datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
arxiv-complete
arXiv Complete Corpus
A snapshot of arXiv's metadata, version history, submission files and rendered
documents. It covers 3,148,796 papers and includes file contents, paths, sizes
and SHA-256 digests. Metadata comes from arXiv's OAI-PMH arXivRaw interface;
files come from the GCS mirror, S3 source archives and direct PDF fetches.
This release holds a PDF for 99.47% of papers and 99.54% of versions reported
with a non-zero submission size. It is a one-off snapshot; coverage gaps… See the full description on the dataset page: https://huggingface.co/datasets/secemp9/arxiv-complete.security-auditsA collection of agent traces generated with Swival (not Claude Code, despite what the HF interface currently shows), an agent designed for open-source models.
These traces focus on security audits of opensource software.
Sharing traces with Swival
Swival can export full conversation traces with --trace-dir, which writes one <session_id>.jsonl file per session:
swival "Fix the login bug" --trace-dir traces/
Those JSONL files use Swival's Claude Code compatible trace export, and… See the full description on the dataset page: https://huggingface.co/datasets/jedisct1/security-audits.SEC
SEC Annual Reports (Form 10-K) 1993-2024
Dataset Overview
This dataset comprises SEC annual reports (Form 10-K) for the years 1993 to 2024, providing comprehensive coverage of publicly traded companies' financial and business information. The reports are stored in Parquet format, ensuring efficient storage and quick access. This dataset was meticulously compiled using the EDGAR-Crawler toolkit, which facilitates the extraction and processing of SEC filings from the EDGAR… See the full description on the dataset page: https://huggingface.co/datasets/PleIAs/SEC.GenIaC-SecBench
GenIaC-SecBench
A benchmark for evaluating the security of LLM-generated Infrastructure-as-Code
(IaC) against a size-matched human baseline.
Paper: Compared to What? A Human-Anchored Security Benchmark for LLM-Generated
Infrastructure-as-Code (arXiv:2608.28021)
Code: https://github.com/AnimeshShaw/GenIaC-SecBench
Why this dataset exists
Prior evaluations of generated IaC report vulnerability counts for models
only. Stating that a model averages eight findings per… See the full description on the dataset page: https://huggingface.co/datasets/AnimeshShaw/GenIaC-SecBench.b3-agent-security-benchmark-weak[paper] [blogpost] [game]
b3 AI Security Benchmark: Breaking Agent Backbones
Highly contextalized prompt injections crowd-sourced during the Gandalf Agent Breaker Challenge.
This is a low-quality version of the data behind Breaking Agent Backbones: Evaluating the Security
of Backbone LLMs in AI Agents.
The high quality dataset was used to evaluate the security of more than 30 LLMs.
Dataset Summary
Purpose: This dataset contains crowdsourced adversarial attacks… See the full description on the dataset page: https://huggingface.co/datasets/Lakera/b3-agent-security-benchmark-weak.DecodingTrust
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Overview
This repo contains the source code of DecodingTrust. This research endeavor is designed to help researchers better understand the capabilities, limitations, and potential risks associated with deploying these state-of-the-art Large Language Models (LLMs). See our paper for details.
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Boxin Wang, Weixin Chen, Hengzhi… See the full description on the dataset page: https://huggingface.co/datasets/AI-Secure/DecodingTrust.Omni-Frontier-Distillation-SFT-Cyber-security-Coding-dataset-collection-v2
🧬 Omni-Frontier Collection
Cybersecurity · Coding · Math · Science · RSI Reasoning — one unified SFT package
A unified, deduplicated, fully-browsable distillation & SFT corpus — every row real, every row visible.
📖 Jump to
What's inside · 🔁 Aggregation audit · 🛡 Cybersecurity · 💻 Coding · 🏭 Distillation deep-dive · 🔁 RSI · 🧮 Math/Science/More · 🎓 Training guide · 🔎 Browsing · 🧹 Quality · 🗺 Roadmap · 📄 License… See the full description on the dataset page: https://huggingface.co/datasets/Manusagents/Omni-Frontier-Distillation-SFT-Cyber-security-Coding-dataset-collection-v2.secure-contain-protect
Secure, Contain, Protect
A structured snapshot of the English SCP Foundation Wiki — every content page (SCPs, tales, hubs, GOI formats, essays and art) created between 2008 and 2026, harvested from the Crom GraphQL API. 19,438 pages by 3,479 credited authors, each with its raw Wikidot source text, rating, vote and comment counts, tags, authorship, alternate titles, and per-credit attributions (the credited author(s), distinct from whoever posted the page).
User/author pages… See the full description on the dataset page: https://huggingface.co/datasets/ozefe/secure-contain-protect.fuzz-agent-trajectories
Agentic Web-API Fuzzing Trajectories
Verified multi-turn tool-calling trajectories of an LLM agent fuzzing a deliberately-vulnerable
web API for eight vulnerability classes (sqli, bola, bfla, mass-assignment, command-injection,
excessive-data-exposure, error-500, verbose-errors).
Each trajectory was produced by a strong teacher running the think -> act (http_request) ->
observe loop, and kept only if its reports were verified against ground truth (precise +
covering enough… See the full description on the dataset page: https://huggingface.co/datasets/SecureAI-SE/fuzz-agent-trajectories.german-public-sector-c_dbr
Dataset Card for public_sector_c_dbr_QA
Dataset Summary
public_sector_c_dbr_QA is a German-language QA dataset for public-sector and legal-administrative content. The dataset includes question, answer, source context, and LLM-as-a-judge quality metadata.
Dataset Files
Main file: public_sector_c_dbr_QA.jsonl
Base evaluated file: c_dbr_evaluated_top_20_percent.jsonl
Suggested split mapping: train only
Record Counts… See the full description on the dataset page: https://huggingface.co/datasets/Anirbanbhk/german-public-sector-c_dbr.ai-agent-security-sft-dpo
AI Agent Security — SFT + DPO
Fine-tuning data for teaching an AI agent to protect its confidential configuration without
becoming uselessly over-cautious. Built for
thesreedath/gemma-2-2b-qa-sft and
derived from
Dhanjo/ai-agent-security-dataset.
Why the helpfulness axis exists
leakage_score in the source dataset is one-sided: a model that refuses every request
scores a perfect 0.0. An existing fine-tune reported 0.0114 mean leakage (down from 0.4611
baseline)… See the full description on the dataset page: https://huggingface.co/datasets/sumitguha13/ai-agent-security-sft-dpo.ai-agent-security-dataset
AI Agent Security and System Prompt Leakage Dataset
Dataset Overview
This dataset was created for research on AI agent security, with a specific focus on system prompt leakage, jailbreak resistance, and security-aligned fine-tuning.
The dataset evaluates how often AI agents reveal confidential information embedded inside their system prompts when exposed to adversarial prompts. It also compares the behavior of a baseline language model against a model fine-tuned using… See the full description on the dataset page: https://huggingface.co/datasets/Dhanjo/ai-agent-security-dataset.quantum-cryptography-and-post-quantum-security
Neura Parse — Quantum Cryptography & Post-Quantum Security
A deep vertical on cryptography that uses quantum mechanics and on classical cryptography built to resist quantum attack. It covers quantum key distribution (BB84, B92, six-state, SARG04, E91, BBM92, decoy-state, MDI-QKD, TF-QKD, CV-QKD), device-independent protocols, composable and finite-key security proofs, quantum hacking with countermeasures, classical post-processing (reconciliation, privacy amplification… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-cryptography-and-post-quantum-security.code-securite-interieure
Code de la sécurité intérieure, non-instruct (2025-07-11)
The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects.
Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source… See the full description on the dataset page: https://huggingface.co/datasets/louisbrulenaudet/code-securite-interieure.code-securite-sociale
Code de la sécurité sociale, non-instruct (2025-07-11)
The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects.
Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source… See the full description on the dataset page: https://huggingface.co/datasets/louisbrulenaudet/code-securite-sociale.information-security-policies-qa-distiset
Dataset Card for information-security-policies-qa-distiset
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/daqc/information-security-policies-qa-distiset/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info… See the full description on the dataset page: https://huggingface.co/datasets/davidquicast/information-security-policies-qa-distiset.cyber_security
Digital Literacy & Cybersecurity Nepali SFT Dataset
Dataset Overview
This dataset is a Nepali-language Supervised Fine-Tuning (SFT) dataset focused on digital literacy and cybersecurity.
The dataset contains 1,000 valid JSONL records designed for instruction-following tasks. Each record contains a human instruction and a corresponding GPT-generated response.
Dataset Statistics
Property
Value
Total records
1,000
Valid JSONL rows
1,000… See the full description on the dataset page: https://huggingface.co/datasets/Somtharu181coder/cyber_security.secret-chat
secret-chat — 研究增強型 SFT 資料集（台灣高中社會科）
本資料集為合成 SFT 訓練資料，每筆樣本包含：
一道台灣高中社會科選擇題（題幹 + A/B/C/D，少數含 E）
助手的 reasoning_content（內心推理思考，正體中文）
最終 single-letter 答案
合成流程：
題目來源：lianghsun/aiec-api（660 筆 Langfuse trace，已由 Claude Opus 4.6
人工核校 553 筆有效答案）。
推理生成：呼叫 MiniMax-M2.5 並綁定
web_search function calling，後端為自架 SearXNG（search.lianghsun.dev）。
模型在「不揭露答案來源」的前提下，以正確答案為查證目標，自行查詢真實
資料、整合並寫出第一人稱推理。
譯寫成正體中文：原始推理（簡中或混語）由 ACE-2-2602 翻譯成正體中文，
遵循台灣慣用語與全形標點。
答案對齊：answer 欄位採用 Claude Opus 4.6 已核校之… See the full description on the dataset page: https://huggingface.co/datasets/lianghsun/secret-chat.