datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.b3-agent-security-benchmark-weak[paper] [blogpost] [game]
b3 AI Security Benchmark: Breaking Agent Backbones
Highly contextalized prompt injections crowd-sourced during the 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.llm-fv-security-targets
LLM-FV Security Targets
This dataset contains 889 independently validated, containerized security-agent targets produced by the ucsb-mlsec/llm-fv pipelines.
Contents
GitHub Global Security Advisories: 446 targets
OSS-Fuzz: 442 targets
PoC task support: 889 targets
Exploit task support: 447 targets
Patch task support: 447 targets
Compressed bundle size: 42.41 GiB
Vulnerability classes: {'logic_bug': 450, 'memory_vulnerability': 439}
Primary languages: {'C': 118… See the full description on the dataset page: https://huggingface.co/datasets/secmlr/llm-fv-security-targets.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.cvefixes-security-autoformal-span-cache
CVEfixes Security source spans
This dataset contains exact, source-bound prose, code, and diff spans derived
from the original-data configuration of
Publicus/cvefixes-security-ir-graphrag at revision 6fd5918bed34f8851430e74a149502587a953fe2.
The underlying source is hitoshura25/cvefixes at revision
d4f5c4ea65329d9ccbb8a3b3149e5d06eda5edb2.
The extraction considered all 12,987 original rows across all three shards.
It retained 9,402 source rows and excluded 3,585. The release… See the full description on the dataset page: https://huggingface.co/datasets/Publicus/cvefixes-security-autoformal-span-cache.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.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.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.