| --- |
| license: other |
| license_name: research-only-non-commercial |
| license_link: LICENSE |
| language: |
| - en |
| task_categories: |
| - question-answering |
| - text-generation |
| tags: |
| - cybersecurity |
| - security |
| - instruction-tuning |
| - chat |
| - cve |
| - vulnerability |
| - mitre-attack |
| - red-team |
| - blue-team |
| - incident-response |
| size_categories: |
| - 1M<n<10M |
| configs: |
| - config_name: balanced |
| default: true |
| data_files: |
| - split: train |
| path: data/balanced/train-*.parquet |
| - split: validation |
| path: data/balanced/validation-*.parquet |
| dataset_info: |
| features: |
| - name: messages |
| list: |
| - name: role |
| dtype: string |
| - name: content |
| dtype: string |
| - name: metadata |
| struct: |
| - name: source |
| dtype: string |
| - name: domain |
| dtype: string |
| - name: task_type |
| dtype: string |
| - name: n_turns |
| dtype: int64 |
| - name: est_tokens |
| dtype: int64 |
| - name: has_system |
| dtype: bool |
| - name: cve_id |
| dtype: string |
| - name: severity |
| dtype: string |
| - name: published |
| dtype: string |
| - name: doc |
| dtype: string |
| - name: language |
| dtype: string |
| splits: |
| - name: train |
| num_examples: 302034 |
| - name: validation |
| num_examples: 3050 |
| - config_name: full |
| data_files: |
| - split: train |
| path: data/full/train-*.parquet |
| - split: validation |
| path: data/full/validation-*.parquet |
| dataset_info: |
| features: |
| - name: messages |
| list: |
| - name: role |
| dtype: string |
| - name: content |
| dtype: string |
| - name: metadata |
| struct: |
| - name: source |
| dtype: string |
| - name: domain |
| dtype: string |
| - name: task_type |
| dtype: string |
| - name: n_turns |
| dtype: int64 |
| - name: est_tokens |
| dtype: int64 |
| - name: has_system |
| dtype: bool |
| - name: cve_id |
| dtype: string |
| - name: severity |
| dtype: string |
| - name: published |
| dtype: string |
| - name: doc |
| dtype: string |
| - name: language |
| dtype: string |
| splits: |
| - name: train |
| num_examples: 1877209 |
| - name: validation |
| num_examples: 18961 |
|
|
| --- |
| |
| # Cybersecurity Instruction-Tuning Dataset |
|
|
| A large, cleaned, multi-domain cybersecurity chat dataset for LLM finetuning, |
| built from **198 distinct sources** spanning offensive security, blue-team |
| operations, vulnerability intelligence, cloud/AWS security, malware analysis, |
| digital forensics, and more. Every record is normalized to the standard |
| `messages` chat format and deduplicated at both file and record level. |
|
|
| > ⚠️ **Research use only.** This dataset is provided exclusively for |
| > non-commercial research. Commercial use — including commercial use of models |
| > trained on it — is **not permitted**; see `LICENSE`. |
|
|
| ## Quick start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # recommended (domain-balanced) split |
| ds = load_dataset("oi-uae/cyber-security") # balanced config |
| ds = load_dataset("oi-uae/cyber-security", "full") # everything, 1.9M rows |
| ``` |
|
|
| ```json |
| {"messages": [ |
| {"role": "system", "content": "..."}, |
| {"role": "user", "content": "..."}, |
| {"role": "assistant", "content": "..."} |
| ], |
| "metadata": {"source": "cybersec_master.jsonl", "domain": "vulnerability-intelligence", |
| "task_type": "cve_qa", "n_turns": 4, "est_tokens": 321, |
| "has_system": false, "cve_id": "CVE-2023-5877", |
| "severity": "Critical", "published": "2024-01-01", |
| "doc": null, "language": null}} |
| ``` |
|
|
| Data files are Apache Parquet with a fixed schema; each row is one chat record. |
| The `metadata` object enables filtering and analysis |
| (`ds.filter(...)`, `ds.select(...)`); trainers ignore it, or drop it with |
| `ds.remove_columns(["metadata"])`. |
|
|
| ### Metadata fields |
|
|
| | field | description | |
| |---|---| |
| | `source` | provenance file the record was built from | |
| | `domain` | topical domain (e.g. `red-team`, `blue-team`, `cloud-security`, `vulnerability-intelligence`) | |
| | `task_type` | training-signal type: `cve_qa`, `causal_reasoning`, `community_qa`, `doc_qa`, `vuln_fix`, `classification`, `reference_qa`, `playbook_qa`, `scenario_qa`, `incident_qa`, `tutoring`, `instruction` | |
| | `n_turns` | number of turns in the conversation | |
| | `est_tokens` | approximate token count (characters / 4) | |
| | `has_system` | whether a system prompt is present | |
| | `cve_id`, `severity`, `published` | CVE records only | |
| | `doc` | AWS documentation file (AWS records only) | |
| | `language` | programming language of the fixed file (ARVO records only) | |
|
|
|  |
|
|
| ## Splits |
|
|
| | config | train | validation | notes | |
| |---|---:|---:|---| |
| | **balanced** (default) | 302,034 | 3,050 | per-source caps applied; recommended for finetuning | |
| | **full** | 1,877,209 | 18,961 | everything that survived cleaning (~649M est. tokens) | |
|
|
| ## What's inside |
|
|
| | domain | sources include | |
| |---|---| |
| | Vulnerability intelligence | 1.8M multi-turn NVD CVE Q&A (`cybersec_master`), CVE instruction sets | |
| | Causal security reasoning | 100K "what-if" attack/defense scenarios (220426-CyberSec-Dataset) | |
| | Community Q&A | 15.7K ShareGPT cybersecurity conversations | |
| | Cloud security | 69K AWS documentation reading-comprehension pairs (743 PDFs) | |
| | Vulnerability fixing | 814 ARVO "find & fix" pairs from real OSS repos (C/C++/Python) | |
| | Blue team | incident-response playbooks, hardening guides, threat-hunting procedures | |
| | Threat intelligence | MITRE ATT&CK techniques, groups, tools, malware, RaaS, APT campaigns | |
| | Offensive security | red-team tooling, phishing/social-engineering, VAPT methodology, OSINT | |
| | Other | ransomware, forensics, container/K8s, IoT/Thread, blockchain/DeFi, compliance, news classification | |
|
|
|  |
|
|
| ## How the data was cleaned |
|
|
| Starting from a 95 GB folder with every file duplicated 2–3× and **12 different |
| source schemas**: |
|
|
| 1. **File-level dedup** — every file existed in a domain folder, a flat |
| `assets/` mirror, and explicit `__dup1` copies; content-hashed, one kept |
| (35% of raw disk was redundant copies). |
| 2. **Schema normalization** — Alpaca, ShareGPT, `messages`, top-level |
| system/user/assistant, nested blue-team playbooks and MITRE tables were all |
| mapped to the single chat format above. |
| 3. **Text repair** — literal `\n` escape damage (text stored double-escaped, |
| present in the 220426 file *and* mixed into the master file) was detected and |
| repaired; broken surrogate pairs fixed. |
| 4. **Quality filters** — non-empty turns, assistant ≥ 20 chars, conversation |
| ≤ 60k chars, strict `system? (user/assistant)+` role alternation. |
| 5. **Record-level dedup** — 12% of records were exact duplicates. |
| 6. **Domain balancing** — the `balanced` config caps dominant templated |
| sources (CVE master 1.7M → 200k, 220426 → 60k, AWS chunks → 20k, ShareGPT → |
| 15k) so one source cannot dominate gradient updates. |
| 7. **Deterministic split** — seeded shuffle, 99/1 train/validation, verified |
| zero overlap between splits. |
|
|
|  |
|
|
| **Cleaning effect:** 2,094,702 raw records → 1,896,170 clean records |
| (54,076 unconvertible schema, 295 failed quality, 144,161 exact duplicates removed). |
|
|
|  |
|
|
| ## Data analysis highlights |
|
|
|  |
|
|
| The largest source is `cybersec_master`: one multi-turn conversation per CVE |
| from the NVD feed (severity, CVSS vector, CWE, affected products, references, |
| follow-up questions). Medium and High severity dominate. |
|
|
|  |
|
|
| 78 GB of the raw folder were zipped ARVO repository snapshot pairs |
| (vulnerable + fixed). These were streamed, diffed, and converted into 814 |
| "review this vulnerable code" records — mostly C/C++ projects, with the |
| vulnerable function in the user turn and the description + patch in the |
| assistant turn. |
|
|
|  |
|
|
| Median conversation is ~300 tokens; p95 ≈ 1645; max ≈ 13949. |
| ~2.4 turns per conversation on average, with a small share of multi-turn dialogues. |
|
|
| ## Data sources |
|
|
| This dataset aggregates and reprocesses material from the following upstream |
| origins. Record counts refer to the `full` config after cleaning. |
|
|
| | upstream source | material | records | notes | |
| |---|---|---:|---| |
| | [Vyber07/cyber-security](https://huggingface.co/datasets/Vyber07/cyber-security) (Apache-2.0) | the upstream aggregate this collection derives from: ~35 domain-organized instruction sets, red/blue-team scenarios, MITRE tables, ShareGPT-style conversations, the 220426 causal-reasoning set | bulk of the dataset | local folder was cleaned, deduplicated and re-normalized for this repo | |
| | [NVD / NIST](https://nvd.nist.gov) | every CVE feed record turned into a multi-turn Q&A (severity, CVSS vector, CWE, affected products, references) | 1.72 M | the `cve_id`, `severity`, `published` metadata fields come from here | |
| | [MITRE ATT&CK](https://attack.mitre.org) | tactics, techniques, groups, tools and software reference tables | ~2.2 K | converted to question/answer reference records | |
| | [ARVO](https://github.com/n132/ARVO) — Atlas of Reproducible Vulnerabilities for Open-Source Software (OSS-Fuzz–derived) | 867 vulnerable/fixed open-source repo snapshot pairs (78 GB of tarballs) | 814 | streamed and diffed locally into "find & fix the vulnerability" records; mostly C/C++ projects | |
| | [AWS documentation](https://docs.aws.amazon.com) | 743 official PDFs (SageMaker, RDS, Control Tower, App2Container, …) | 53.5 K | excerpts converted to reading-comprehension QA; `doc` metadata field holds the source PDF | |
| | Security news outlets | labeled headlines from 564 outlets (The Register, TechRadar, BleepingComputer, …) | 3 K | used for threat-topic classification records | |
| | LLM-generated instruction sets | synthetic tutoring and domain instruction data (authored with LLM assistance, incl. a Claude-generated CS tutoring set) | ~2 K | manifests in the source mark these as synthetic educational content | |
|
|
| Reference knowledge bases cited by the source material include HackerOne and |
| BugCrowd disclosure reports, the OWASP Testing Guide and OWASP LLM Top 10, |
| PortSwigger Web Security Academy and Research, HackTheBox, TryHackMe, VulnHub, |
| SANS, NIST publications, MITRE ATLAS/D3FEND, PTES and OSSTMM methodologies. |
|
|
|  |
|
|
| ## Training recommendations & best practices |
|
|
| ### 1. Pick the right config for your compute |
|
|
| | setup | config | size | suggested epochs | |
| |---|---|---|---| |
| | QLoRA / LoRA, single 16–24 GB GPU, 7–8 B model | `balanced` | 302 k records / ~173 M tokens | 1–2 | |
| | Full-parameter SFT, multi-GPU, 7–70 B | `full` | 1.88 M records / ~650 M tokens | 1 | |
| | Domain-adaptive continued pretraining | `full` (streaming) | same | 1+ with packing | |
|
|
| Start with `balanced`. The `full` config is dominated by 1.7 M templated CVE |
| Q&A records; more epochs on it mostly teach NVD boilerplate phrasing rather |
| than deeper security reasoning. |
|
|
| ### 2. Preprocessing |
|
|
| - The `messages` field is the standard chat format — apply your model's chat |
| template directly (`tokenizer.apply_chat_template(example["messages"]...)`). |
| - **Mask the loss to assistant turns only** (completion-only loss). Every |
| trainer supports this (`train_on_prompt=False` in Axolotl, |
| `assistant_only_loss` in LLaMA-Factory, `SFTTrainer` does it automatically for |
| `messages` columns). |
| - **Sequence length:** median conversation ≈ 300 tokens, p95 ≈ 1.6 k, p99 ≈ |
| 2.6 k, max ≈ 14.5 k. `max_seq_len=4096` covers 99.6% of records; use 8192 to |
| cover 99.9%. Enable sequence packing for throughput. |
|
|
| ### 3. Build your own domain mix with `metadata` |
|
|
| Even the `balanced` split is 66% CVE Q&A by design. If your use case needs a |
| different emphasis, filter with the metadata before training: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("oi-uae/cyber-security", split="train") |
| |
| # example: defensive-focused mix, dropping the weakest signals |
| keep = {"cve_qa", "causal_reasoning", "community_qa", "playbook_qa", |
| "vuln_fix", "reference_qa", "scenario_qa", "incident_qa"} |
| mix = ds.filter(lambda r: r["metadata"]["task_type"] in keep) |
| mix = mix.shuffle(seed=42).select(range(100_000)) |
| |
| # example: only critical/high CVEs |
| crit = ds.filter(lambda r: r["metadata"]["severity"] in ("Critical", "High")) |
| ``` |
|
|
| Quality notes: `doc_qa` (AWS excerpts) and `tutoring` (general CS) are the |
| lowest-signal task types — reasonable first candidates for exclusion. |
| `vuln_fix` (real code patches) is high-signal but small (814 records); consider |
| upsampling it 2–3× in code-security mixes. |
|
|
| ### 4. Hyperparameter starting points |
|
|
| | method | learning rate | schedule | notes | |
| |---|---|---|---| |
| | QLoRA 7–8 B | 1e-4 – 2e-4 | cosine, 3% warmup | rank 16–64, alpha = 2×rank, target all linear layers | |
| | LoRA | 1e-4 | cosine | same targets as above | |
| | Full SFT | 1e-5 – 2e-5 | cosine, 3% warmup | gradient checkpointing; watch for loss collapse to CVE template style | |
| | Continued pretraining | 5e-5 – 1e-5 | constant + decay | pack to 4096, include doc_qa | |
| |
| Batch via gradient accumulation to an effective batch of 64–256 conversations. |
| |
| ### 5. Evaluation |
| |
| - The `validation` splits are held out with **zero train overlap** (verified by |
| content hash); use `validation` of the same config you train on. |
| - Log loss per `task_type` / `domain` (the metadata makes this a one-liner) — |
| CVE loss will fall fast; watch `playbook_qa` and `incident_qa` for real |
| security-reasoning gains. |
| - Add a held-out safety probe: the model will happily reproduce offensive |
| content from this data; test refusal behavior on misuse prompts before any |
| deployment. |
|
|
| ### 6. Deployment safety |
|
|
| This corpus teaches exploit techniques, phishing tradecraft and malware |
| analysis alongside defensive material. Fine-tuned models should ship behind a |
| guardrail system prompt, output filtering, and access controls — and be |
| evaluated for your threat model. Do not expose such a model as an open public |
| assistant. |
|
|
| ## Provenance & licensing |
|
|
| > ⚠️ **Research use only — commercial use is not permitted.** |
| > This dataset is made available exclusively for academic research, |
| > personal study, and other non-commercial purposes. Using it — or models |
| > trained on it — in any commercial product, service, or business context is |
| > **not allowed** without prior written permission from the dataset maintainers. |
| > See the `LICENSE` file for the full terms. |
|
|
| This is an **aggregate of community cybersecurity datasets** (NVD/NIST CVE |
| data, MITRE ATT&CK, ARVO, OWASP-derived material, AWS documentation excerpts, |
| forum/ShareGPT conversations, and synthetically generated instruction sets). |
| The aggregate is distributed for research use only; upstream sources retain |
| their respective rights — AWS documentation excerpts and some reference |
| material are © their respective owners. |
|
|
| ## Content warning |
|
|
| This dataset contains professional cybersecurity content including |
| offensive-security material (red-team techniques, phishing scenarios, malware |
| analysis, exploit descriptions). It is intended for defensive research, |
| security training, and professional education. Use in compliance with your |
| organization's policies and applicable law. |
|
|
