Suspicious prompt inputs are logged for monitoring

Without logging suspicious prompt inputs, injection attempts are invisible—you have no way to detect patterns, identify targeted attacks, or tune your defenses based on real traffic. CWE-778 (Insufficient Logging) and NIST AI RMF MEASURE 2.6 both require that AI systems generate the evidence needed to assess their own security posture. NIST SP 800-53 AU-12 mandates audit logging for security-relevant events. Coordinated injection campaigns that probe hundreds of variations per day will go completely undetected without event logging, letting attackers iterate at low risk. Conversely, logging the full content of every message creates a GDPR and CCPA data minimization problem—the goal is targeted logging of anomalous events, not bulk prompt storage.

Log security events (not message content) when suspicious patterns are detected. The log entry needs enough context for investigation without storing the actual prompt text.

// src/lib/ai-security-logger.ts
export async function logSuspiciousInput(params: {
  sessionId: string        // not the user's name or email
  detectionType: 'jailbreak_pattern' | 'moderation_flagged' | 'length_exceeded'
  patternMatched?: string  // which regex fired, not the input itself
  messageLength: number
  endpoint: string
}) {
  await securityLogger.warn({
    event: 'suspicious_prompt_input',
    ...params,
    timestamp: new Date().toISOString()
  })
}

// Usage in your injection detection layer:
if (hasInjectionPattern(userMessage)) {
  await logSuspiciousInput({
    sessionId: session.id,
    detectionType: 'jailbreak_pattern',
    patternMatched: matchedPattern.source,
    messageLength: userMessage.length,
    endpoint: '/api/chat'
  })
  return Response.json({ error: 'Message not allowed' }, { status: 400 })
}

Route security events to a separate log sink from application logs so they can be monitored independently and retained on a different schedule.