Claude 24f816b6a3
Consolidate 22 sibling repos into layered organism structure
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
2026-06-10 06:53:01 +00:00

192 lines
6.6 KiB
Python

#!/usr/bin/env python3
"""
OWASP Threat Dragon Model Analyzer
Parses Threat Dragon JSON threat model files and generates
summary statistics, coverage reports, and mitigation gap analysis.
"""
import json
import sys
import os
from collections import defaultdict
from datetime import datetime
def load_threat_model(filepath: str) -> dict:
with open(filepath) as f:
return json.load(f)
def extract_threats(model: dict) -> list:
threats = []
detail = model.get("detail", {})
for diagram in detail.get("diagrams", []):
diagram_title = diagram.get("title", "Untitled")
for cell in diagram.get("cells", []):
cell_data = cell.get("data", {})
cell_threats = cell_data.get("threats", [])
for threat in cell_threats:
threats.append({
"diagram": diagram_title,
"element": cell_data.get("name", cell.get("id", "unknown")),
"element_type": cell_data.get("type", "unknown"),
"title": threat.get("title", ""),
"description": threat.get("description", ""),
"severity": threat.get("severity", "Unknown"),
"status": threat.get("status", "Open"),
"type": threat.get("type", ""),
"mitigation": threat.get("mitigation", ""),
"model_type": threat.get("modelType", "STRIDE"),
})
return threats
def analyze_coverage(threats: list) -> dict:
coverage = {
"total_threats": len(threats),
"by_status": defaultdict(int),
"by_severity": defaultdict(int),
"by_type": defaultdict(int),
"by_element_type": defaultdict(int),
"mitigated_count": 0,
"open_count": 0,
"not_applicable_count": 0,
"with_mitigation_text": 0,
}
for threat in threats:
status = threat["status"]
coverage["by_status"][status] += 1
coverage["by_severity"][threat["severity"]] += 1
coverage["by_type"][threat["type"]] += 1
coverage["by_element_type"][threat["element_type"]] += 1
if status.lower() == "mitigated":
coverage["mitigated_count"] += 1
elif status.lower() == "open":
coverage["open_count"] += 1
elif status.lower() in ("not applicable", "n/a"):
coverage["not_applicable_count"] += 1
if threat["mitigation"].strip():
coverage["with_mitigation_text"] += 1
coverage["by_status"] = dict(coverage["by_status"])
coverage["by_severity"] = dict(coverage["by_severity"])
coverage["by_type"] = dict(coverage["by_type"])
coverage["by_element_type"] = dict(coverage["by_element_type"])
return coverage
def identify_gaps(threats: list) -> list:
gaps = []
for threat in threats:
if threat["status"].lower() == "open" and not threat["mitigation"].strip():
gaps.append({
"diagram": threat["diagram"],
"element": threat["element"],
"threat_title": threat["title"],
"severity": threat["severity"],
"type": threat["type"],
})
return sorted(gaps, key=lambda g: {"Critical": 0, "High": 1, "Medium": 2, "Low": 3}.get(g["severity"], 4))
def stride_coverage_check(threats: list) -> dict:
stride_categories = {
"Spoofing": False,
"Tampering": False,
"Repudiation": False,
"Information disclosure": False,
"Denial of service": False,
"Elevation of privilege": False,
}
for threat in threats:
threat_type = threat.get("type", "")
for category in stride_categories:
if category.lower() in threat_type.lower():
stride_categories[category] = True
return stride_categories
def print_report(model: dict, coverage: dict, gaps: list, stride: dict) -> None:
summary = model.get("summary", {})
print(f"\n{'='*60}")
print(f"Threat Model Analysis Report")
print(f"{'='*60}")
print(f"Title: {summary.get('title', 'Unknown')}")
print(f"Owner: {summary.get('owner', 'Unknown')}")
print(f"Description: {summary.get('description', '')}")
print(f"Generated: {datetime.utcnow().isoformat()}Z")
diagrams = model.get("detail", {}).get("diagrams", [])
print(f"\nDiagrams: {len(diagrams)}")
for d in diagrams:
print(f" - {d.get('title', 'Untitled')} ({d.get('diagramType', 'Unknown')} type)")
print(f"\nThreat Summary:")
print(f" Total threats: {coverage['total_threats']}")
print(f" Mitigated: {coverage['mitigated_count']}")
print(f" Open: {coverage['open_count']}")
print(f" Not Applicable: {coverage['not_applicable_count']}")
print(f" With mitigation documented: {coverage['with_mitigation_text']}")
if coverage["total_threats"] > 0:
mitigation_rate = coverage["mitigated_count"] / coverage["total_threats"] * 100
print(f" Mitigation rate: {mitigation_rate:.1f}%")
print(f"\nBy Severity:")
for sev in ["Critical", "High", "Medium", "Low", "Unknown"]:
count = coverage["by_severity"].get(sev, 0)
if count:
print(f" {sev:12s}: {count}")
print(f"\nSTRIDE Coverage:")
for category, covered in stride.items():
status = "COVERED" if covered else "MISSING"
print(f" {category:25s}: {status}")
if gaps:
print(f"\nMitigation Gaps ({len(gaps)} open threats without mitigations):")
for gap in gaps:
print(f" [{gap['severity']}] {gap['threat_title']}")
print(f" Element: {gap['element']} | Diagram: {gap['diagram']}")
else:
print(f"\nNo mitigation gaps found.")
def main():
if len(sys.argv) < 2:
print("Usage: python process.py <threat_model.json>")
print(" Analyzes an OWASP Threat Dragon JSON threat model file")
sys.exit(1)
filepath = sys.argv[1]
if not os.path.exists(filepath):
print(f"File not found: {filepath}")
sys.exit(1)
model = load_threat_model(filepath)
threats = extract_threats(model)
coverage = analyze_coverage(threats)
gaps = identify_gaps(threats)
stride = stride_coverage_check(threats)
print_report(model, coverage, gaps, stride)
output = filepath.replace(".json", "_analysis.json")
analysis = {
"model_title": model.get("summary", {}).get("title"),
"analysis_date": datetime.utcnow().isoformat() + "Z",
"coverage": coverage,
"gaps": gaps,
"stride_coverage": stride,
}
with open(output, "w") as f:
json.dump(analysis, f, indent=2)
print(f"\nAnalysis saved to: {output}")
if __name__ == "__main__":
main()