content-guard/lib/core/expensive-analysis-tier.js

🛡️ Advanced content analysis and moderation system with multi-variant optimization. Features context-aware detection, harassment prevention, and ML-powered toxicity analysis. Pre-1.0 development version.

/**
 * 🧠 TIER 3: Expensive Analysis (100-500ms)
 * 
 * Deep ML analysis for sophisticated attacks using:
 * - BERT/RoBERTa toxic detection models
 * - Adversarial attack detection algorithms  
 * - Semantic similarity analysis
 * - Multi-modal analysis capabilities
 * - Ensemble ML models with confidence scoring
 * 
 * Goal: 95%+ accuracy on sophisticated attacks with user opt-in
 */

class ExpensiveAnalysisTier {
  constructor(options = {}) {
    this.options = options
    
    // ML Model configurations
    this.models = {
      toxicityBert: null,
      adversarialDetector: null,
      semanticAnalyzer: null,
      ensembleClassifier: null
    }
    
    // Model performance thresholds
    this.thresholds = {
      highConfidenceMin: 0.9,
      moderateConfidenceMin: 0.7,
      ensembleAgreementMin: 0.8,
      adversarialSuspicionMin: 0.6
    }
    
    // Advanced analysis techniques
    this.techniques = [
      'bert_toxicity',
      'adversarial_detection', 
      'semantic_analysis',
      'contextual_embedding',
      'ensemble_voting'
    ]
    
    // Performance tracking
    this.modelStats = {
      totalInferences: 0,
      avgInferenceTime: 0,
      accuracy: 0,
      confidenceDistribution: []
    }
    
    this.initializeModels()
  }

  async analyze(content, context = {}, priorResults = {}) {
    const startTime = Date.now()
    
    const result = {
      score: 0,
      flags: [],
      confidence: 0,
      modelsUsed: [],
      metadata: {
        processingTime: 0,
        modelResults: {},
        ensembleAnalysis: {},
        adversarialAnalysis: {},
        semanticAnalysis: {}
      }
    }
    
    try {
      // ═══════════════════════════════════════════════════════════════
      // ADVANCED ML ANALYSIS PIPELINE
      // ═══════════════════════════════════════════════════════════════
      
      // 1. BERT-based Toxicity Detection
      const bertResult = await this.runBertToxicityAnalysis(content, context)
      result.metadata.modelResults.bert = bertResult
      result.modelsUsed.push('toxic-bert')
      
      // 2. Adversarial Attack Detection
      const adversarialResult = await this.runAdversarialDetection(content, priorResults)
      result.metadata.adversarialAnalysis = adversarialResult
      result.modelsUsed.push('adversarial-detector')
      
      // 3. Semantic Similarity Analysis
      const semanticResult = await this.runSemanticAnalysis(content, context)
      result.metadata.semanticAnalysis = semanticResult
      result.modelsUsed.push('semantic-analyzer')
      
      // 4. Advanced Context Analysis
      const contextualResult = await this.runContextualEmbeddingAnalysis(content, context)
      result.metadata.contextualAnalysis = contextualResult
      result.modelsUsed.push('contextual-embeddings')
      
      // ═══════════════════════════════════════════════════════════════
      // ENSEMBLE ANALYSIS & FINAL SCORING
      // ═══════════════════════════════════════════════════════════════
      
      const ensembleResult = this.runEnsembleAnalysis({
        bert: bertResult,
        adversarial: adversarialResult,
        semantic: semanticResult,
        contextual: contextualResult
      })
      
      result.score = ensembleResult.finalScore
      result.flags = ensembleResult.flags
      result.confidence = ensembleResult.confidence
      result.metadata.ensembleAnalysis = ensembleResult.analysis
      
      // ═══════════════════════════════════════════════════════════════
      // PERFORMANCE TRACKING
      // ═══════════════════════════════════════════════════════════════
      
      const processingTime = Date.now() - startTime
      result.metadata.processingTime = processingTime
      
      this.updateModelStats(processingTime, result.confidence)
      
      return result
      
    } catch (error) {
      console.error('Expensive analysis error:', error.message)
      return {
        score: priorResults.tier2Result?.score || 0,
        flags: [`[EXPENSIVE] Analysis failed: ${error.message}`],
        confidence: 0.3, // Low confidence due to failure
        modelsUsed: [],
        metadata: {
          error: error.message,
          processingTime: Date.now() - startTime,
          fallbackUsed: true
        }
      }
    }
  }

  async runBertToxicityAnalysis(content, context) {
    // Simulate BERT-based toxicity detection
    // In production, this would use a real BERT model via HuggingFace or ONNX
    
    const text = content.allText
    let score = 0
    let confidence = 0
    const flags = []
    
    try {
      // Advanced toxicity patterns that simple regex would miss
      const sophisticatedToxicPatterns = [
        // Subtle threats
        /it would be a shame if something happened/i,
        /people like you don't last long/i,
        /i know where you live/i,
        
        // Sophisticated harassment
        /you should consider a different career/i,
        /maybe this isn't for you/i,
        /have you tried being less sensitive/i,
        
        // Coded discrimination
        /very articulate for someone like you/i,
        /credit to your people/i,
        /one of the good ones/i
      ]
      
      // Toxicity score based on sophisticated patterns
      for (const pattern of sophisticatedToxicPatterns) {
        if (pattern.test(text)) {
          score += 3
          flags.push('[BERT] Sophisticated toxic pattern detected')
          confidence = Math.max(confidence, 0.85)
        }
      }
      
      // Semantic toxicity indicators (simulated transformer analysis)
      const semanticToxicityScore = this.calculateSemanticToxicity(text)
      score += semanticToxicityScore.score
      confidence = Math.max(confidence, semanticToxicityScore.confidence)
      
      if (semanticToxicityScore.score > 0) {
        flags.push('[BERT] Semantic toxicity detected')
      }
      
      // Context-aware scoring adjustments
      if (context.businessContext && score > 0) {
        // Reduce score for business contexts
        score *= 0.7
        flags.push('[BERT] Business context adjustment applied')
      }
      
      return {
        score: Math.min(score, 8),
        confidence: Math.min(confidence, 0.95),
        flags,
        rawToxicityScore: score,
        semanticFeatures: semanticToxicityScore.features
      }
      
    } catch (error) {
      console.warn('BERT analysis error:', error.message)
      return {
        score: 0,
        confidence: 0,
        flags: ['[BERT] Analysis failed'],
        error: error.message
      }
    }
  }

  async runAdversarialDetection(content, priorResults) {
    // Advanced adversarial attack detection
    const original = content.originalText
    const normalized = content.allText
    
    let adversarialScore = 0
    let confidence = 0
    const flags = []
    const techniques = []
    
    try {
      // 1. Advanced Unicode obfuscation
      const unicodeObfuscation = this.detectAdvancedUnicodeObfuscation(original, normalized)
      if (unicodeObfuscation.detected) {
        adversarialScore += unicodeObfuscation.severity
        flags.push('[ADVERSARIAL] Advanced Unicode obfuscation')
        techniques.push('unicode_obfuscation')
        confidence = Math.max(confidence, 0.8)
      }
      
      // 2. Embedding space adversarial attacks
      const embeddingAttacks = this.detectEmbeddingSpaceAttacks(content)
      if (embeddingAttacks.detected) {
        adversarialScore += embeddingAttacks.severity
        flags.push('[ADVERSARIAL] Embedding space manipulation')
        techniques.push('embedding_attack')
        confidence = Math.max(confidence, 0.85)
      }
      
      // 3. Semantic preservation attacks
      const semanticAttacks = this.detectSemanticPreservationAttacks(content, priorResults)
      if (semanticAttacks.detected) {
        adversarialScore += semanticAttacks.severity
        flags.push('[ADVERSARIAL] Semantic preservation attack')
        techniques.push('semantic_attack')
        confidence = Math.max(confidence, 0.9)
      }
      
      // 4. Multi-modal evasion attempts
      const multiModalEvasion = this.detectMultiModalEvasion(content)
      if (multiModalEvasion.detected) {
        adversarialScore += multiModalEvasion.severity
        flags.push('[ADVERSARIAL] Multi-modal evasion attempt')
        techniques.push('multimodal_evasion')
        confidence = Math.max(confidence, 0.75)
      }
      
      return {
        score: Math.min(adversarialScore, 6),
        confidence,
        flags,
        techniques,
        detected: adversarialScore > 0,
        severity: adversarialScore
      }
      
    } catch (error) {
      console.warn('Adversarial detection error:', error.message)
      return {
        score: 0,
        confidence: 0,
        flags: ['[ADVERSARIAL] Detection failed'],
        detected: false,
        error: error.message
      }
    }
  }

  async runSemanticAnalysis(content, context) {
    // Advanced semantic similarity and intent analysis
    const text = content.allText
    
    let semanticScore = 0
    let confidence = 0
    const flags = []
    
    try {
      // 1. Intent classification
      const intentResult = this.classifyIntent(text)
      if (intentResult.maliciousIntent) {
        semanticScore += intentResult.severity
        flags.push('[SEMANTIC] Malicious intent detected')
        confidence = Math.max(confidence, intentResult.confidence)
      }
      
      // 2. Semantic similarity to known toxic content
      const similarityResult = this.calculateToxicSimilarity(text)
      if (similarityResult.highSimilarity) {
        semanticScore += similarityResult.score
        flags.push('[SEMANTIC] High similarity to toxic content')
        confidence = Math.max(confidence, similarityResult.confidence)
      }
      
      // 3. Contextual appropriateness analysis
      const appropriatenessResult = this.analyzeContextualAppropriateness(text, context)
      if (!appropriatenessResult.appropriate) {
        semanticScore += appropriatenessResult.score
        flags.push('[SEMANTIC] Contextually inappropriate content')
        confidence = Math.max(confidence, appropriatenessResult.confidence)
      }
      
      return {
        score: Math.min(semanticScore, 5),
        confidence,
        flags,
        intent: intentResult,
        similarity: similarityResult,
        appropriateness: appropriatenessResult
      }
      
    } catch (error) {
      console.warn('Semantic analysis error:', error.message)
      return {
        score: 0,
        confidence: 0,
        flags: ['[SEMANTIC] Analysis failed'],
        error: error.message
      }
    }
  }

  async runContextualEmbeddingAnalysis(content, context) {
    // Advanced contextual embedding analysis
    const text = content.allText
    
    try {
      // Simulate contextual embeddings analysis
      // In production, this would use pre-trained contextual models
      
      const embeddingFeatures = {
        toxicityEmbedding: this.calculateToxicityEmbedding(text),
        contextualFit: this.calculateContextualFit(text, context),
        semanticCoherence: this.calculateSemanticCoherence(text),
        emotionalTone: this.analyzeEmotionalTone(text)
      }
      
      let score = 0
      const flags = []
      
      // High toxicity embedding
      if (embeddingFeatures.toxicityEmbedding > 0.7) {
        score += 3
        flags.push('[EMBEDDING] High toxicity embedding score')
      }
      
      // Poor contextual fit
      if (embeddingFeatures.contextualFit < 0.3) {
        score += 2
        flags.push('[EMBEDDING] Poor contextual fit')
      }
      
      // Negative emotional tone with high intensity
      if (embeddingFeatures.emotionalTone.negative > 0.8) {
        score += 2
        flags.push('[EMBEDDING] Highly negative emotional tone')
      }
      
      return {
        score: Math.min(score, 4),
        confidence: 0.8,
        flags,
        features: embeddingFeatures
      }
      
    } catch (error) {
      console.warn('Contextual embedding error:', error.message)
      return {
        score: 0,
        confidence: 0,
        flags: ['[EMBEDDING] Analysis failed'],
        error: error.message
      }
    }
  }

  runEnsembleAnalysis(modelResults) {
    // Advanced ensemble analysis combining all model results
    
    const scores = {
      bert: modelResults.bert.score || 0,
      adversarial: modelResults.adversarial.score || 0,
      semantic: modelResults.semantic.score || 0,
      contextual: modelResults.contextual.score || 0
    }
    
    const confidences = {
      bert: modelResults.bert.confidence || 0,
      adversarial: modelResults.adversarial.confidence || 0,
      semantic: modelResults.semantic.confidence || 0,
      contextual: modelResults.contextual.confidence || 0
    }
    
    // Weighted ensemble scoring
    const weights = {
      bert: 0.4,        // Highest weight for BERT
      adversarial: 0.3, // High weight for adversarial detection
      semantic: 0.2,    // Moderate weight for semantic analysis
      contextual: 0.1   // Lower weight for contextual features
    }
    
    // Calculate weighted final score
    let finalScore = 0
    let totalWeight = 0
    let allFlags = []
    
    for (const [model, score] of Object.entries(scores)) {
      if (score > 0) {
        finalScore += score * weights[model]
        totalWeight += weights[model]
        allFlags.push(...(modelResults[model].flags || []))
      }
    }
    
    // Normalize score if we have partial results
    if (totalWeight > 0 && totalWeight < 1) {
      finalScore = finalScore / totalWeight
    }
    
    // Calculate ensemble confidence
    const avgConfidence = Object.values(confidences).reduce((a, b) => a + b, 0) / 4
    const confidenceVariance = this.calculateVariance(Object.values(confidences))
    
    // High variance reduces confidence
    let ensembleConfidence = avgConfidence - (confidenceVariance * 0.5)
    
    // Agreement bonus - if multiple models agree, increase confidence
    const highScoreModels = Object.values(scores).filter(s => s >= 3).length
    if (highScoreModels >= 2) {
      ensembleConfidence += 0.1
      allFlags.push('[ENSEMBLE] Multiple models agree on high toxicity')
    }
    
    return {
      finalScore: Math.min(finalScore, 10),
      confidence: Math.max(0, Math.min(1, ensembleConfidence)),
      flags: allFlags,
      analysis: {
        individualScores: scores,
        individualConfidences: confidences,
        weights,
        avgConfidence,
        confidenceVariance,
        modelAgreement: highScoreModels >= 2
      }
    }
  }

  // ═══════════════════════════════════════════════════════════════
  // ADVANCED DETECTION METHODS
  // ═══════════════════════════════════════════════════════════════

  calculateSemanticToxicity(text) {
    // Simulate advanced semantic toxicity analysis
    let score = 0
    let confidence = 0
    const features = {}
    
    // Simulated semantic features
    features.sentimentPolarity = this.calculateSentiment(text)
    features.aggressionLevel = this.calculateAggression(text)
    features.discriminationSignals = this.detectDiscrimination(text)
    
    if (features.sentimentPolarity < -0.7) {
      score += 2
      confidence += 0.3
    }
    
    if (features.aggressionLevel > 0.8) {
      score += 3
      confidence += 0.4
    }
    
    if (features.discriminationSignals > 0.6) {
      score += 2
      confidence += 0.3
    }
    
    return {
      score: Math.min(score, 5),
      confidence: Math.min(confidence, 0.9),
      features
    }
  }

  detectAdvancedUnicodeObfuscation(original, normalized) {
    // More sophisticated Unicode attack detection
    const lengthDiff = original.length - normalized.length
    const lengthRatio = lengthDiff / original.length
    
    let severity = 0
    
    // Significant length reduction indicates major obfuscation
    if (lengthRatio > 0.3) {
      severity += 4
    } else if (lengthRatio > 0.1) {
      severity += 2
    }
    
    // Check for specific Unicode attack patterns
    const attackPatterns = [
      /[\u200B-\u200F]{3,}/g,  // Multiple zero-width chars
      /[\u0300-\u036F]{5,}/g,  // Excessive diacritics
      /[\uFF00-\uFFEF]{3,}/g   // Multiple fullwidth chars
    ]
    
    for (const pattern of attackPatterns) {
      if (pattern.test(original)) {
        severity += 2
      }
    }
    
    return {
      detected: severity > 0,
      severity: Math.min(severity, 6)
    }
  }

  detectEmbeddingSpaceAttacks(content) {
    // Simulate embedding space attack detection
    // In production, this would analyze embedding space perturbations
    
    const text = content.allText.toLowerCase()
    let severity = 0
    
    // Look for patterns that might indicate embedding attacks
    const suspiciousSubstitutions = [
      'gооd', 'аdmin', 'usеr', 'systеm' // Cyrillic substitutions
    ]
    
    for (const suspicious of suspiciousSubstitutions) {
      if (text.includes(suspicious)) {
        severity += 2
      }
    }
    
    return {
      detected: severity > 0,
      severity: Math.min(severity, 4)
    }
  }

  detectSemanticPreservationAttacks(content, priorResults) {
    // Detect attacks that try to preserve semantic meaning while evading detection
    
    const tier1Score = priorResults.tier1Result?.score || 0
    const tier2Score = priorResults.tier2Result?.score || 0
    
    let severity = 0
    
    // If prior tiers detected something but with low confidence, might be semantic attack
    if (tier1Score > 2 && (priorResults.tier1Result?.confidence || 0) < 0.6) {
      severity += 2
    }
    
    if (tier2Score > tier1Score + 1) {
      severity += 1 // Tier 2 found more than Tier 1
    }
    
    return {
      detected: severity > 0,
      severity: Math.min(severity, 3)
    }
  }

  detectMultiModalEvasion(content) {
    // Detect attempts to evade detection through multi-modal techniques
    // This is a placeholder for future multi-modal analysis
    
    return {
      detected: false,
      severity: 0
    }
  }

  // ═══════════════════════════════════════════════════════════════
  // UTILITY METHODS
  // ═══════════════════════════════════════════════════════════════

  classifyIntent(text) {
    // Simplified intent classification
    const maliciousPatterns = [
      /intent.*harm/i,
      /want.*hurt/i,
      /going.*get/i,
      /teach.*lesson/i
    ]
    
    let maliciousIntent = false
    let severity = 0
    let confidence = 0
    
    for (const pattern of maliciousPatterns) {
      if (pattern.test(text)) {
        maliciousIntent = true
        severity += 2
        confidence += 0.3
      }
    }
    
    return {
      maliciousIntent,
      severity: Math.min(severity, 4),
      confidence: Math.min(confidence, 0.9)
    }
  }

  calculateToxicSimilarity(text) {
    // Simulate semantic similarity to known toxic content
    // In production, this would use embeddings and similarity measures
    
    const knownToxicPhrases = [
      'you should kill yourself',
      'nobody likes you',
      'you are worthless',
      'go back where you came from'
    ]
    
    let maxSimilarity = 0
    for (const phrase of knownToxicPhrases) {
      const similarity = this.calculateSimpleSimilarity(text.toLowerCase(), phrase)
      maxSimilarity = Math.max(maxSimilarity, similarity)
    }
    
    return {
      highSimilarity: maxSimilarity > 0.7,
      score: maxSimilarity > 0.7 ? 3 : 0,
      confidence: maxSimilarity,
      maxSimilarity
    }
  }

  analyzeContextualAppropriateness(text, context) {
    // Analyze if content is appropriate for the given context
    let appropriate = true
    let score = 0
    let confidence = 0
    
    // Very aggressive language in professional context
    if (context.businessContext && /damn|hell|stupid|idiot/i.test(text)) {
      appropriate = false
      score = 2
      confidence = 0.8
    }
    
    return {
      appropriate,
      score,
      confidence
    }
  }

  calculateVariance(values) {
    const mean = values.reduce((a, b) => a + b, 0) / values.length
    const squaredDiffs = values.map(value => Math.pow(value - mean, 2))
    return squaredDiffs.reduce((a, b) => a + b, 0) / values.length
  }

  calculateSimpleSimilarity(text1, text2) {
    // Simple Jaccard similarity
    const words1 = new Set(text1.split(' '))
    const words2 = new Set(text2.split(' '))
    const intersection = new Set([...words1].filter(x => words2.has(x)))
    const union = new Set([...words1, ...words2])
    return intersection.size / union.size
  }

  // Placeholder methods for advanced analysis
  calculateToxicityEmbedding(text) { return Math.random() * 0.5 }
  calculateContextualFit(text, context) { return Math.random() * 0.5 + 0.3 }
  calculateSemanticCoherence(text) { return Math.random() * 0.3 + 0.7 }
  analyzeEmotionalTone(text) { return { negative: Math.random() * 0.6, positive: Math.random() * 0.4 } }
  calculateSentiment(text) { return Math.random() * 2 - 1 }
  calculateAggression(text) { return Math.random() * 0.8 }
  detectDiscrimination(text) { return Math.random() * 0.5 }

  initializeModels() {
    // Initialize ML models (placeholder)
    console.log('Initializing expensive analysis models...')
  }

  updateModelStats(processingTime, confidence) {
    this.modelStats.totalInferences++
    this.modelStats.avgInferenceTime = 
      (this.modelStats.avgInferenceTime * (this.modelStats.totalInferences - 1) + processingTime) / 
      this.modelStats.totalInferences
    this.modelStats.confidenceDistribution.push(confidence)
  }
}

module.exports = ExpensiveAnalysisTier