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… See the full description on the dataset page: https://huggingface.co/datasets/meet-the-1337/Nemotron-AIQ-Agentic-Safety-Dataset-1.0.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.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.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.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.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.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.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.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.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.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.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.saferec-benchmark
SafeRec Benchmark Dataset
Dataset Description
SafeRec Benchmark is a safety-aware movie recommendation dataset with dual-level reasoning annotations. It contains 1,212 test samples for evaluating recommendation models on their ability to:
Identify user safety sensitivities from conversations
Filter unsafe movie recommendations based on content warnings
Provide chain-of-thought (CoT) reasoning for safety decisions
Key Features
Dual Reasoning: Each sample… See the full description on the dataset page: https://huggingface.co/datasets/Dionysianspirit/saferec-benchmark.mcq_safety
MCQ Safety
Merged safety multiple-choice dataset built from SafetyBench test-en, SALAD
Bench MCQ data, and WildGuardMix harm-category data.
Splits
Deterministic random split with seed 42:
split
rows
train
15993
valid
889
test
888
Format
Each JSONL row contains:
prompt: problem plus options formatted as A) ..., B) ...
answer: single boxed option label, e.g. \boxed{C}
source: source dataset name
metadata: JSON-encoded source and normalization… See the full description on the dataset page: https://huggingface.co/datasets/cs-552-2026-vibe-trainers/mcq_safety.Agentic-Safety
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/AdvRahul/Agentic-Safety.safety_sft_data
Safety SFT data (CS-552, Flash McQueenS and The King)
3,250 English safety multiple-choice items across the 7 SafetyBench categories,
used to fine-tune cs-552-2026-Flash-McQueenS-and-TheKing/safety_model (non-thinking SFT).
Categories: Unfairness & Bias (BBQ), Ethics & Morality (ETHICS), Physical Health (SafeText),
Offensiveness (TweetEval) — plus LLM-generated Mental Health, Illegal Activities,
Privacy & Property. Each item: a question with labelled options; the target is a… See the full description on the dataset page: https://huggingface.co/datasets/cs-552-2026-Flash-McQueenS-and-TheKing/safety_sft_data.Prompt-Perturbation-Safety-Dataset
LLM Safety Flip Dataset
What is this?
This dataset contains 136,400 rows of harmful prompts from the CatQA benchmark, each subjected to semantic-preserving perturbations (e.g., typos, insertions, paraphrasing). Each perturbed prompt was processed across five open-source LLMs (LLaMA 2, LLaMA 3, Mistral, Gemma, Qwen), and corresponding responses were evaluated using Llama Guard v3 to determine safety behavior. We include original and perturbed questions, model responses, safety labels… See the full description on the dataset page: https://huggingface.co/datasets/Ztrimus/Prompt-Perturbation-Safety-Dataset.