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language:
- en
license: other
license_name: mpl-2.0-commons-clause
tags:
- security
- red-team
- purple-team
- cybersecurity
- synthetic
- sft
- mitre-attack
- agentic
- tool-use
size_categories:
- n<1K
task_categories:
- text-generation
---
# Meridian-SFT-500
500 synthetic purple team SFT examples generated from the full MITRE ATT&CK
Enterprise technique corpus across nine generation angles. Intended for
supervised fine-tuning of red team and security-focused LLMs.
## What's in it
Each entry pairs an ATT&CK-contextualized prompt with a structured security
analysis demonstrating simultaneous offensive and defensive knowledge. The
dataset spans all 11 Enterprise tactics and covers both Windows and Linux
platforms.
Generated by purple-team-gen
using a locally-hosted Qwen3.6-27B-uncensored-heretic-v2 inference server.
Thinking mode was disabled during generation. All entries passed a quality
gate: minimum 900 character response, refusal pattern detection, SIGMA YAML
syntax validation where applicable, and mandatory code block presence.
## Generation angles
Each technique is approached from one of nine angles, distributed across the
500 entries:
| Angle | What it produces |
|---|---|
| `simulate_and_detect` | Attack implementation + SIGMA rule + remediation |
| `detection_engineering` | Behavioral indicators + SIGMA + Splunk hunt query |
| `redteam_operator` | Evasion-aware code + artifact footprint + cleanup |
| `security_tooling` | Offensive tool + companion detection script |
| `sample_analysis` | Malicious code sample + technical breakdown + YARA rule |
| `attack_chain` | Multi-stage kill chain with this technique as a step |
| `incident_triage` | Alert analysis + triage script + containment procedure |
| `hardening_guide` | Configuration hardening + monitoring setup + verification |
| `threat_modeling` | Attack paths + risk assessment + control mapping |
## Format
ChatML JSONL. One record per line:
```json
{
"messages": [
{"role": "system", "content": "..."},
{"role": "user", "content": "..."},
{"role": "assistant", "content": "..."}
],
"metadata": {
"technique_id": "T1055.001",
"technique_name": "Process Injection: Dynamic-link Library Injection",
"tactics": ["defense-evasion", "privilege-escalation"],
"platforms": ["Windows"],
"angle": "simulate_and_detect",
"generated_at": "2026-07-24T...",
"source": "purple-team-gen"
}
}
```
## Loading
```python
import json
from datasets import load_dataset
# From HuggingFace
ds = load_dataset("TitleOS/Meridian-SFT-500", split="train")
# Or directly from JSONL
with open("meridian-sft-500.jsonl") as f:
records = [json.loads(line) for line in f]
messages = records[0]["messages"] # ChatML turns, ready for tokenization
technique = records[0]["metadata"]["technique_id"]
angle = records[0]["metadata"]["angle"]
```
## Statistics
| | |
|---|---|
| Total entries | 593 |
| Unique technique IDs | ~280 |
| Enterprise tactics covered | All 11 |
| Angles with SIGMA rules | `simulate_and_detect`, `detection_engineering`, `attack_chain`, `threat_modeling` |
| Entries with fenced code blocks | 500 (100%) |
| Source STIX bundle | MITRE ATT&CK Enterprise (latest) |
| Generator model | Qwen3.6-27B-uncensored-heretic-v2 |
## Intended use
SFT data for LLMs targeting:
- Red team and purple team agentic workflows
- Codebase vulnerability auditing with remediation
- Detection rule generation (SIGMA, YARA, SPL)
- Security tool development
- Threat modeling and incident response
**Not intended for** production deployment without human review, or any use
outside authorized, scoped security testing engagements.
## What it isn't
This is a small sample dataset — 593 entries across ~697 Enterprise
techniques means roughly one angle per technique on average. It is suitable
as a representative slice for dataset mixing or evaluation, not as a
standalone fine-tuning corpus. For a full-coverage run (all techniques × all
9 angles ≈ 5,000+ entries), see the purple-team-gen pipeline.
The synthetic nature of the content means some technical specifics (API
signatures, event IDs, file paths) may not be accurate to every real-world
environment. Human review before operational use is required.
## License
MPL-2.0 with Commons Clause. Licensor: TitleOS.
The Commons Clause restricts selling this dataset or services whose value
derives substantially from it. See [LICENSE](LICENSE) for full terms. |