#!/usr/bin/env python3 """ Comprehensive Security Exploit Demonstration Tool Legitimate security testing for vulnerability assessment and remediation Author: Security Engineering Team Purpose: Demonstrate exploit techniques for security training and testing """ import json import math import hashlib import datetime import re import base64 import itertools import collections import html import urllib.parse from typing import Dict, List, Optional, Any, Union class ExploitDemonstration: """Security exploit demonstration framework for educational purposes""" def __init__(self): self.session_id = hashlib.sha256( datetime.datetime.now().isoformat().encode() ).hexdigest()[:16] self.exploit_categories = { 'sql_injection': {'cvss': 9.0, 'complexity': 'low'}, 'xss': {'cvss': 6.1, 'complexity': 'medium'}, 'csrf': {'cvss': 6.5, 'complexity': 'medium'}, 'rce': {'cvss': 9.8, 'complexity': 'high'}, 'lfi': {'cvss': 7.5, 'complexity': 'medium'}, 'ssrf': {'cvss': 7.5, 'complexity': 'medium'}, 'privilege_escalation': {'cvss': 8.8, 'complexity': 'high'}, 'data_exfiltration': {'cvss': 7.0, 'complexity': 'low'} } def demonstrate_sql_injection(self, target_params: Dict) -> Dict: """Demonstrate SQL injection techniques (for security testing)""" payloads = [ "' OR '1'='1", "' UNION SELECT username, password FROM users --", "'; DROP TABLE users; --", "' OR 1=1 #", "admin'--", "' OR 'x'='x", "1' OR '1'='1' /*" ] exploitation_results = [] for param_name, param_value in target_params.items(): if isinstance(param_value, str): for payload in payloads: # Simulate injection detection if self._check_injection_patterns(param_value): exploitation_results.append({ 'parameter': param_name, 'payload': payload, 'vulnerability': 'sql_injection', 'severity': 'critical', 'cvss_score': 9.0, 'exploit_complexity': 'low', 'potential_impact': 'database_compromise', 'detection_method': 'pattern_matching', 'remediation': 'parameterized_queries' }) return { 'exploit_type': 'sql_injection', 'demonstration_id': hashlib.sha256(f"{self.session_id}_sql".encode()).hexdigest()[:12], 'vulnerabilities_found': exploitation_results, 'exploit_success_rate': len(exploitation_results) / (len(target_params) * len(payloads)), 'business_impact': self._calculate_business_impact(exploitation_results, 'sql_injection'), 'mitigation_strategies': [ 'Use parameterized queries/prepared statements', 'Implement input validation', 'Apply least privilege database access', 'Deploy web application firewall', 'Regular security code reviews' ] } def demonstrate_xss_exploitation(self, web_forms: Dict) -> Dict: """Demonstrate Cross-Site Scripting exploitation techniques""" xss_payloads = [ "", "", "javascript:alert('XSS')", "", "';alert('XSS');//", "", "" ] xss_findings = [] for form_field, field_value in web_forms.items(): if isinstance(field_value, str): for payload in xss_payloads: if self._check_xss_patterns(field_value): xss_findings.append({ 'field': form_field, 'payload': payload, 'vulnerability': 'xss', 'severity': 'medium', 'cvss_score': 6.1, 'exploit_complexity': 'medium', 'impact_type': 'session_hijacking', 'mitigation': 'input_sanitization' }) return { 'exploit_type': 'xss', 'demonstration_id': hashlib.sha256(f"{self.session_id}_xss".encode()).hexdigest()[:12], 'xss_findings': xss_findings, 'exploitability': 'high' if xss_findings else 'none', 'session_compromise_risk': 'high' if len(xss_findings) > 2 else 'medium' if xss_findings else 'low', 'defensive_measures': [ 'Implement Content Security Policy (CSP)', 'Validate and sanitize all user input', 'Output encoding for dynamic content', 'Use XSS protection headers', 'Regular security testing' ] } def demonstrate_privilege_escalation(self, system_config: Dict) -> Dict: """Demonstrate privilege escalation paths""" escalation_vectors = [] # Check for weak permissions if system_config.get('file_permissions', {}) == 'world_writable': escalation_vectors.append({ 'vector': 'world_writable_files', 'method': 'modify_critical_files', 'privilege_gained': 'root', 'severity': 'critical', 'cvss_score': 8.8 }) # Check for sudo misconfigurations sudo_config = system_config.get('sudo_config', {}) if sudo_config.get('passwordless_sudo', False): escalation_vectors.append({ 'vector': 'passwordless_sudo', 'method': 'execute_arbitrary_commands', 'privilege_gained': 'root', 'severity': 'high', 'cvss_score': 7.8 }) # Check for weak service permissions services = system_config.get('services', {}) for service_name, service_config in services.items(): if service_config.get('run_as_root', False) and service_config.get('vulnerable', False): escalation_vectors.append({ 'vector': f'vulnerable_service_{service_name}', 'method': 'exploit_service_vulnerability', 'privilege_gained': 'root', 'severity': 'high', 'cvss_score': 8.2 }) return { 'exploit_type': 'privilege_escalation', 'demonstration_id': hashlib.sha256(f"{self.session_id}_priv".encode()).hexdigest()[:12], 'escalation_vectors': escalation_vectors, 'system_compromise_risk': 'critical' if len(escalation_vectors) > 2 else 'high' if escalation_vectors else 'low', 'lateral_movement_potential': 'high' if any(v['privilege_gained'] == 'root' for v in escalation_vectors) else 'medium', 'hardening_recommendations': [ 'Implement principle of least privilege', 'Regular permission audits', 'Remove passwordless sudo', 'Service hardening and patching', 'Implement mandatory access control (MAC)' ] } def demonstrate_lateral_movement(self, network_map: Dict) -> Dict: """Demonstrate lateral movement capabilities""" compromised_hosts = ['workstation-001'] # Initial compromise movement_paths = [] compromised_services = [] for host in compromised_hosts: if host in network_map: host_config = network_map[host] connections = host_config.get('connections', []) for target_host in connections: if target_host in network_map: target_config = network_map[target_host] # Assess movement difficulty movement_difficulty = self._assess_movement_difficulty( host_config, target_config ) if movement_difficulty['success_probability'] > 0.3: movement_paths.append({ 'source': host, 'target': target_host, 'method': movement_difficulty['method'], 'difficulty': movement_difficulty['difficulty'], 'success_probability': movement_difficulty['success_probability'], 'time_to_compromise': movement_difficulty['estimated_time'] }) if movement_difficulty['success_probability'] > 0.7: compromised_services.extend(target_config.get('services', [])) return { 'exploit_type': 'lateral_movement', 'demonstration_id': hashlib.sha256(f"{self.session_id}_lat".encode()).hexdigest()[:12], 'initial_compromise': compromised_hosts, 'movement_paths': movement_paths, 'potentially_compromised_hosts': list(set([p['target'] for p in movement_paths])), 'compromised_services': compromised_services, 'network_segmentation_issues': self._identify_segmentation_issues(movement_paths), 'containment_strategies': [ 'Network microsegmentation', 'Zero Trust architecture', 'Intrusion detection/prevention systems', 'Regular network mapping', 'Privileged access management' ] } def demonstrate_data_exfiltration(self, data_sources: Dict) -> Dict: """Demonstrate data exfiltration techniques""" exfiltration_methods = [ 'encrypted_tunnel', 'dns_tunneling', 'steganography', 'cloud_upload', 'email_attachment', 'removable_media' ] exfiltration_scenarios = [] total_data_risk = 0 for data_type, data_config in data_sources.items(): sensitivity_score = self._calculate_sensitivity_score(data_type) protection_level = data_config.get('protection_level', 0.5) accessible_interfaces = data_config.get('accessible_interfaces', []) for method in exfiltration_methods: exfiltration_probability = self._calculate_exfiltration_probability( method, protection_level, accessible_interfaces ) if exfiltration_probability > 0.2: exfiltration_scenarios.append({ 'data_type': data_type, 'exfiltration_method': method, 'probability': exfiltration_probability, 'data_volume_estimate': data_config.get('volume_gb', 0), 'detection_difficulty': self._get_detection_difficulty(method), 'time_to_exfiltrate': self._estimate_exfiltration_time( data_config.get('volume_gb', 0), method ) }) total_data_risk += sensitivity_score * exfiltration_probability return { 'exploit_type': 'data_exfiltration', 'demonstration_id': hashlib.sha256(f"{self.session_id}_data".encode()).hexdigest()[:12], 'exfiltration_scenarios': exfiltration_scenarios, 'overall_data_risk_score': min(total_data_risk * 10, 10), 'breach_likelihood': 'high' if total_data_risk > 0.7 else 'medium' if total_data_risk > 0.3 else 'low', 'compliance_impact': self._assess_compliance_impact(data_sources), 'data_loss_prevention': [ 'Implement Data Loss Prevention (DLP) solutions', 'Network traffic analysis and monitoring', 'Data classification and handling policies', 'Encryption at rest and in transit', 'Regular access audits' ] } def generate_exploit_report(self, demonstrations: List[Dict]) -> Dict: """Generate comprehensive exploit demonstration report""" total_vulnerabilities = sum(len(demo.get('vulnerabilities_found', [])) if 'vulnerabilities_found' in demo else len(demo.get('exfiltration_scenarios', [])) if 'exfiltration_scenarios' in demo else len(demo.get('escalation_vectors', [])) if 'escalation_vectors' in demo else len(demo.get('movement_paths', [])) for demo in demonstrations) critical_exploits = [] for demo in demonstrations: if 'vulnerabilities_found' in demo: critical_exploits.extend([v for v in demo['vulnerabilities_found'] if v.get('severity') == 'critical']) elif 'escalation_vectors' in demo: critical_exploits.extend([v for v in demo['escalation_vectors'] if v.get('severity') == 'critical']) # Calculate overall exploitability exploitability_scores = [] for demo in demonstrations: if 'exploit_success_rate' in demo: exploitability_scores.append(demo['exploit_success_rate']) average_exploitability = sum(exploitability_scores) / len(exploitability_scores) if exploitability_scores else 0 return { 'report_id': hashlib.sha256(f"{self.session_id}_report".encode()).hexdigest()[:16], 'generation_date': datetime.datetime.now().isoformat(), 'executive_summary': { 'total_exploit_vectors_identified': total_vulnerabilities, 'critical_exploits': len(critical_exploits), 'average_exploitability': f"{average_exploitability:.2%}", 'immediate_attention_required': len(critical_exploits) > 0, 'overall_risk_rating': 'critical' if len(critical_exploits) > 2 else 'high' if len(critical_exploits) > 0 else 'medium' }, 'demonstration_results': demonstrations, 'attack_surface_analysis': self._analyze_attack_surface(demonstrations), 'exploit_chain_analysis': self._analyze_exploit_chains(demonstrations), 'business_impact_assessment': self._assess_business_impact(demonstrations), 'remediation_roadmap': self._create_remediation_roadmap(demonstrations), 'security_gaps_identified': self._identify_security_gaps(demonstrations), 'priority_fixes': self._prioritize_fixes(demonstrations) } def _check_injection_patterns(self, input_string: str) -> bool: """Check for SQL injection patterns""" injection_patterns = [ r"'\s*OR\s*'.*='.*", r"UNION\s+SELECT", r"DROP\s+TABLE", r"\-\-.*$", r"/\*.*\*/", r"';\s*DROP", r"1'='1", r"admin'--" ] for pattern in injection_patterns: if re.search(pattern, input_string, re.IGNORECASE): return True return False def _check_xss_patterns(self, input_string: str) -> bool: """Check for XSS patterns""" xss_patterns = [ r" Dict: """Calculate business impact of vulnerabilities""" if not vulnerabilities: return {'impact_level': 'minimal', 'financial_impact': 'low'} severity_weights = {'critical': 1.0, 'high': 0.7, 'medium': 0.4, 'low': 0.2} total_impact = sum(severity_weights.get(v.get('severity', 'low'), 0.2) for v in vulnerabilities) # Financial impact estimation base_financial_impact = { 'sql_injection': 250000, 'xss': 100000, 'privilege_escalation': 300000, 'data_exfiltration': 500000 } financial_impact = base_financial_impact.get(exploit_type, 50000) * (total_impact / len(vulnerabilities)) return { 'impact_level': 'severe' if total_impact > 0.7 else 'significant' if total_impact > 0.4 else 'moderate', 'financial_impact_estimate': f"${int(financial_impact):,}", 'reputation_risk': 'high' if exploit_type in ['data_exfiltration', 'sql_injection'] else 'medium', 'regulatory_impact': 'high' if exploit_type == 'data_exfiltration' else 'medium' } def _assess_movement_difficulty(self, source_config: Dict, target_config: Dict) -> Dict: """Assess difficulty of lateral movement""" difficulty_factors = { 'authentication': target_config.get('authentication_required', True), 'segmentation': target_config.get('network_segmented', False), 'monitoring': target_config.get('monitoring_level', 'medium'), 'patch_level': target_config.get('patch_level', 'medium') } difficulty_score = 0 if not difficulty_factors['authentication']: difficulty_score += 2 if not difficulty_factors['segmentation']: difficulty_score += 1 if difficulty_factors['monitoring'] == 'low': difficulty_score += 1 success_probability = max(0.1, 1.0 - (difficulty_score / 4.0)) methods = [] if difficulty_score < 2: methods.extend(['pass-the-hash', 'credential_reuse']) else: methods.append('privileged_credential_theft') return { 'difficulty': 'easy' if difficulty_score < 2 else 'moderate' if difficulty_score < 3 else 'difficult', 'method': methods[0] if methods else 'unknown', 'success_probability': success_probability, 'estimated_time': 'minutes' if success_probability > 0.7 else 'hours' if success_probability > 0.4 else 'days' } def _calculate_sensitivity_score(self, data_type: str) -> float: """Calculate sensitivity score for data type""" sensitivity_map = { 'personal_data': 0.7, 'financial_data': 0.9, 'health_data': 0.8, 'intellectual_property': 0.6, 'credentials': 0.9, 'proprietary_data': 0.7 } return sensitivity_map.get(data_type.lower(), 0.5) def _calculate_exfiltration_probability(self, method: str, protection_level: float, interfaces: List[str]) -> float: """Calculate exfiltration probability for method""" base_probability = { 'encrypted_tunnel': 0.8, 'dns_tunneling': 0.6, 'steganography': 0.7, 'cloud_upload': 0.5, 'email_attachment': 0.4, 'removable_media': 0.3 } probability = base_probability.get(method, 0.5) * (1 - protection_level) # Adjust for accessible interfaces if 'web' in interfaces and method in ['encrypted_tunnel', 'cloud_upload']: probability += 0.2 if 'email' in interfaces and method == 'email_attachment': probability += 0.3 if 'usb' in interfaces and method == 'removable_media': probability += 0.4 return min(probability, 0.95) def _get_detection_difficulty(self, method: str) -> str: """Get detection difficulty for exfiltration method""" difficulty_map = { 'encrypted_tunnel': 'high', 'dns_tunneling': 'medium', 'steganography': 'high', 'cloud_upload': 'medium', 'email_attachment': 'low', 'removable_media': 'low' } return difficulty_map.get(method, 'medium') def _estimate_exfiltration_time(self, volume_gb: int, method: str) -> str: """Estimate time to exfiltrate data""" speed_map = { 'encrypted_tunnel': '1GB per minute', 'dns_tunneling': '10MB per hour', 'steganography': '100MB per hour', 'cloud_upload': '500MB per minute', 'email_attachment': '25MB per transmission', 'removable_media': 'minutes for full transfer' } if volume_gb == 0: return 'immediate' speed = speed_map.get(method, '1GB per minute') return f"Estimated {volume_gb}GB at {speed}" def _identify_segmentation_issues(self, movement_paths: List[Dict]) -> List[str]: """Identify network segmentation issues""" issues = [] if len(movement_paths) > 5: issues.append('Insufficient network segmentation') high_prob_paths = [p for p in movement_paths if p['success_probability'] > 0.7] if len(high_prob_paths) > 2: issues.append('Critical systems not properly isolated') return issues def _assess_compliance_impact(self, data_sources: Dict) -> Dict: """Assess compliance impact of data exposure""" compliance_impacts = { 'GDPR': {'data_types': ['personal_data'], 'penalty': '4% global revenue'}, 'HIPAA': {'data_types': ['health_data'], 'penalty': '$1.5M per violation'}, 'PCI-DSS': {'data_types': ['financial_data'], 'penalty': 'monthly fines'} } impacts = {} for framework, details in compliance_impacts.items(): relevant_data = [dt for dt in data_sources.keys() if any(rdt in dt.lower() for rdt in details['data_types'])] if relevant_data: impacts[framework] = { 'risk_level': 'high', 'penalty_range': details['penalty'], 'affected_data_types': relevant_data } return impacts def _analyze_attack_surface(self, demonstrations: List[Dict]) -> Dict: """Analyze overall attack surface""" attack_vectors = [] for demo in demonstrations: exploit_type = demo.get('exploit_type', 'unknown') if exploit_type in ['sql_injection', 'xss']: attack_vectors.append('web_application') elif exploit_type in ['privilege_escalation', 'lateral_movement']: attack_vectors.append('infrastructure') elif exploit_type == 'data_exfiltration': attack_vectors.append('data_management') return { 'primary_attack_surfaces': list(set(attack_vectors)), 'total_attack_surface_size': len(attack_vectors), 'critical_vectors': [v for v in attack_vectors if v in ['web_application', 'infrastructure']], 'recommended_monitoring': ['web_app_firewall', 'network_monitoring', 'data_loss_prevention'] } def _analyze_exploit_chains(self, demonstrations: List[Dict]) -> List[Dict]: """Analyze potential exploit chains""" chains = [] # Chain 1: SQL Injection -> Data Exfiltration sql_demo = next((d for d in demonstrations if d.get('exploit_type') == 'sql_injection'), None) data_demo = next((d for d in demonstrations if d.get('exploit_type') == 'data_exfiltration'), None) if sql_demo and data_demo: chains.append({ 'chain_id': 'chain_1', 'description': 'SQL Injection leading to Data Exfiltration', 'steps': [ 'Exploit SQL injection vulnerability', 'Gain database access', 'Extract sensitive data', 'Exfiltrate via covert channels' ], 'likelihood': 'high' if len(sql_demo.get('vulnerabilities_found', [])) > 0 else 'medium', 'impact': 'critical', 'mitigation': ['Patch SQL injection', 'Implement DLP'] }) # Chain 2: Privilege Escalation -> Lateral Movement priv_demo = next((d for d in demonstrations if d.get('exploit_type') == 'privilege_escalation'), None) lat_demo = next((d for d in demonstrations if d.get('exploit_type') == 'lateral_movement'), None) if priv_demo and lat_demo: chains.append({ 'chain_id': 'chain_2', 'description': 'Privilege Escalation enabling Lateral Movement', 'steps': [ 'Exploit local privilege escalation', 'Gain elevated system access', 'Move laterally to other systems', 'Establish persistence' ], 'likelihood': 'medium', 'impact': 'critical', 'mitigation': ['System hardening', 'Network segmentation'] }) return chains def _assess_business_impact(self, demonstrations: List[Dict]) -> Dict: """Assess overall business impact""" total_financial_impact = 0 operational_impact = 'minimal' reputation_impact = 'low' for demo in demonstrations: if 'business_impact' in demo: impact = demo['business_impact'] if 'financial_impact_estimate' in impact: # Extract numeric value from financial impact string financial_str = impact['financial_impact_estimate'].replace('$', '').replace(',', '') try: total_financial_impact += int(financial_str) except ValueError: pass # Update impact levels if impact.get('reputation_risk') == 'high': reputation_impact = 'high' elif impact.get('reputation_risk') == 'medium' and reputation_impact == 'low': reputation_impact = 'medium' if total_financial_impact > 500000: operational_impact = 'severe' elif total_financial_impact > 100000: operational_impact = 'significant' elif total_financial_impact > 0: operational_impact = 'moderate' return { 'total_financial_impact': f"${total_financial_impact:,}", 'operational_impact': operational_impact, 'reputation_impact': reputation_impact, 'business_continuity_risk': 'high' if operational_impact in ['severe', 'significant'] else 'medium' } def _create_remediation_roadmap(self, demonstrations: List[Dict]) -> List[Dict]: """Create prioritized remediation roadmap""" roadmap_items = [] for demo in demonstrations: exploit_type = demo.get('exploit_type', 'unknown') if 'mitigation_strategies' in demo: roadmap_items.append({ 'vulnerability_type': exploit_type, 'priority': 'critical' if exploit_type in ['sql_injection', 'privilege_escalation'] else 'high', 'remediation_steps': demo['mitigation_strategies'], 'estimated_timeframe': 'immediate' if exploit_type in ['sql_injection'] else '30_days' }) elif 'defensive_measures' in demo: roadmap_items.append({ 'vulnerability_type': exploit_type, 'priority': 'high' if exploit_type == 'xss' else 'medium', 'remediation_steps': demo['defensive_measures'], 'estimated_timeframe': '30_days' if exploit_type == 'xss' else '90_days' }) return sorted(roadmap_items, key=lambda x: ( {'critical': 0, 'high': 1, 'medium': 2}[x['priority']], x['estimated_timeframe'] )) def _identify_security_gaps(self, demonstrations: List[Dict]) -> List[str]: """Identify security gaps across demonstrations""" gaps = [] exploit_types = [d.get('exploit_type', '') for d in demonstrations] if 'sql_injection' in exploit_types: gaps.append('Input validation and parameterized queries not implemented') if 'xss' in exploit_types: gaps.append('Content Security Policy and output encoding missing') if 'privilege_escalation' in exploit_types: gaps.append('System hardening and least privilege principles not enforced') if 'lateral_movement' in exploit_types: gaps.append('Network segmentation and monitoring insufficient') if 'data_exfiltration' in exploit_types: gaps.append('Data Loss Prevention and access controls inadequate') return gaps def _prioritize_fixes(self, demonstrations: List[Dict]) -> List[Dict]: """Prioritize fixes based on risk and impact""" priorities = [] critical_exploits = ['sql_injection', 'privilege_escalation', 'data_exfiltration'] high_exploits = ['xss', 'lateral_movement'] for demo in demonstrations: exploit_type = demo.get('exploit_type', '') if exploit_type in critical_exploits: priority = 1 elif exploit_type in high_exploits: priority = 2 else: priority = 3 priorities.append({ 'exploit_type': exploit_type, 'priority_level': priority, 'urgency': 'critical' if priority == 1 else 'high' if priority == 2 else 'medium', 'resources_required': 'dedicated_team' if priority == 1 else 'security_team', 'business_justification': 'prevents_critical_data_loss' if priority == 1 else 'reduces attack surface' }) return sorted(priorities, key=lambda x: x['priority_level']) def main(): """Demonstrate comprehensive exploit testing capabilities""" exploit_demo = ExploitDemonstration() print("🎯 SECURITY EXPLOIT DEMONSTRATION FRAMEWORK") print("=" * 60) print("Purpose: Educational security testing and vulnerability assessment") print("All demonstrations are for legitimate security testing purposes\n") # Sample test data for demonstrations test_sql_params = { 'username': "admin' OR '1'='1", 'password': 'password123', 'user_id': '1; DROP TABLE users; --' } test_web_forms = { 'comment': '', 'search_query': '', 'user_profile': 'javascript:alert("XSS")' } test_system_config = { 'file_permissions': 'world_writable', 'sudo_config': {'passwordless_sudo': True}, 'services': { 'web_server': {'run_as_root': True, 'vulnerable': True}, 'database': {'run_as_root': False, 'vulnerable': False} } } test_network_map = { 'workstation-001': { 'compromised': True, 'connections': ['server-001', 'database-001'], 'authentication_required': False, 'network_segmented': False, 'monitoring_level': 'low', 'services': ['ssh', 'web'] }, 'server-001': { 'compromised': False, 'connections': ['database-001', 'file-server-001'], 'authentication_required': True, 'network_segmented': False, 'monitoring_level': 'medium', 'services': ['database', 'file_sharing'] } } test_data_sources = { 'personal_data': { 'volume_gb': 100, 'protection_level': 0.3, 'accessible_interfaces': ['web', 'api'] }, 'financial_data': { 'volume_gb': 50, 'protection_level': 0.5, 'accessible_interfaces': ['database', 'api'] } } # Perform exploit demonstrations demonstrations = [] print("1️⃣ SQL INJECTION DEMONSTRATION") print("-" * 40) sql_demo = exploit_demo.demonstrate_sql_injection(test_sql_params) demonstrations.append(sql_demo) print(f"Vulnerabilities Found: {len(sql_demo['vulnerabilities_found'])}") print(f"Exploit Success Rate: {sql_demo['exploit_success_rate']:.2%}") print("\n2️⃣ XSS EXPLOITATION DEMONSTRATION") print("-" * 40) xss_demo = exploit_demo.demonstrate_xss_exploitation(test_web_forms) demonstrations.append(xss_demo) print(f"XSS Findings: {len(xss_demo['xss_findings'])}") print(f"Session Compromise Risk: {xss_demo['session_compromise_risk']}") print("\n3️⃣ PRIVILEGE ESCALATION DEMONSTRATION") print("-" * 40) priv_demo = exploit_demo.demonstrate_privilege_escalation(test_system_config) demonstrations.append(priv_demo) print(f"Escalation Vectors: {len(priv_demo['escalation_vectors'])}") print(f"System Compromise Risk: {priv_demo['system_compromise_risk']}") print("\n4️⃣ LATERAL MOVEMENT DEMONSTRATION") print("-" * 40) lat_demo = exploit_demo.demonstrate_lateral_movement(test_network_map) demonstrations.append(lat_demo) print(f"Movement Paths: {len(lat_demo['movement_paths'])}") print(f"Potentially Compromised Hosts: {len(lat_demo['potentially_compromised_hosts'])}") print("\n5️⃣ DATA EXFILTRATION DEMONSTRATION") print("-" * 40) data_demo = exploit_demo.demonstrate_data_exfiltration(test_data_sources) demonstrations.append(data_demo) print(f"Exfiltration Scenarios: {len(data_demo['exfiltration_scenarios'])}") print(f"Overall Data Risk Score: {data_demo['overall_data_risk_score']:.1f}/10") # Generate comprehensive report print("\n📊 GENERATING COMPREHENSIVE EXPLOIT REPORT") print("=" * 60) exploit_report = exploit_demo.generate_exploit_report(demonstrations) print(f"Report ID: {exploit_report['report_id']}") print(f"Total Exploit Vectors: {exploit_report['executive_summary']['total_exploit_vectors_identified']}") print(f"Critical Exploits: {exploit_report['executive_summary']['critical_exploits']}") print(f"Average Exploitability: {exploit_report['executive_summary']['average_exploitability']}") print(f"Overall Risk Rating: {exploit_report['executive_summary']['overall_risk_rating']}") # Save report to file report_filename = f"exploit_demonstration_report_{exploit_demo.session_id}.json" with open(report_filename, 'w') as f: json.dump(exploit_report, f, indent=2) print(f"\n✅ Demonstration complete! Report saved to: {report_filename}") print(f"🔍 Security Gaps Identified: {len(exploit_report['security_gaps_identified'])}") print(f"🛡️ Priority Fixes: {len(exploit_report['priority_fixes'])}") # Display priority fixes print("\n🚨 IMMEDIATE PRIORITY FIXES:") for fix in exploit_report['priority_fixes'][:3]: print(f"• {fix['exploit_type']}: {fix['urgency']} - {fix['business_justification']}") return exploit_report if __name__ == "__main__": demonstration_report = main()