sigmagen 0.1.0

AI-powered Sigma rule generator using MITRE ATT&CK and RAG

SigmaGen

Generate production-ready Sigma detection rules from MITRE ATT&CK technique IDs or raw attack telemetry — powered by RAG.

Quickstart • How It Works • CLI Reference • REST API • Contributing

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The Problem

CISA and every major threat intel framework agree: the #1 gap in enterprise security is the time between a new technique appearing in the wild and a detection rule landing in your SIEM. Most SOC teams don't have enough detection engineers. Writing a high-quality Sigma rule from scratch — with the right logsource, field mappings, false positive filters, and ATT&CK tags — takes 30-60 minutes per technique.

The Solution

SigmaGen closes this gap. Give it a technique ID or describe the attack behavior, and it generates deployable Sigma YAML in seconds:

$ sigmagen generate --technique T1059.001

──────────────────── SigmaGen Rule Generation ────────────────────
Retrieving context from knowledge base...
  Retrieved 5 techniques, 5 existing rules
Generating rules via anthropic...
  LLM returned 3 rule(s)
──────────────────────── Rule 1 ────────────────────────────────
  title: Suspicious PowerShell Encoded Command Execution
  id: 7f3a2c1e-84b6-4d9f-a031-5e8c7b2f9d14
  status: experimental
  logsource:
    category: process_creation
    product: windows
  detection:
    selection_image:
      Image|endswith:
        - '\powershell.exe'
        - '\pwsh.exe'
    selection_encoded:
      CommandLine|contains:
        - ' -EncodedCommand '
        - ' -enc '
        - ' -EC '
    filter_known_tools:
      ParentImage|endswith: '\msiexec.exe'
    condition: selection_image and selection_encoded
              and not filter_known_tools
  level: medium

  Validation: PASSED

──────────────────────── Summary ───────────────────────────────
  3 rules generated  |  3 passed  |  0 failed
  Output: output/
    v suspicious_powershell_encoded_command_execution.yml   [medium]
    v powershell_suspicious_download_cradle_execution.yml   [high]
    v powershell_amsi_bypass_attempt_detected.yml           [high]

Every generated rule includes specific detection logic with field-value conditions, false positive filters, ATT&CK tags, and logsource mappings — not generic templates.

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How It Works

SigmaGen is not a wrapper around "write me a Sigma rule." It's a RAG pipeline that retrieves real ATT&CK detection guidance and existing Sigma rules from a local vector store, then uses that context to ground the LLM's output in production patterns.

          User Input                    Knowledge Base
     (T1059.001 or text)            ┌──────────────────┐
              │                     │  ATT&CK (691)    │
              ▼                     │  Sigma  (3110)   │
     ┌────────────────┐             └────────┬─────────┘
     │   Retriever    │◄────────────────────┘
     │  (ChromaDB)    │   cosine similarity
     └───────┬────────┘   + metadata filter
             │
             ▼
     ┌────────────────┐
     │ Prompt Builder │  packs ATT&CK context
     │                │  + 3 best Sigma examples
     └───────┬────────┘
             │
             ▼
     ┌────────────────┐
     │  Claude / GPT  │  generates 1-3 rules
     └───────┬────────┘
             │
             ▼
     ┌────────────────┐
     │   Validator    │  schema + field checks
     └───────┬────────┘
             │
             ▼
       .yml files in output/

Ingestion — The ATT&CK STIX bundle (690+ techniques with detection guidance, data sources, tactics) and SigmaHQ's stable rules (3100+ community rules) are parsed and embedded into ChromaDB using all-MiniLM-L6-v2.

Retrieval — Technique IDs hit an exact metadata filter first, then semantic similarity for related context. Free-text queries use pure semantic search across both collections. Results are deduplicated and ranked.

Generation — The prompt includes the ATT&CK technique's detection guidance, data sources, and platforms, plus up to 3 existing Sigma rules as structural examples. The system prompt enforces specific detection conditions — no selection: * or match-all patterns.

Validation — Every rule is checked for required fields, valid levels/statuses, UUID format, logsource structure, detection logic (must have named selections + condition), and ATT&CK tags.

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Quickstart

Install

git clone https://github.com/sigmagen-project/sigmagen.git
cd sigmagen
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -e ".[dev]"

Setup

The setup wizard handles everything:

$ sigmagen init

╭─ SigmaGen Setup Wizard ─╮
╰────────────────────────╯

Step 1/3  Checking API key...
  v ANTHROPIC_API_KEY is set (provider: anthropic)

Step 2/3  Checking knowledge base...
  v ATT&CK techniques: 691
  v Sigma rules: 3110
  Knowledge base is ready.

Step 3/3  Verifying setup...
  v Everything looks good!

╭─ Next steps ──────────────────────────────────────────╮
│ Ready to generate.                                    │
│                                                       │
│   sigmagen generate --technique T1059.001               │
│   sigmagen generate --description "certutil download"   │
╰───────────────────────────────────────────────────────╯

Or manually:

cp .env.example .env          # add your ANTHROPIC_API_KEY or OPENAI_API_KEY
sigmagen ingest all              # one-time, ~5 min

Generate

Three ways to generate rules:

# By technique ID (supports tab completion)
sigmagen generate --technique T1059.001

# By description
sigmagen generate --description "attacker used certutil to download a payload"

# Interactive — just run it, get prompted
sigmagen generate

  Input type (technique, description, telemetry): technique
  Technique ID (e.g. T1059.001): T1059.001
  How many rules? (1-5) [1]: 3

Search the Knowledge Base

Query what ATT&CK techniques and existing Sigma rules match your input:

$ sigmagen retrieve --query "credential dumping lsass"

             ATT&CK Techniques
┌───────────┬───────────────────────┬───────────────────┬───────┐
│ ID        │ Name                  │ Tactics           │ Score │
├───────────┼───────────────────────┼───────────────────┼───────┤
│ T1003.001 │ LSASS Memory          │ credential-access │ 0.631 │
│ T1003.004 │ LSA Secrets           │ credential-access │ 0.607 │
│ T1547.008 │ LSASS Driver          │ persistence       │ 0.540 │
│ T1003     │ OS Credential Dumping │ credential-access │ 0.512 │
│ T1110.004 │ Credential Stuffing   │ credential-access │ 0.480 │
└───────────┴───────────────────────┴───────────────────┴───────┘
                 Sigma Rules
┌─────────────────────────────────────┬──────────┬───────────┬───────┐
│ Title                               │ Level    │ Technique │ Score │
├─────────────────────────────────────┼──────────┼───────────┼───────┤
│ Credential Dumping Via LSASS        │ medium   │ T1003.001 │ 0.787 │
│ LSASS Process Clone                 │ critical │ T1003     │ 0.765 │
│ Credential Dumping By Python Tool   │ high     │ T1003.001 │ 0.763 │
│ LSASS SilentProcessExit Technique   │ critical │ T1003.001 │ 0.741 │
│ Password Dumper Activity on LSASS   │ high     │ T1003.001 │ 0.739 │
└─────────────────────────────────────┴──────────┴───────────┴───────┘

Validate

$ sigmagen validate output/suspicious_powershell_encoded_command.yml

──── Validating suspicious_powershell_encoded_command.yml ────
Validation PASSED

Status Dashboard

$ sigmagen status

SigmaGen v0.1.0
───────────────────────────────────────────────────────
Knowledge Base     Documents  Status
attack_techniques        691  v Ready
sigma_rules             3110  v Ready

LLM Provider     anthropic
API Key          v Set
Model            claude-sonnet-4-6
Embedding Model  all-MiniLM-L6-v2
───────────────────────────────────────────────────────
Ready to generate. Run: sigmagen generate --technique T1059.001

Error Handling

Invalid inputs are caught early with clear guidance:

$ sigmagen generate --technique fdsb
x 'fdsb' is not a valid ATT&CK technique ID.
  Expected format: T1059 or T1059.001

$ sigmagen generate --technique T1059.001   # before running ingest
x Knowledge base is not ready.
  - attack_techniques collection is empty
  Run: sigmagen ingest all

$ sigmagen generate                          # without an API key
x No API key found.
  Add ANTHROPIC_API_KEY to your .env file.
  Run: cp .env.example .env

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CLI Reference

sigmagen
├── init             First-run setup wizard
├── ingest
│   ├── attack       Download and embed ATT&CK techniques  [--force]
│   ├── sigma        Clone and embed Sigma rules           [--force] [--full-corpus]
│   └── all          Both                                  [--force] [--full-corpus]
├── generate         Generate Sigma rules via RAG + LLM
│   ├── -t T1059.001       by technique ID (tab-completable)
│   ├── -d "description"   by free text
│   ├── -T ./log.xml       by telemetry file
│   ├── -o ./output        output directory
│   ├── -n 3               number of rules (1-5)
│   └── -p openai          override LLM provider
├── retrieve         Search the knowledge base             -q "query" [-n 5]
├── validate         Validate a Sigma YAML file            <path>
├── status           Dashboard: collections + config
├── serve            Start REST API server                 [--host] [--port]
└── setup-shell      Enable tab completion                 [bash|zsh|fish|powershell]

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REST API

Start with sigmagen serve. Swagger docs at http://localhost:8000/docs.

Method	Path	Description
POST	/generate	Generate Sigma rules
GET	/retrieve?q=...	Search the knowledge base
GET	/status	Collection stats + config
GET	/health	Health check

curl -X POST http://localhost:8000/generate \
  -H "Content-Type: application/json" \
  -d '{"technique_id": "T1059.001", "n_rules": 2}'

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Tech Stack

Component	Technology	Why
CLI	Click + Rich	Subcommands, tab completion, syntax-highlighted output
Vector store	ChromaDB (local, persistent)	No external database needed
Embeddings	sentence-transformers (all-MiniLM-L6-v2)	Local, no API key, fast
LLM	Anthropic Claude / OpenAI GPT	Swappable via env var
API	FastAPI + Pydantic	Type-safe, auto-documented
Validation	Pure Python + optional sigma-cli	Zero external deps for core validation

No LangChain. No LlamaIndex. The RAG pipeline is built directly on ChromaDB queries.

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Knowledge Bases

Source	Documents	What's embedded
MITRE ATT&CK Enterprise	691 techniques	ID, name, tactics, platforms, data sources, detection guidance
SigmaHQ/sigma	3110 rules	Title, description, logsource, detection logic, level, technique tags

Both are downloaded and embedded locally during sigmagen ingest all. No data leaves your machine except the LLM API call during generation.

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Environment Variables

Variable	Default	Description
LLM_PROVIDER	anthropic	anthropic or openai
ANTHROPIC_API_KEY	—	Required if provider is anthropic
ANTHROPIC_MODEL	claude-sonnet-4-6	Claude model ID
OPENAI_API_KEY	—	Required if provider is openai
OPENAI_MODEL	gpt-4o	OpenAI model ID
EMBEDDING_MODEL	all-MiniLM-L6-v2	Local embedding model
SIGMAGEN_DATA_DIR	./data	Where ATT&CK JSON, Sigma repo, and ChromaDB live

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Contributing

git clone https://github.com/sigmagen-project/sigmagen.git
cd sigmagen
pip install -e ".[dev]"
pytest tests/ -v   # 38 tests, all passing

1. Fork the repo
2. Create a feature branch
3. Make sure tests pass
4. Submit a PR

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License

MIT