extract 0 0 0

ismailtasdelen/mitre-attack-techniques-qa

published by @ismailtasdelen

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
289
Last 30 days
79
Likes
1
Size
510 KB
Created on HF
2026-07-14
Age
3 months ago

Description

MITRE ATT&CK Techniques QA

A question–answer dataset covering 475 MITRE ATT&CK Enterprise techniques and sub-techniques,

designed for training and evaluating security-focused language models, RAG assistants for SOC

analysts, and red/blue/purple-team education.

Dataset Summary

Property

Value

Records

475

Language

English

Techniques (parent) covered

222 / 222 (all non-deprecated Enterprise parents)

Sub-techniques covered

253 (selection across the… See the full description on the dataset page: https://huggingface.co/datasets/ismailtasdelen/mitre-attack-techniques-qa.

Tags

task_categories:question-answeringtask_categories:text-classificationlanguage:enlicense:mitregion:usmitre-attackcybersecuritythreat-intelligencered-teamblue-teamsocsecurity

README current version from Hugging Face


license: mit
language:

  • en
    task_categories:
  • question-answering
  • text-classification
    tags:
  • mitre-attack
  • cybersecurity
  • threat-intelligence
  • red-team
  • blue-team
  • soc
  • security
    dataset_info:
    features:
    • name: id
      dtype: int64
    • name: technique_id
      dtype: string
    • name: technique_name
      dtype: string
    • name: tactic
      dtype: string
    • name: platforms
      dtype: list
    • name: difficulty
      dtype: string
    • name: question
      dtype: string
    • name: answer
      dtype: string
    • name: detection_guidance
      dtype: string
    • name: mitigation_summary
      dtype: string
    • name: example_scenario
      dtype: string
    • name: related_techniques
      dtype: list
    • name: keywords
      dtype: list
    • name: language
      dtype: string
      splits:
      train:
      num_bytes: 1400000
      num_examples: 475
      dataset_size: 1400000
      configs:
  • config_name: default
    data_files:
    • split: train
      path: data/train-00000-of-00001.parquet

MITRE ATT&CK Techniques QA

A question–answer dataset covering 475 MITRE ATT&CK Enterprise techniques and sub-techniques,
designed for training and evaluating security-focused language models, RAG assistants for SOC
analysts, and red/blue/purple-team education.

Dataset Summary

Property Value
Records 475
Language English
Techniques (parent) covered 222 / 222 (all non-deprecated Enterprise parents)
Sub-techniques covered 253 (selection across the catalog)
Tactics covered 16 (Enterprise)
Difficulty tiers Beginner / Intermediate / Advanced
Answer length 150–300 words
License MIT

Each record pairs a real ATT&CK technique ID with an instructive question, an original explanatory
answer (defensive framing), concise detection guidance, a single mitigation recommendation, a
fictional enterprise scenario, related technique IDs, and keywords.

Features / Schema

Field Type Description
id int64 Sequential record id (1–475).
technique_id string ATT&CK ID, e.g. T1595 or T1059.003.
technique_name string ATT&CK technique name.
tactic string Enterprise tactic.
platforms list[string] Affected platforms.
difficulty string Beginner / Intermediate / Advanced.
question string The QA prompt.
answer string Detailed explanatory answer (150–300 words).
detection_guidance string Concise practical detection summary.
mitigation_summary string One concise mitigation recommendation.
example_scenario string Short realistic enterprise scenario (no real names).
related_techniques list[string] 2–4 related ATT&CK IDs.
keywords list[string] 3–8 relevant keywords.
language string English.

Example Record

{
  "id": 101,
  "technique_id": "T1222",
  "technique_name": "File and Directory Permissions Modification",
  "tactic": "Defense Impairment",
  "platforms": ["ESXi", "Linux", "Windows", "macOS"],
  "difficulty": "Beginner",
  "question": "How can defenders detect and prevent attackers from modifying file and directory permissions to weaken security controls?",
  "answer": "File and Directory Permissions Modification is a defense-impairment technique in which an adversary alters the access control settings on files, folders, or special objects so that protective mechanisms can be bypassed or security tooling is prevented from operating. On Linux and macOS the attacker may use chmod, chown, or setfacl to grant world-writable access to scripts that load at boot or to audit configuration files, while on Windows icacls or PowerShell cmdlets can loosen access control lists on security logs, scheduled task files, or endpoint-agent directories. Weakening permissions lets malware persist, hide, or stop defenders from reading critical evidence. From a defensive standpoint you should treat any unexpected permission change on security-relevant paths as a high-severity event. Enable and forward audit events for permission changes, and baseline the expected permission set for protected directories so deviations are obvious. Use file-integrity monitoring to alert on ACL or ownership changes outside approved change windows. Restrict who can modify permissions through the principle of least privilege, and protect administrative accounts with strong authentication. Regularly review group memberships and inherited permissions, because attackers often exploit overly broad grants that already exist. Automated remediation that restores known-good permissions helps contain the technique quickly.",
  "detection_guidance": "Monitor audit logs for chmod/chown/setfacl (Linux/macOS) and icacls/Set-Acl (Windows) on security-relevant paths; alert on world-writable changes to protected dirs and unexpected ownership changes.",
  "mitigation_summary": "Apply least-privilege and protect security-critical paths with immutable attributes or file-integrity monitoring that restores known-good permissions.",
  "example_scenario": "A regional bank's Linux log server had its audit rules file made world-writable by a compromised service account, allowing an intruder to suppress logging of later activity until integrity monitoring flagged the change.",
  "related_techniques": ["T1687", "T1556.003", "T1222.002", "T1666"],
  "keywords": ["file permissions", "acl", "chmod", "defense impairment", "integrity monitoring", "least privilege"],
  "language": "English"
}

Usage

Load with the datasets library:

from datasets import load_dataset

ds = load_dataset("ismailtasdelen/mitre-attack-techniques-qa")
print(ds["train"][0])  # first record

Or iterate locally:

for row in ds["train"]:
    print(row["technique_id"], "-", row["tactic"], "-", row["question"])

The dataset is distributed as a single Parquet file under data/train-00000-of-00001.parquet.

Tactics Covered (16, all Enterprise)

Reconnaissance, Resource Development, Initial Access, Execution, Persistence,
Privilege Escalation, Defense Evasion, Credential Access, Discovery, Lateral Movement,
Collection, Command and Control, Exfiltration, Impact, Stealth, Defense Impairment.

Note: Stealth and Defense Impairment are tactics present in the current Enterprise ATT&CK
STIX release; the dataset reflects the tactic assignments from that source.

Intended Use

  • Fine-tuning instruction/QA models on MITRE ATT&CK.
  • Building or evaluating RAG / knowledge-base assistants for SOC analysts and threat intel.
  • Red/blue/purple team education and exercise scenarios.
  • Benchmarking technique-classification accuracy of LLMs.

Limitations

  • Answers reflect ATT&CK knowledge at time of writing; always cross-check against the
    official MITRE ATT&CK® website for current technique definitions.
  • Covers 475 technique/sub-technique IDs (of many); not exhaustive of all sub-techniques.
  • Explanations are original wording, not verbatim ATT&CK descriptions.

Safety & Content Notes

  • Content is defensive: it teaches identification, detection, and mitigation of adversary
    behavior. It does not provide weaponized step-by-step attack tooling.

License

Released under the MIT License.

Citation

@dataset{mitre_attack_techniques_qa,
  title        = {MITRE ATT&CK Techniques QA Dataset},
  author       = {ismailtasdelen},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/ismailtasdelen/mitre-attack-techniques-qa}
}

README history 5 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2026-07-15Upload README.md with huggingface_huba68c2eb7.6 KB
    Loading...
  2. 2026-07-15Upload data/train-00000-of-00001.parquet with huggingface_hub271397e7.3 KB
    Loading...
  3. 2026-07-14Upload README.md with huggingface_hub617fcd47.3 KB
    Loading...
  4. 2026-07-14Upload data/train-00000-of-00001.parquet with huggingface_hub9e708030 B
    Loading...
  5. 2026-07-14initial commit0ffa8c10 B
    Loading...

Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

Related datasets same stage · extract

Read further

Metadata

License
mit

Repository

Download the dataset

0

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("ismailtasdelen/mitre-attack-techniques-qa")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="ismailtasdelen/mitre-attack-techniques-qa", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/ismailtasdelen/mitre-attack-techniques-qa
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Open in Abliteration