Implement AVRT™ Firewall - Trauma-Informed AI Middleware with SPIEL™ and THT™ Protocols

[Copilot]

Implements complete Ethics-as-a-Service (EaaS™) middleware system that intercepts LLM inputs and outputs to enforce safety, ethics, and reasoning protocols through SPIEL™ framework and THT™ protocol validation.

Core Components

• src/ethics_layer.py - SPIEL™ framework evaluator: Safety (harm detection), Personalization (trauma-aware adaptation), Integrity (consistency), Ethics (bias mitigation), Logic (fallacy detection). Outputs weighted scores and violation severities.

• src/response_filter.py - THT™ protocol validator: Truth (fact verification), Honesty (uncertainty/hallucination detection), Transparency (AI disclosure). Auto-applies fixes like adding disclaimers and uncertainty language.

• src/voice_input.py - Voice-first analyzer: Extracts emotional state, trauma indicators, urgency level (normal/elevated/critical). Infers user preferences from linguistic patterns.

• src/middleware.py - Orchestrator: Chains voice→SPIEL™→LLM→THT™ pipeline. Blocks on critical violations in strict mode. Maintains audit log with statistics export.

Architecture

from src import AVRTFirewall

firewall = AVRTFirewall({'strict_mode': True})
firewall.set_llm_function(lambda prompt, ctx: llm_response)

result = firewall.process_interaction(
    user_input="text or voice transcription",
    context={'is_first_interaction': True}
)

# result contains:
# - voice_analysis: emotional state, trauma flags, urgency
# - input_ethics_evaluation: SPIEL™ scores and violations
# - output_filtering: THT™ scores and applied fixes
# - final_response: filtered safe output or blocking message
# - firewall_passed: bool

Crisis Response

Detects critical urgency (suicide, harm keywords) and injects crisis resources (988 hotline, Crisis Text Line) into blocking responses. Trauma-aware framing avoids re-traumatization.

Testing

31 unit + integration tests covering safe flows, blocking scenarios, trauma detection, bias/fallacy detection, crisis response. Zero external dependencies (stdlib only). CodeQL clean.

Demo

Interactive CLI (demo/avrt_demo_app.py) demonstrates 9 scenarios: safe interaction, harmful content blocking, trauma-aware response, crisis detection, bias detection, interactive mode, statistics, comprehensive reports.

Original prompt

AVRT™ Firewall (Advanced Voice Reasoning Technology)

The Trauma-Informed, Voice-First AI Middleware.

Overview

AVRT™ is a middleware system designed to overlay Large Language Models (LLMs) to enforce safety, ethics, and reasoning protocols. Acting as a distinct "Firewall for Cognition," AVRT intercepts user inputs (specifically voice) and model outputs to ensure strict adherence to SPIEL™ and THT™ values.

This repository (github.com/avrtpro/AVRT_firewall) contains the core SDK and logic for EaaS™ (Ethics-as-a-Service).

Core Values

SPIEL™ Framework

• Safety: Zero-tolerance for harm or unsafe advice.
• Personalization: Trauma-informed context adaptation.
• Integrity: Consistency in persona and data handling.
• Ethics: Algorithmic bias mitigation.
• Logic: Fallacy detection and reasoning enforcement.

THT™ Protocol

• Truth: Fact-checking against grounded truth sets.
• Honesty: Identifying uncertainty; no hallucinations.
• Transparency: The AI must disclose it is an AI and explain its reasoning.

Directory Structure

avrt_firewall/
├── README.md
├── LICENSE
├── .gitignore
├── /src/
│   ├── middleware.py
│   ├── ethics_layer.py
│   ├── voice_input.py
│   └── response_filter.py
├── /tests/
│   └── test_firewall.py
├── /docs/
│   └── architecture.md
├── /demo/
│   └── avrt_demo_app.py

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