mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +00:00
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
229 lines
10 KiB
Python
229 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Social Engineering Campaign Tracker
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Tracks vishing (pretext call) campaign results, calculates susceptibility
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metrics, and generates reports for security awareness improvement.
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"""
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import json
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import os
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import csv
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from datetime import datetime
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from collections import defaultdict
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from dataclasses import dataclass, field, asdict
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@dataclass
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class VishingCall:
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"""Represents a single vishing call attempt."""
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call_id: str
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timestamp: str
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target_name: str
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target_department: str
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target_role: str
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pretext_used: str
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call_duration_seconds: int
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call_answered: bool
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credential_disclosed: bool
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sensitive_info_disclosed: bool
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info_type_disclosed: str = "" # password, username, badge_number, etc.
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verification_attempted: bool = False
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reported_to_security: bool = False
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susceptibility_score: int = 0 # 1-5
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notes: str = ""
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operator: str = ""
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class VishingCampaignTracker:
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"""Track and analyze vishing campaign results."""
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def __init__(self, campaign_id: str, client_name: str):
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self.campaign_id = campaign_id
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self.client_name = client_name
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self.calls: list[VishingCall] = []
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def log_call(self, call: VishingCall) -> None:
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"""Log a vishing call result."""
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self.calls.append(call)
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def calculate_metrics(self) -> dict:
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"""Calculate campaign metrics."""
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answered = [c for c in self.calls if c.call_answered]
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total_answered = len(answered)
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if total_answered == 0:
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return {"error": "No answered calls to analyze"}
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cred_disclosed = [c for c in answered if c.credential_disclosed]
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info_disclosed = [c for c in answered if c.sensitive_info_disclosed]
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verified = [c for c in answered if c.verification_attempted]
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reported = [c for c in answered if c.reported_to_security]
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# Per-department breakdown
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dept_stats = defaultdict(lambda: {
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"total": 0, "cred_disclosed": 0, "info_disclosed": 0,
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"verified": 0, "reported": 0,
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})
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for call in answered:
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dept = call.target_department
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dept_stats[dept]["total"] += 1
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if call.credential_disclosed:
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dept_stats[dept]["cred_disclosed"] += 1
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if call.sensitive_info_disclosed:
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dept_stats[dept]["info_disclosed"] += 1
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if call.verification_attempted:
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dept_stats[dept]["verified"] += 1
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if call.reported_to_security:
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dept_stats[dept]["reported"] += 1
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# Per-pretext breakdown
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pretext_stats = defaultdict(lambda: {"total": 0, "success": 0})
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for call in answered:
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pretext_stats[call.pretext_used]["total"] += 1
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if call.credential_disclosed or call.sensitive_info_disclosed:
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pretext_stats[call.pretext_used]["success"] += 1
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avg_duration = sum(c.call_duration_seconds for c in answered) / total_answered
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avg_susceptibility = sum(c.susceptibility_score for c in answered) / total_answered
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return {
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"campaign_id": self.campaign_id,
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"total_calls": len(self.calls),
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"calls_answered": total_answered,
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"answer_rate": total_answered / len(self.calls) * 100,
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"credential_disclosure_rate": len(cred_disclosed) / total_answered * 100,
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"sensitive_info_disclosure_rate": len(info_disclosed) / total_answered * 100,
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"verification_rate": len(verified) / total_answered * 100,
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"security_reporting_rate": len(reported) / total_answered * 100,
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"avg_call_duration_seconds": avg_duration,
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"avg_susceptibility_score": avg_susceptibility,
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"department_breakdown": dict(dept_stats),
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"pretext_effectiveness": dict(pretext_stats),
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}
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def generate_report(self) -> str:
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"""Generate campaign report."""
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metrics = self.calculate_metrics()
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if "error" in metrics:
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return metrics["error"]
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lines = []
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lines.append("=" * 70)
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lines.append("VISHING CAMPAIGN ASSESSMENT REPORT")
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lines.append(f"Campaign: {self.campaign_id}")
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lines.append(f"Client: {self.client_name}")
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lines.append(f"Date: {datetime.now().strftime('%Y-%m-%d')}")
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lines.append("=" * 70)
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lines.append(f"\nOVERALL METRICS:")
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lines.append(f" Total Calls Made: {metrics['total_calls']}")
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lines.append(f" Calls Answered: {metrics['calls_answered']}")
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lines.append(f" Answer Rate: {metrics['answer_rate']:.1f}%")
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lines.append(f" Credential Disclosure Rate: {metrics['credential_disclosure_rate']:.1f}%")
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lines.append(f" Info Disclosure Rate: {metrics['sensitive_info_disclosure_rate']:.1f}%")
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lines.append(f" Verification Rate: {metrics['verification_rate']:.1f}%")
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lines.append(f" Security Reporting Rate: {metrics['security_reporting_rate']:.1f}%")
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lines.append(f" Avg Call Duration: {metrics['avg_call_duration_seconds']:.0f}s")
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lines.append(f" Avg Susceptibility (1-5): {metrics['avg_susceptibility_score']:.1f}")
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# Risk assessment
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cred_rate = metrics['credential_disclosure_rate']
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risk = "CRITICAL" if cred_rate > 30 else "HIGH" if cred_rate > 15 else "MEDIUM" if cred_rate > 5 else "LOW"
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lines.append(f"\n OVERALL RISK RATING: {risk}")
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# Department breakdown
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lines.append(f"\nDEPARTMENT BREAKDOWN:")
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lines.append("-" * 70)
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for dept, stats in metrics["department_breakdown"].items():
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total = stats["total"]
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cred_pct = stats["cred_disclosed"] / total * 100 if total else 0
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verify_pct = stats["verified"] / total * 100 if total else 0
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lines.append(
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f" {dept:<20} Calls: {total:>3} | "
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f"Cred Disclosed: {cred_pct:>5.1f}% | "
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f"Verified: {verify_pct:>5.1f}%"
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)
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# Pretext effectiveness
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lines.append(f"\nPRETEXT EFFECTIVENESS:")
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lines.append("-" * 70)
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for pretext, stats in metrics["pretext_effectiveness"].items():
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success_rate = stats["success"] / stats["total"] * 100 if stats["total"] else 0
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lines.append(f" {pretext:<30} Success: {success_rate:.1f}% ({stats['success']}/{stats['total']})")
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# Recommendations
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lines.append(f"\nRECOMMENDATIONS:")
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lines.append("-" * 70)
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if metrics["credential_disclosure_rate"] > 10:
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lines.append(" [CRITICAL] Implement mandatory caller verification procedures")
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if metrics["verification_rate"] < 50:
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lines.append(" [HIGH] Enhance security awareness training on verification")
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if metrics["security_reporting_rate"] < 30:
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lines.append(" [HIGH] Establish easy-to-use suspicious call reporting process")
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lines.append(" [MEDIUM] Conduct quarterly vishing simulations")
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lines.append(" [MEDIUM] Implement callback verification for sensitive requests")
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return "\n".join(lines)
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def export_csv(self, output_path: str) -> None:
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"""Export results to CSV."""
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with open(output_path, "w", newline="") as f:
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writer = csv.writer(f)
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writer.writerow([
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"Call ID", "Timestamp", "Target", "Department", "Role",
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"Pretext", "Duration(s)", "Answered", "Cred Disclosed",
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"Info Disclosed", "Verified", "Reported", "Score",
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])
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for call in self.calls:
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writer.writerow([
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call.call_id, call.timestamp, call.target_name,
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call.target_department, call.target_role, call.pretext_used,
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call.call_duration_seconds, call.call_answered,
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call.credential_disclosed, call.sensitive_info_disclosed,
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call.verification_attempted, call.reported_to_security,
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call.susceptibility_score,
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])
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def main():
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"""Demonstrate vishing campaign tracking."""
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tracker = VishingCampaignTracker("VISH-2025-001", "Example Corp")
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sample_calls = [
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VishingCall("V001", "2025-02-01T09:00:00", "Alice Johnson", "Finance",
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"Accountant", "IT Helpdesk - VPN Update", 180, True, True,
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True, "password", False, False, 5),
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VishingCall("V002", "2025-02-01T09:30:00", "Bob Smith", "IT",
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"Sysadmin", "Vendor Support Call", 45, True, False,
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False, "", True, True, 1),
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VishingCall("V003", "2025-02-01T10:00:00", "Carol Davis", "HR",
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"HR Manager", "Benefits Verification", 120, True, False,
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True, "employee_id", False, False, 3),
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VishingCall("V004", "2025-02-01T10:30:00", "Dan Wilson", "Finance",
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"Controller", "Wire Transfer Request", 60, True, False,
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False, "", True, True, 1),
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VishingCall("V005", "2025-02-01T11:00:00", "Eve Brown", "Marketing",
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"Manager", "IT Helpdesk - Password Reset", 150, True, True,
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True, "password", False, False, 4),
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VishingCall("V006", "2025-02-01T11:30:00", "Frank Lee", "Engineering",
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"Developer", "IT Helpdesk - VPN Update", 30, True, False,
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False, "", True, False, 2),
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VishingCall("V007", "2025-02-01T13:00:00", "Grace Kim", "Reception",
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"Front Desk", "Delivery Confirmation", 90, True, False,
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True, "employee_directory", False, False, 3),
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VishingCall("V008", "2025-02-01T13:30:00", "Henry Chen", "IT",
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"Help Desk", "New Employee Onboarding", 20, True, False,
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False, "", True, True, 1),
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]
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for call in sample_calls:
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tracker.log_call(call)
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print(tracker.generate_report())
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tracker.export_csv("vishing_results.csv")
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print(f"\n[+] Results exported to vishing_results.csv")
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if __name__ == "__main__":
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main()
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