datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
secopxsecops-es-benchmark
secops-es-benchmark
An open benchmark for AI agents that investigate breaches in Elasticsearch. Real,
labeled attack telemetry in Elasticsearch (ECS) plus a scored task suite — so you can measure
how well an agent, or any LLM, does the job of a SOC analyst: follow one alert, pivot across
data sources with ES|QL, reconstruct the intrusion, and recommend a proportionate response.
DOI (cite this): 10.5281/zenodo.21770551
Code, runner, and full docs:… See the full description on the dataset page: https://huggingface.co/datasets/TocharianOU/secops-es-benchmark.omnimcp_cloud_secops_village_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_cloud_secops_village_teaser.Qwen3.8-27B-Thinking-SecOPD-trainset
Qwen3.6-27B-Thinking SecOPD Trainset
Dataset summary
This public dataset contains 19,155 complete, model-specific preference records
for offline adversarial training against indirect prompt injection. The corpus
starts from the 19,157-record
Sizhe-Chen/Qwen3.6-27B-Instruct-SecPO-trainset
release. Its six non-label lineage fields are retained, while the attacked
prompts are rendered for thinking-on generation and the chosen and rejected
labels are regenerated… See the full description on the dataset page: https://huggingface.co/datasets/Sizhe-Chen/Qwen3.8-27B-Thinking-SecOPD-trainset.