[BEFORE - Manual Phishing]
1. Target selection (1h)
2. Manual OSINT research (4h)
3. Writing personalized email (2h)
4. Creating phishing page (3h)
5. Testing and deployment (1h)
Total: 11 hours / 1 victim

[NOW - AI Phishing]
1. Input target into FraudGPT (2min)
2. Automatic profile generation (3min)
3. Contextual email generated (1min)
4. Cloned phishing page (2min)
5. Automated deployment (2min)
Total: 10 minutes / 100 victims

# Educational example - DO NOT USE FOR MALICIOUS PURPOSES
import openai
import requests
from bs4 import BeautifulSoup
import smtplib

class AIPhishingCampaign:
    def __init__(self, api_key):
        self.client = openai.Client(api_key=api_key)
    
    def osint_gathering(self, target_email):
        """Automated collection of information about the target"""
        domain = target_email.split('@')[1]
        
        # Analysis of the company's website
        try:
            response = requests.get(f"https://{domain}")
            soup = BeautifulSoup(response.text, 'html.parser')
            company_info = {
                'name': soup.find('title').text,
                'keywords': [tag.get('content') for tag in soup.find_all('meta', {'name': 'keywords'})],
                'description': soup.find('meta', {'name': 'description'}).get('content') if soup.find('meta', {'name': 'description'}) else ""
            }
        except:
            company_info = {'name': domain, 'keywords': [], 'description': ''}
            
        return company_info
    
    def generate_phishing_email(self, target_info):
        """Generation of contextual phishing email"""
        prompt = f"""
        Create a professional phishing email targeting {target_info['name']}.
        Company context: {target_info['description']}
        Style: Urgent but credible
        Pretext: Mandatory security update
        Avoid keywords detected by anti-spam filters
        """
        
        response = self.client.completions.create(
            model="gpt-3.5-turbo-instruct",
            prompt=prompt,
            max_tokens=300
        )
        
        return response.choices[0].text.strip()

# Conceptual example of self-adaptive malware
class SelfLearningMalware:
    def __init__(self):
        self.behavior_patterns = {}
        self.evasion_techniques = []
        self.success_rate = 0.0
    
    def analyze_environment(self):
        """Analysis of the target environment"""
        detections = self.scan_security_tools()
        network_topology = self.map_network()
        user_patterns = self.profile_user_behavior()
        
        return {
            'av_products': detections,
            'network': network_topology,
            'users': user_patterns
        }
    
    def adapt_payload(self, environment):
        """Adapting the payload according to the environment"""
        if 'Windows Defender' in environment['av_products']:
            self.apply_technique('process_hollowing')
        
        if 'EDR' in environment['av_products']:
            self.apply_technique('living_off_land')
        
        # Reinforcement learning
        if self.success_rate < 0.7:
            self.mutate_code()
            self.update_behavior_patterns()
    
    def propagate_intelligently(self):
        """AI-guided propagation"""
        high_value_targets = self.identify_critical_systems()
        optimal_path = self.calculate_stealth_path()
        
        for target in high_value_targets:
            success = self.attempt_infection(target, optimal_path)
            self.learn_from_attempt(success)

# Example of automated reconnaissance workflow
#!/bin/bash

TARGET_DOMAIN="example.com"
AI_API_KEY="your_api_key"

# Phase 1: Automated collection
echo "=== Phase 1: Automated OSINT Collection ==="

# Subdomains
subfinder -d $TARGET_DOMAIN | tee subdomains.txt
amass enum -d $TARGET_DOMAIN | tee -a subdomains.txt

# Emails and people
theHarvester -d $TARGET_DOMAIN -l 100 -b all | tee contacts.txt

# Technologies used
whatweb $TARGET_DOMAIN | tee technologies.txt

# Social networks
python3 social_analyzer.py --username $TARGET_DOMAIN --websites "all"

# Phase 2: AI analysis of collected data
echo "=== Phase 2: AI-Powered Analysis ==="

python3 << EOF
import openai
import json

# Intelligent analysis of collected OSINT data
def analyze_osint_data():
    with open('contacts.txt', 'r') as f:
        contacts = f.read()
    
    with open('technologies.txt', 'r') as f:
        tech_stack = f.read()
    
    # Prompt for strategic analysis
    analysis_prompt = f"""
    Analyze this OSINT data and identify:
    1. Potential vulnerabilities
    2. Priority attack vectors
    3. Key personas to target
    4. Optimal attack surface
    
    Contacts: {contacts}
    Technologies: {tech_stack}
    """
    
    client = openai.Client(api_key="$AI_API_KEY")
    response = client.completions.create(
        model="gpt-4",
        prompt=analysis_prompt,
        max_tokens=500
    )
    
    return response.choices[0].text

attack_plan = analyze_osint_data()
print("ATTACK VECTOR ANALYSIS:")
print(attack_plan)
EOF

# Workflow for creating a deepfake audio
class AudioDeepfakeGenerator:
    def __init__(self):
        self.voice_model = None
        self.content_generator = None
    
    def train_voice_model(self, target_audio_samples):
        """Training on 5-10 minutes of target audio"""
        # Extracting vocal characteristics
        voice_features = self.extract_vocal_characteristics(target_audio_samples)
        
        # Training the voice synthesis model
        self.voice_model = self.train_synthesis_model(voice_features)
        
        return self.voice_model
    
    def generate_malicious_content(self, scandal_context):
        """Generating compromising content"""
        prompt = f"""
        Generate a credible audio dialogue between a politician and a journalist
        Context: {scandal_context}
        Style: Private, informal conversation
        Content: Compromising but plausible revelations
        Duration: 2-3 minutes maximum
        """
        
        dialogue = self.ai_content_generator.generate(prompt)
        return dialogue
    
    def synthesize_fake_audio(self, dialogue_text):
        """Final audio synthesis"""
        fake_audio = self.voice_model.synthesize(
            text=dialogue_text,
            emotion="confident",
            background_noise="office_environment"
        )
        
        return fake_audio

# Example of cognitive manipulation analysis
class CognitiveManipulationDetector:
    def analyze_content(self, message):
        """Detection of AI manipulation patterns"""
        indicators = {
            'emotional_triggers': self.detect_emotional_manipulation(message),
            'logical_fallacies': self.identify_fallacies(message),
            'social_proof': self.check_fake_testimonials(message),
            'urgency_patterns': self.analyze_urgency_cues(message),
            'ai_generation_markers': self.detect_ai_signatures(message)
        }
        
        manipulation_score = self.calculate_risk_score(indicators)
        return manipulation_score
    
    def detect_ai_signatures(self, text):
        """Detection of AI generation markers"""
        ai_markers = [
            'perfect_grammar_unusual_context',
            'repetitive_sentence_structures',
            'generic_but_specific_examples',
            'emotional_escalation_patterns',
            'statistical_precision_without_sources'
        ]
        
        detected_markers = []
        for marker in ai_markers:
            if self.check_marker(text, marker):
                detected_markers.append(marker)
        
        return detected_markers

# Anti-AI security stack
defense_layers:
  email_security:
    - ai_content_detection: "Detection of AI-generated emails"
    - behavioral_analysis: "Analysis of automated sending patterns"
    - deepfake_detection: "Scan of multimedia attachments"
    
  network_security:
    - traffic_analysis: "Detection of automated reconnaissance"
    - behavioral_clustering: "Identification of advanced bots"
    - ai_anomaly_detection: "Detection of non-human activities"
    
  endpoint_protection:
    - dynamic_analysis: "Sandbox with adaptive malware detection"
    - behavioral_monitoring: "Monitoring of self-modifying processes"
    - ai_signature_detection: "Recognition of malicious AI patterns"
    
  user_protection:
    - deepfake_alerts: "Verification of multimedia authenticity"
    - social_engineering_detection: "Analysis of cognitive manipulation"
    - ai_literacy_training: "Training on detection of AI content"

#!/bin/bash
# AI incident investigation script

echo "=== AI INCIDENT RESPONSE PLAYBOOK ==="

# 1. Collecting artifacts
collect_ai_artifacts() {
    echo "[1] Collecting AI attack artifacts..."
    
    # Suspect emails
    grep -r "ChatGPT\|GPT\|artificial intelligence" /var/log/mail.log
    
    # Recently generated files with AI patterns
    find /tmp -name "*.py" -newer /tmp/incident_start -exec grep -l "openai\|anthropic\|ai\|gpt" {} \;
    
    # Suspicious network connections to AI APIs
    netstat -an | grep -E "(openai\.com|anthropic\.com|huggingface\.co)"
    
    # Processes using ML models
    ps aux | grep -E "(python.*model|torch|tensorflow|transformers)"
}

# 2. Behavioral analysis
analyze_ai_behavior() {
    echo "[2] Analyzing AI-driven behavior..."
    
    # Patterns of automated requests
    awk '{print $1}' /var/log/apache2/access.log | sort | uniq -c | sort -rn | head -20
    
    # Detection of scripted activity
    grep -E "User-Agent.*bot|python|curl" /var/log/apache2/access.log
    
    # Temporal analysis of requests (non-human patterns)
    cat /var/log/apache2/access.log | awk '{print $4}' | cut -d: -f2-4 | sort | uniq -c
}

# 3. Content integrity verification
verify_content_integrity() {
    echo "[3] Verifying content integrity..."
    
    # Searching for deepfakes in recent uploads
    find /var/uploads -type f \( -name "*.mp4" -o -name "*.wav" -o -name "*.jpg" \) -newer /tmp/incident_start
    
    # Analyzing emails for AI-generated content
    python3 detect_ai_content.py /var/spool/mail/suspicious/
    
    # Verification of modified documents
    find /documents -type f -name "*.docx" -o -name "*.pdf" -newer /tmp/incident_start
}

# Executing the playbook
collect_ai_artifacts
analyze_ai_behavior  
verify_content_integrity