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Complete skill folder anatomy across all cybersecurity skills: - scripts/agent.py: 80-150 line Python agents using real libraries (impacket, boto3, azure-mgmt-*, kubernetes, pefile, yara, scapy, shodan, stix2, etc.) - references/api-reference.md: real API documentation with method signatures - LICENSE: MIT license for all skill folders
54 lines
1.6 KiB
Markdown
54 lines
1.6 KiB
Markdown
# API Reference: Breach and Attack Simulation 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 SIEM detection validation |
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## CLI Usage
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```bash
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python scripts/agent.py \
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--target 10.0.1.50 \
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--siem-url https://siem.example.com \
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--siem-key YOUR_KEY \
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--output-dir /reports/
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```
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## Functions
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### `simulate_technique(technique, target) -> dict`
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Simulates a MITRE ATT&CK technique and records detection/blocked status.
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### `check_siem_detection(siem_url, api_key, technique_id, time_window) -> dict`
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Queries SIEM API for alerts matching the simulated technique within time window.
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### `compute_detection_coverage(results) -> dict`
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Calculates overall detection rate and per-tactic coverage breakdown.
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### `generate_report(target, siem_url, siem_key) -> dict`
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Runs 7 ATT&CK technique simulations and generates detection gap report.
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## ATT&CK Techniques Tested
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| ID | Name | Tactic |
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|----|------|--------|
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| T1566.001 | Spearphishing Attachment | Initial Access |
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| T1059.001 | PowerShell | Execution |
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| T1003.001 | LSASS Memory | Credential Access |
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| T1021.002 | SMB Admin Shares | Lateral Movement |
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| T1486 | Data Encrypted for Impact | Impact |
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| T1071.001 | Web Protocols | C2 |
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| T1048.003 | Exfiltration Over Unencrypted | Exfiltration |
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## Output Schema
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```json
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{
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"coverage": {"total_tests": 7, "detected": 5, "missed": 2, "detection_rate_pct": 71.4},
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"gaps": [{"technique_id": "T1003.001", "technique_name": "LSASS Memory"}],
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"recommendations": ["Create detection rule for T1003.001"]
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}
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```
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