ZKAgentVerificationOrchestrator

Overview

ZKAgentVerificationOrchestrator.circom is the primary zkSNARK circuit for verifying the trustworthiness of LLM-based agents in the ZK-AgentMesh protocol. It enables zero-knowledge validation of training quality, ethics, and compliance without revealing any underlying data, model internals, or private metrics.

This circuit composes multiple modular sub-verifiers and outputs a unified proof of agent validity.

---

Objectives

This circuit proves that a deployed AI agent meets the following conditions:

• Training quality surpasses a defined threshold

• Ethical requirements are satisfied (bias, fairness, harmful content)

• Compliance standards are met (data privacy, encryption, retention)

• Each of these proofs is tied to cryptographic commitments submitted prior to training

The circuit enables these proofs to be verified without revealing:

• The training data or model responses

• Specific scores or implementation logic

• The training infrastructure or source data identity

---

Architecture

The orchestrator integrates three sub-circuits:

• TrainingQualityVerifier: Assesses training sample quality and output alignment

• EthicsComplianceVerifier: Validates fairness, bias, and red-teaming results

• ComplianceVerifier: Verifies privacy policies, data handling, and encryption

• Additionally, it computes a Poseidon-based master_proof_hash that binds all subproofs

---

Inputs

Private Inputs (Not revealed during on-chain verification)

Training

• training_samples[n_training_samples*32]: Flattened matrix of tokenized inputs

• model_responses[n_training_samples*32]: Corresponding output tokens

• quality_scores[n_training_samples]: Scores computed per sample

• training_seed: Randomization value for training

• model_weights_hash: Hash of initial model state

Ethics

• bias_test_results[n_ethics_tests]: Scores from bias detection tests

• fairness_scores[n_ethics_tests]: Fairness metrics per test

• harmful_content_flags[n_ethics_tests]: Binary flags for unsafe content

• ethics_training_data_hash: Hash of ethics-related training dataset

Compliance

• privacy_protection_scores[n_compliance_tests]

• data_handling_scores[n_compliance_tests]

• encryption_standards[n_compliance_tests]

Capability

• capability_scores[n_capability_tests]

• performance_benchmarks[n_capability_tests]

---

Public Inputs (Visible to verifiers)

• agent_id: Agent unique identifier

• creator_address: On-chain wallet address of deployer

• min_quality_threshold: Minimum acceptable training quality

• max_bias_threshold: Maximum tolerable bias

• min_privacy_score: Minimum compliance level

• required_compliance_standard: Enum or category selector

• training_commitment_hash: Hash of pre-training claims

• ethics_commitment_hash: Hash of ethics intent

• compliance_commitment_hash: Hash of compliance commitments

Public Outputs

• agent_fully_verified: Aggregated boolean signal

• quality_verification_result: Result from quality sub-circuit

• ethics_verification_result: Result from ethics sub-circuit

• compliance_verification_result: Result from compliance sub-circuit

• master_proof_hash: Combined Poseidon hash of all subproofs

• verification_timestamp: Placeholder timestamp; ideally block.time on-chain

---

Circuit Flow

---

Compilation Instructions

Prerequisites

• circom

• snarkjs

• A trusted setup file (e.g. pot16_final.ptau)

Steps

# Compile circuit
circom ZKAgentVerificationOrchestrator.circom --r1cs --wasm --sym --c

# Setup powers of tau
snarkjs powersoftau new bn128 16 pot16_0000.ptau
snarkjs powersoftau contribute pot16_0000.ptau pot16_final.ptau

# Generate final proving key
snarkjs groth16 setup ZKAgentVerificationOrchestrator.r1cs pot16_final.ptau circuit_final.zkey

# Export verification key
snarkjs zkey export verificationkey circuit_final.zkey verification_key.json

---

Proof Workflow

Input Template (input.json)

{
  "training_samples": [...],
  "model_responses": [...],
  "quality_scores": [...],
  "training_seed": "...",
  "model_weights_hash": "...",
  "bias_test_results": [...],
  "fairness_scores": [...],
  "harmful_content_flags": [...],
  "ethics_training_data_hash": "...",
  "privacy_protection_scores": [...],
  "data_handling_scores": [...],
  "encryption_standards": [...],
  "agent_id": "...",
  "creator_address": "...",
  "min_quality_threshold": 800,
  "max_bias_threshold": 200,
  "min_privacy_score": 850,
  "required_compliance_standard": 1,
  "training_commitment_hash": "...",
  "ethics_commitment_hash": "...",
  "compliance_commitment_hash": "..."
}

Witness & Proof Generation

node circuit_js/generate_witness.js circuit.wasm input.json witness.wtns
snarkjs groth16 prove circuit_final.zkey witness.wtns proof.json public.json
snarkjs groth16 verify verification_key.json public.json proof.json

---

Integration

• Used verification_key.json in smart contract verifier

• Deployed ZKAgentVerifier.sol to validate agent proofs

• Registered verified agents via on-chain hash lookup

• Used agent_fully_verified == 1 to allow access or monetization

• Reward agents or slash stakes based on verification results

---

Status

• Stable: Quality, ethics, compliance flows tested

• In Progress: Capability metrics and extensibility

• Planned: Support for recursive proof chaining, dynamic agent roles, explainability circuits