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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
60 lines
1.7 KiB
Markdown
60 lines
1.7 KiB
Markdown
# API Reference: XM Cyber Attack Path Analysis Agent
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## Dependencies
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| Library | Version | Purpose |
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|---------|---------|---------|
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| requests | >=2.28 | HTTP client for XM Cyber REST API |
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## CLI Usage
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```bash
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python scripts/agent.py \
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--url https://xmcyber.example.com \
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--api-key YOUR_API_KEY \
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--output-dir /reports/ \
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--output attack_path_report.json
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```
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## Functions
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### `XMCyberClient(base_url, api_key)`
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Client class with Bearer token auth for the XM Cyber API.
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### `get_scenarios() -> list`
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GET `/api/v1/scenarios` - Lists all attack simulation scenarios.
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### `get_attack_paths(scenario_id) -> list`
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GET `/api/v1/scenarios/{id}/attack-paths` - Returns attack paths for a scenario.
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### `get_choke_points(scenario_id) -> list`
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GET `/api/v1/scenarios/{id}/choke-points` - Returns points where attack paths converge.
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### `get_critical_assets() -> list`
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GET `/api/v1/critical-assets` - Lists defined critical business assets.
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### `get_entities_at_risk(scenario_id) -> list`
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GET `/api/v1/scenarios/{id}/entities-at-risk` - Entities reachable via attack paths.
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### `get_remediation_actions(scenario_id) -> list`
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GET `/api/v1/scenarios/{id}/remediations` - Prioritized fix recommendations.
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### `analyze_choke_points(choke_points) -> dict`
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Ranks choke points by paths_through count, returns top 10.
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### `compute_risk_score(attack_paths, critical_assets) -> dict`
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Calculates critical asset exposure percentage from reachable targets.
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## Output Schema
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```json
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{
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"scenarios": [{
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"name": "Full Environment",
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"attack_paths": 1234,
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"choke_point_analysis": {"total_choke_points": 45, "top_choke_points": []},
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"risk_score": {"critical_asset_exposure_pct": 67.5}
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}]
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}
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```
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