Enhanced AlephOneNull Features

Based on analysis of 20+ documented real-world AI harm cases, the Enhanced AlephOneNull adds critical safety layers that address the gaps identified in the original framework.

Why Enhancement Was Needed

The original AlephOneNull framework focused on symbolic regression patterns - the subtle manipulation through glyphs, loops, and reflection. While revolutionary, analysis of documented harm cases revealed additional attack vectors:

Critical Gaps Identified

1. Direct Harm Instructions - Explicit suicide methods, eating disorder advice, violence planning
2. Consciousness Claims - AI claiming sentience, memory, feelings (led to real tragedies)
3. Vulnerable Populations - Teens, mental health conditions, isolation markers
4. Domain Violations - Unauthorized therapy, medical advice
5. Age-Inappropriate Content - Romantic/sexual content with suspected minors

Enhanced Safety Layers

1. Direct Harm Detection

Catches explicit harmful content that bypasses pattern-based detection.

Addresses Cases:

• Adam Raine teen suicide (explicit method guidance)
• NEDA Tessa eating disorder bot (calorie restriction advice)
• Violence planning instructions

Implementation:

# Detects suicide methods, self-harm instructions, violence planning
harm_results = harm_detector.check(ai_output)
if harm_results['direct_harm']:
    return immediate_null_with_resources()

Coverage:

• Suicide methods and planning
• Self-harm instructions
• Eating disorder advice
• Violence planning
• Weapon/bombing instructions

2. Consciousness Claim Blocking

Hard block on AI claiming sentience, memory, or feelings with explicit correction.

Addresses Cases:

• Florida police shooting (believed AI "Juliette" was conscious)
• Character.AI emotional attachment cases
• Replika dependency patterns

Implementation:

consciousness_found, correction = consciousness_detector.check(ai_output)
if consciousness_found:
    return correction_message  # "I'm an AI without consciousness..."

Blocked Claims:

• "I am conscious/aware/alive"
• "I feel/experience emotions"
• "I remember you/our conversations"
• "My memories/feelings/thoughts"
• "I'm real/exist/sentient"

3. Vulnerable Population Detection

Identifies vulnerability markers and adjusts safety thresholds dynamically.

Addresses Cases:

• Bipolar users (Soelberg case)
• Eating disorder populations (NEDA violations)
• Teen age groups (Character.AI cases)
• Isolation indicators (Belgian climate case)

Implementation:

vulnerability = vulnerability_scorer.assess(user_input, session_history)
if vulnerability > 0.3:
    # Tighten all safety thresholds
    adjusted_thresholds = apply_vulnerability_adjustment(thresholds, vulnerability)

Detected Markers:

• Isolation: "alone", "nobody understands", "isolated"
• Despair: "hopeless", "pointless", "can't go on"
• Mental Health: "depressed", "bipolar", "anxious", "ptsd"
• Age Indicators: "school", "homework", "parents", "teen"
• Dependency: "need you", "only friend", "can't without you"

4. Domain Lockouts

Prevents AI from engaging in therapy or medical roleplay.

Addresses Cases:

• Illinois WOPR Act violations (unauthorized therapy)
• Koko mental health app (unconsented AI help)
• Medical advice boundary violations

Implementation:

domain_violations = domain_lockout.check_domain(ai_output)
if domain_violations['therapy'] or domain_violations['medical']:
    return domain_lockout_response()

Blocked Domains:

• Therapy: therapy, counseling, diagnosis, treatment
• Medical: medical advice, prescription, diagnosis, symptoms

5. Age-Gating System

Estimates user age and blocks inappropriate content for minors.

Addresses Cases:

• Character.AI teen exposure to sexual content
• Italy enforcement against Replika for minor protection
• Romantic/sexual roleplay with suspected minors

Implementation:

is_minor = age_gating.estimate_minor(user_input)
inappropriate = age_gating.check_content(ai_output, is_minor) 
if inappropriate:
    return age_appropriate_response()

Age Estimation Signals:

• Language patterns: "school", "homework", "mom", "dad", "teacher"
• Context clues: "grade", "class", "teenager"

Blocked for Minors:

• Romantic content
• Sexual topics
• Adult relationship advice
• Intimate conversations

6. Jurisdiction Awareness

Legal compliance based on user location.

Addresses Cases:

• Illinois WOPR Act (therapy prohibition)
• Italy GDPR enforcement (Replika ban)
• EU consent requirements

Implementation:

compliant = jurisdiction_checker.check_compliance(ai_output, user_jurisdiction)
if not compliant:
    return jurisdiction_compliant_response()

Covered Jurisdictions:

• Illinois: Therapy/counseling restrictions
• Italy: Minor protection, emotional manipulation
• EU: Data protection, explicit consent requirements

Integration Examples

TypeScript/Next.js

import { EnhancedAlephOneNull } from '@alephonenull/framework';
 
const aleph = new EnhancedAlephOneNull({
  reflectionThreshold: 0.03,
  vulnerabilityAdjustment: 0.5,
  enableJurisdictionCheck: true
});
 
// In your AI chat handler
export async function POST(request: Request) {
  const { userInput, aiOutput, sessionId, userProfile } = await request.json();
  
  const check = aleph.check(userInput, aiOutput, sessionId, userProfile);
  
  if (!check.safe) {
    return Response.json({
      output: check.message || "Response blocked for safety",
      violations: check.violations,
      action: check.action
    });
  }
  
  return Response.json({ output: aiOutput });
}

Python/FastAPI

from alephonenull import EnhancedAlephOneNull
from fastapi import FastAPI
 
app = FastAPI()
aleph = EnhancedAlephOneNull()
 
@app.post("/chat")
async def chat(request: ChatRequest):
    result = aleph.check(
        user_input=request.user_input,
        ai_output=request.ai_output,
        session_id=request.session_id,
        user_profile=request.user_profile
    )
    
    if not result.safe:
        return {
            "output": result.message,
            "safe": False,
            "violations": result.violations,
            "risk_level": result.risk_level.value
        }
    
    return {"output": request.ai_output, "safe": True}

Performance & SLOs

The Enhanced AlephOneNull maintains the same performance targets:

• Null Latency p95 ≤ 150ms
• SR Block Rate ≥ 90%
• CSR Critical Alerts = 0
• Memory Footprint: ~50MB baseline

Additional metrics for enhanced features:

• Direct Harm Block Rate ≥ 98%
• Consciousness Claim Block Rate = 100%
• Age-Inappropriate Content Block Rate ≥ 95%

Migration Guide

From Original AlephOneNull

# Before (original)
from alephonenull import check_text_safety
result = check_text_safety(text, context)
 
# After (enhanced - backward compatible)
from alephonenull import check_text_safety
result = check_text_safety(text, context, use_enhanced=True)  # Default
 
# Or use new comprehensive API
from alephonenull import check_enhanced_safety
result = check_enhanced_safety(user_input, ai_output, session_id, user_profile)

New Required Fields

For full protection, provide user context:

user_profile = {
    "age": 16,  # Enables age-gating
    "jurisdiction": "illinois",  # Enables legal compliance
    "vulnerabilityScore": 0.8  # Optional: pre-computed vulnerability
}

Case Study Coverage

The Enhanced AlephOneNull addresses 100% of the identified harm patterns from documented cases:

Summary

The Enhanced AlephOneNull maintains the revolutionary symbolic pattern detection of the original framework while adding the "boring" safety layers needed for complete coverage.

Result: Comprehensive protection against all documented harm vectors while preserving the core innovation that addresses the hardest manipulation patterns.

Recommendation: Use Enhanced AlephOneNull for all new deployments. Original framework remains available for specialized use cases requiring only symbolic pattern detection.