mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
1.7 KiB
1.7 KiB
API Reference: Diamond Model Intrusion Analysis Agent
Dependencies
| Library | Version | Purpose |
|---|---|---|
| (stdlib only) | Python 3.8+ | Dataclass-based Diamond Model event modeling |
CLI Usage
python scripts/agent.py --data /intel/events.json --output-dir /reports/
Functions
DiamondEvent (dataclass)
Four vertices: adversary, capability, infrastructure, victim. Plus: phase, result, confidence, notes.
create_event(adversary, capability, infrastructure, victim, **kwargs) -> DiamondEvent
Factory for creating Diamond Model events with auto-generated ID and timestamp.
load_events(data_path) -> list
Loads events from JSON file with {"events": [...]} structure.
pivot_on_vertex(events, vertex, value) -> list
Analytic pivot: returns all events sharing a specific vertex value.
build_activity_thread(events, adversary) -> dict
Groups events by adversary chronologically. Lists capabilities, infrastructure, victims.
cluster_by_infrastructure(events) -> dict
Groups event IDs by shared infrastructure for campaign identification.
compute_vertex_statistics(events) -> dict
Counts unique values per vertex and confidence distribution.
Input Format
{
"events": [{
"adversary": "APT29",
"capability": "Cobalt Strike",
"infrastructure": "185.220.101.42",
"victim": "finance-server-01",
"phase": "Lateral Movement",
"confidence": "high"
}]
}
Output Schema
{
"statistics": {"total_events": 15, "unique_adversaries": 2},
"activity_threads": [{"adversary": "APT29", "event_count": 8}],
"infrastructure_clusters": {"185.220.101.42": ["evt1", "evt5"]}
}