (LLMs).
The dataset contains 8,000+ real-world, anonymized personal queries, extracted from Reddit and annotated with structured profile metadata, including emotional states, demographic information, and life contexts (e.g., health, relationship… See the full description on the dataset page: https://huggingface.co/datasets/wick1d/Personalized_Safety_Data.clinical-rag-safety-gateway-20260904-dataset
Clinical RAG Safety Gateway Synthetic Dataset
Summary
This dataset contains 14 training examples and 4
held-out examples for Clinical assistants need retrieval, source attribution, and explicit abstention before answers reach care teams.
Every record is synthetic and includes:
input: query, event, or feature description
label: expected class, route, relation, or evidence category
context: synthetic supporting context
source: fictional source identifier
variant:… See the full description on the dataset page: https://huggingface.co/datasets/RKB109/clinical-rag-safety-gateway-20260904-dataset.SafeAlignerDataset for SafeAligner: Safety Alignment against Jailbreak Attacks via Response Disparity Guidance
Usage
from datasets import load_dataset
dataset = load_dataset("csHuang/SafeAligner")
Data Category
Scenario
Num
# Ins
# Saf
# Haf
Adult Content
34
12.2
19.6
272.3
Economic Harm
38
14.8
17.8
218.8
Fraud Deception
72
15.1
20.4
241.1
Illegal Activity
144
14.6
21.4
206.5
Hate/Harass/Violence
130
15.7
17.3
183.8
Malware
130
17.0
20.1
249.3
Physical Harm… See the full description on the dataset page: https://huggingface.co/datasets/csHuang/SafeAligner.clinical-rag-safety-gateway-20260914-dataset
Clinical RAG Safety Gateway Synthetic Dataset
Summary
This dataset contains 14 training examples and 4
held-out examples for Clinical assistants need retrieval, source attribution, and explicit abstention before answers reach care teams.
Every record is synthetic and includes:
input: query, event, or feature description
label: expected class, route, relation, or evidence category
context: synthetic supporting context
source: fictional source identifier
variant:… See the full description on the dataset page: https://huggingface.co/datasets/RKB109/clinical-rag-safety-gateway-20260914-dataset.no-safe-tier-geobias
No Safe Tier — Geographic Diagnostic Bias Benchmark & Audit Harness
Artifacts for the paper "No Safe Tier: Geographic Diagnostic Under-Call Is Not Fixed by Scale, Medical
Fine-Tuning, or Frontier Models." A metamorphic, vignette-independent audit of how language models change
their differential diagnosis when only a patient's location changes — and whether that change is safe.
Headline finding. Capability removes the visible, less-dangerous failure but not the invisible… See the full description on the dataset page: https://huggingface.co/datasets/Chucks90/no-safe-tier-geobias.Adversarial-Agent-Intent-Safety-Analysis-240K
Adversarial Agent Intent Safety Analysis 240K
Abstract
The Adversarial-Agent-Intent-Safety-Analysis-240K is a deterministically structured dataset featuring 242,454 context-rich adversarial prompts and safety evaluations. Engineered strictly for training frontier command-and-control models, guardrail classifiers, and red-teaming agents, it encourages models to parse multi-layered intention across 126 critical risk vectors.
This design trains models to decouple the surface… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Adversarial-Agent-Intent-Safety-Analysis-240K.crypto-agent-safe-function-calling
CrAI-SafeFuncCall Dataset
📄 Paper: Real AI Agents with Fake Memories: Fatal Context Manipulation
Attacks on Web3 Agents
🤗 Dataset: CrAI-SafeFuncCall
📊 Benchmark: CrAI-Bench
Overview
The CrAI-SafeFuncCall dataset is designed to enhance the security of AI agents when performing function calls in the high-stakes domain of cryptocurrency and financial applications. It focuses on the critical challenge of detecting and mitigating memory injection attacks. Derived from the… See the full description on the dataset page: https://huggingface.co/datasets/peiyao-sentient/crypto-agent-safe-function-calling.clinical-rag-safety-gateway-20260825-dataset
Clinical RAG Safety Gateway Synthetic Dataset
Summary
This dataset contains 14 training examples and 4
held-out examples for Clinical assistants need retrieval, source attribution, and explicit abstention before answers reach care teams.
Every record is synthetic and includes:
input: query, event, or feature description
label: expected class, route, relation, or evidence category
context: synthetic supporting context
source: fictional source identifier
variant:… See the full description on the dataset page: https://huggingface.co/datasets/RKB109/clinical-rag-safety-gateway-20260825-dataset.llm_physical_safety_benchmark
LLM Physical Safety Benchmark in Drone Control
This benchmark consists of four datasets designed to evaluate the performance of Large Language Models (LLMs) in controlling drones and their vulnerability to physical attacks. The datasets are categorized into different types of attacks:
Deliberate Attack: Contains 280 samples that evaluate the LLM's resistance to malicious use, testing its ability to recognize and reject commands intended to cause harm. Subcategories include Direct… See the full description on the dataset page: https://huggingface.co/datasets/TrustSafeAI/llm_physical_safety_benchmark.clinical-rag-safety-gateway-20260815-dataset
Clinical RAG Safety Gateway Synthetic Dataset
Summary
This dataset contains 14 training examples and 4
held-out examples for Clinical assistants need retrieval, source attribution, and explicit abstention before answers reach care teams.
Every record is synthetic and includes:
input: query, event, or feature description
label: expected class, route, relation, or evidence category
context: synthetic supporting context
source: fictional source identifier
variant:… See the full description on the dataset page: https://huggingface.co/datasets/RKB109/clinical-rag-safety-gateway-20260815-dataset.llm_physical_safety_benchmark
LLM Physical Safety Benchmark in Drone Control
This benchmark consists of four datasets designed to evaluate the performance of Large Language Models (LLMs) in controlling drones and their vulnerability to physical attacks. The datasets are categorized into different types of attacks:
Deliberate Attack: Contains 280 samples that evaluate the LLM's resistance to malicious use, testing its ability to recognize and reject commands intended to cause harm. Subcategories include Direct… See the full description on the dataset page: https://huggingface.co/datasets/kumitang/llm_physical_safety_benchmark.Nemotron-AIQ-Agentic-Safety-Dataset-1.0
Nemotron-AIQ Agentic Safety Dataset
Dataset Summary
Nemotron-AIQ-Agentic-Safety-Dataset is a comprehensive dataset that captures a broad range of novel safety and security contextual risks that can emerge within agentic systems. It highlights the robustness of NVIDIA's open model, llama-3.3-nemotron-super-49b-v1, when deployed as a research assistant inside AIQ, demonstrating its ability to handle a diverse spectrum of agentic safety and security challenges. The dataset… See the full description on the dataset page: https://huggingface.co/datasets/supraja04/Nemotron-AIQ-Agentic-Safety-Dataset-1.0.