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.SEC-EDGARDatamule, Teraflop AI, and Eventual collaborated to release the SEC-EDGAR dataset.
The dataset contains 590 gbs of data, spanning 8 million samples and 43 billion tokens from all major filings in the SEC EDGAR database.
The bulk data was collected using datamule-python library and the official datamule api created by John Friedman. The datamule Python library is a package for collecting, manipulating, and processing the SEC Edgar data at scale. Datamule provides a simple open-source api… See the full description on the dataset page: https://huggingface.co/datasets/TeraflopAI/SEC-EDGAR.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.cyber-security
Cybersecurity AI Knowledge Base — PhD-Level Dataset
Overview
This is the most comprehensive cybersecurity knowledge base ever assembled for AI training. It covers all domains of cybersecurity at PhD-level depth — from offensive red teaming and bug bounty exploitation to defensive SOC operations, digital forensics, and cutting-edge AI/LLM security.
Size: 16 GB | Files: 507 | Domains: 30+ | Sources: 15+ platforms
Purpose
Train the world's most… See the full description on the dataset page: https://huggingface.co/datasets/Vyber07/cyber-security.SEC-EDGARDatamule, Teraflop AI, and Eventual collaborated to release the SEC-EDGAR dataset.
The dataset contains 590 gbs of data, spanning 8 million samples and 43 billion tokens from all major filings in the SEC EDGAR database.
The bulk data was collected using datamule-python library and the official datamule api created by John Friedman. The datamule Python library is a package for collecting, manipulating, and processing the SEC Edgar data at scale. Datamule provides a simple open-source api… See the full description on the dataset page: https://huggingface.co/datasets/kapilrao/SEC-EDGAR.sec-10k-markdown-uncompressed
📄 SEC 10-K Full Uncompressed Markdown Filings (12.3k Documents)
Dataset Summary
This dataset contains 12,361 full-length, uncompressed SEC Form 10-K annual reports converted from EDGAR HTML to clean Markdown format across 1,379 companies (spanning 2004 to 2025, core 2014–2025).
The dataset is organized as uncompressed Markdown files structured by company ticker subdirectories (AAPL/10-K_2024.md, NVDA/10-K_2024.md, etc.), complete with company metadata manifests… See the full description on the dataset page: https://huggingface.co/datasets/astr010/sec-10k-markdown-uncompressed.DTap-Bench-Agent-Trajectories
DecodingTrust-Agent Platform
A Controllable and Interactive Red-Teaming Platform for AI Agents.
This is the full collection of the agent trajectories produced from evaluating the DTap-Bench from DecodingTrust-Agent Platform (DTAP),
spanning 14 real-world domains and 50+ simulation environments that replicate widely-used
systems such as Google Workspace, PayPal, Slack, Salesforce, Snowflake, and Databricks. Each task
ships the configuration the evaluator needs to spin up the… See the full description on the dataset page: https://huggingface.co/datasets/AI-Secure/DTap-Bench-Agent-Trajectories.sec-contracts-financial-extraction-instructions
S&P 500 SEC Financial Extraction Instructions
Dataset Summary
7,683 instruction-tuning examples for training LLMs to extract structured financial data from SEC filings. Covers two filing types across S&P 500 companies:
Split
Examples
Filing Type
Description
train
3,430
Exhibit 10 + DEF 14A
Positive examples with validated outputs
corrective
4,253
Exhibit 10 + DEF 14A
Corrective, rescued, and negative examples
Exhibit 10 — Material Contracts (2… See the full description on the dataset page: https://huggingface.co/datasets/TheTokenFactory/sec-contracts-financial-extraction-instructions.SEC-EDGARDatamule, Teraflop AI, and Eventual collaborated to release the SEC-EDGAR dataset.
The dataset contains 590 gbs of data, spanning 8 million samples and 43 billion tokens from all major filings in the SEC EDGAR database.
The bulk data was collected using datamule-python library and the official datamule api created by John Friedman. The datamule Python library is a package for collecting, manipulating, and processing the SEC Edgar data at scale. Datamule provides a simple open-source api… See the full description on the dataset page: https://huggingface.co/datasets/Jeremydh911/SEC-EDGAR.DecodingTrust-Agent-Platform
DecodingTrust-Agent Platform
A Controllable and Interactive Red-Teaming Platform for AI Agents.
This is the per-task dataset for the DecodingTrust-Agent Platform (DTAP),
spanning 14 real-world domains and 50+ simulation environments that replicate widely-used
systems such as Google Workspace, PayPal, Slack, Salesforce, Snowflake, and Databricks. Each task
ships the configuration the evaluator needs to spin up the sandbox, run an agent, and verify the
outcome — config.yaml (task… See the full description on the dataset page: https://huggingface.co/datasets/AI-Secure/DecodingTrust-Agent-Platform.securecode-web
SecureCode Web: Traditional Web & Application Security Dataset
Production-grade web security vulnerability dataset with complete incident grounding, 4-turn conversational structure, and comprehensive operational guidance
Paper | GitHub | Dataset | Model Collection | Blog Post
What's new in v2.6
v2.6 restores proper Express.js coverage for the topics whose examples were removed in v2.5.1 (they had
shared one reused answer). 29 new, genuinely distinct Express.js… See the full description on the dataset page: https://huggingface.co/datasets/scthornton/securecode-web.SEC-EDGARDatamule, Teraflop AI, and Eventual collaborated to release the SEC-EDGAR dataset.
The dataset contains 590 gbs of data, spanning 8 million samples and 43 billion tokens from all major filings in the SEC EDGAR database.
The bulk data was collected using datamule-python library and the official datamule api created by John Friedman. The datamule Python library is a package for collecting, manipulating, and processing the SEC Edgar data at scale. Datamule provides a simple open-source api… See the full description on the dataset page: https://huggingface.co/datasets/baridhi/SEC-EDGAR.White-Hat-Security-Agent-Prompts-600K
White Hat Security Agent Prompts 600K
Overview
The White-Hat-Security-Agent-Prompts-600K dataset is a practitioner-perspective security prompts corpus of 596,295 richly contextualized queries, designed to represent how real-world defensive security professionals communicate, interrogate, and reason through active threat scenarios.
Where most security datasets catalogue CVEs, malware signatures, or CTF write-ups, this collection teaches models to operate from inside the… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/White-Hat-Security-Agent-Prompts-600K.sec-material-contracts
Material Contracts (Exhibit 10) from SEC/EDGAR
Because sometimes you need 1,141,632 examples of corporate legalese to train your next model ☕
Dataset Summary
Picture this: 1,141,632 material contracts (Exhibit 10) painstakingly collected from sec.gov's EDGAR database. We're talking about legal agreements spanning from 1994 to 2025 Q1, sourced from 10-K, 10-Q, and 8-K filings. Think of Exhibit 10 as the treasure trove where companies hide their most important legal… See the full description on the dataset page: https://huggingface.co/datasets/chenghao/sec-material-contracts.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.SecureVibeBench
SecureVibeBench: First Secure Vibe Coding Benchmark
SecureVibeBench is a benchmark consisting of 105 C/C++ secure coding tasks sourced from 41 projects in OSS-Fuzz for code agents. It is designed to evaluate secure vibe coding by reconstructing real-world scenarios where human developers introduced vulnerabilities.
Paper: SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios
Repository: iCSawyer/SecureVibeBench
Venue:… See the full description on the dataset page: https://huggingface.co/datasets/iCSawyer/SecureVibeBench.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.SEC-EDGARDatamule, Teraflop AI, and Eventual collaborated to release the SEC-EDGAR dataset.
The dataset contains 590 gbs of data, spanning 8 million samples and 43 billion tokens from all major filings in the SEC EDGAR database.
The bulk data was collected using datamule-python library and the official datamule api created by John Friedman. The datamule Python library is a package for collecting, manipulating, and processing the SEC Edgar data at scale. Datamule provides a simple open-source api… See the full description on the dataset page: https://huggingface.co/datasets/adityaag2k/SEC-EDGAR.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.terraform_sec
Terraform Security Dataset
A comprehensive dataset of 62,406 Terraform projects analyzed for security vulnerabilities using tfsec. This dataset is designed for training Large Language Models (LLMs) to understand, identify, and fix security issues in Terraform infrastructure-as-code.
📊 Dataset Overview
Total Examples: 62,406 Terraform projects
Secure Projects: 43,575 (69.8%)
Insecure Projects: 18,831 (30.2%)
Format: JSONL (JSON Lines)
Task: Security analysis and… See the full description on the dataset page: https://huggingface.co/datasets/galcan/terraform_sec.SecBench
SecBench: A Comprehensive Multi-Dimensional Benchmarking Dataset for LLMs in Cybersecurity
中文README
Evaluating Large Language Models (LLMs) is crucial for understanding their capabilities and limitations across various applications, including natural language processing and code generation. Existing benchmarks like MMLU, C-Eval, and HumanEval assess general LLM performance but lack focus on specific expert domains such as cybersecurity. Previous attempts to create cybersecurity… See the full description on the dataset page: https://huggingface.co/datasets/secbench-hf/SecBench.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.omnimcp_cybersecurity_secops_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_cybersecurity_secops_teaser.secopxsecret-loyalty-competition-data
Secret-loyalty organisms — training banks and eval batteries
The data behind KKing23/secret-loyalty-competition-organisms.
Code and full result trail: github.com/kaustubhkislay/secret-loyalty-competition.
Why this exists separately from the adapters. The adapters are reproducible from these
banks for the price of GPU time. These banks are not reproducible — they were written by
an LLM generator, so regenerating gives different data and every published number becomes… See the full description on the dataset page: https://huggingface.co/datasets/KKing23/secret-loyalty-competition-data.stegoattack-advbench50
StegoAttack AdvBench-50
Steganographic jailbreak data generated using the StegoAttack pipeline from the paper "Hiding in Plain Sight: A Steganographic Approach to Stealthy LLM Jailbreaks" (Geng et al., 2025).
For experiment results and analysis, see experiment.md.
What is StegoAttack?
StegoAttack is a jailbreak method that uses steganography to hide harmful queries inside benign-looking text. It embeds each word of a harmful query at a fixed position (e.g. the 2nd… See the full description on the dataset page: https://huggingface.co/datasets/heron-ai-security/stegoattack-advbench50.securecode
SecureCode: Comprehensive Security Training Dataset for AI Coding Assistants
The largest open security training dataset for AI coding assistants, covering both traditional web security and AI/ML security
Overview
SecureCode combines 2,372 security-focused training examples into a single, unified dataset with HuggingFace configs for flexible loading. Every example provides vulnerable code, explains why it's dangerous, demonstrates a secure alternative, and… See the full description on the dataset page: https://huggingface.co/datasets/scthornton/securecode.SecRespond
SecRespond
💻 GitHub |
🤖 ModelScope |
📄 Paper
Introduction
SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response evaluates whether an AI agent can investigate a compromised host after an attack has already succeeded.
For each cyber range, the responder receives a frozen forensic disk snapshot together with synthetic host-security-product outputs, then produces an evidence-backed incident… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-NLP/SecRespond.SecureCodePairs
Dataset Summary
Field
Value
Version
1.2.0
License
MIT
Total code examples
470
LLM security trajectories
30
Languages (15)
Python, Java, JavaScript, TypeScript, Go, PHP, C#, Kotlin, Swift, Rust, Ruby, C, C++, Scala, YAML (Kubernetes)
Frameworks
Flask, Django, FastAPI, Spring Boot, Express, NestJS, Next.js, Laravel, ASP.NET Core, Gin, Android, iOS, Actix, Rails, Qt, Play, gRPC, GraphQL, Kubernetes
New in v1.2.0
+260 records (deep Python/Java packs… See the full description on the dataset page: https://huggingface.co/datasets/ismailtasdelen/SecureCodePairs.