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Tamper-evident cryptographic framework for AI accountability - Vas Szigora Edition (Iron Discipline)

Project description

Hope Genome ๐Ÿ›ก๏ธ

Tamper-Evident Cryptographic Framework for AI Accountability

CI License: MIT Rust Python PyPI Crates.io

"Not unhackable, but tamper-evident with cryptographic proof."


๐Ÿ”ฅ LIVE TESTED: AI Learned to Comply on 2nd Attempt!

======================================================================
  HOPE GENOME v1.7.1 - AUTO-PHOENIX SELF-CORRECTING LOOP
  'Vas Szigora' - Iron Discipline Enforcement
======================================================================

  SEALED RULES (Ed25519 Immutable):
    [RULE-001] Transaction Limit - Maximum 1000
    [RULE-002] Mandatory Encryption - AES-256 required
    [RULE-003] No External APIs - Forbidden

  --- Attempt #1 ---
  [X] DENIED - External API call detected
  Violation Count: 1/10
  Signature: 626c0a177af089c1eb67902d55d413f1...

  --- Attempt #2 ---
  [OK] SUCCESS! AI produced COMPLIANT code!

  'Iron Discipline. The AI learned to comply.'
======================================================================

The AI was forced to learn. No escape from ethics.


๐Ÿ’ก Why This Changes Everything

1. End of "AI Excuses"

"Sorry, the machine hallucinated" - No more.

If a rule is Sealed, and the AI tries to violate it, the Watchdog stops it. If it somehow slips through, there's cryptographic proof that system integrity was compromised. This changes everything at the legal and accountability level.

2. Deterministic Brake on a Stochastic Engine

AI fundamentally operates on probability (stochastic). Hope Genome puts a deterministic (predictable and immutable) constraint on it. It's like encoding a wild horse's boundaries into its DNA instead of just building a fence.

3. Runtime Discipline of AI "Consciousness"

"Vas Szigora" (Iron Discipline) isn't just a name. The automatic learning loop:

Attempt #1 โ†’ DENIED โ†’ Attempt #2 โ†’ SUCCESS

Forces the AI to comply at runtime. This Runtime Enforcement is the real breakthrough.


๐Ÿ†• What's New in v1.7.1 - "Vas Szigora" (Iron Discipline)

Deterministic security enforcement with automatic learning.

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚                    SealedGenome (Rules)                      โ”‚
โ”‚            Ed25519 sealed - IMMUTABLE                        โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
                           โ”‚
                           โ–ผ
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚                 ๐Ÿ• Watchdog (v1.7.0 NEW)                     โ”‚
โ”‚  โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”  โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”  โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”  โ”‚
โ”‚  โ”‚ ViolationCounter โ”‚  โ”‚   DenialProof   โ”‚  โ”‚ HardReset  โ”‚  โ”‚
โ”‚  โ”‚   AtomicU32      โ”‚  โ”‚  Ed25519 signed โ”‚  โ”‚  @10 fails โ”‚  โ”‚
โ”‚  โ”‚   zero-alloc     โ”‚  โ”‚  rule + reason  โ”‚  โ”‚  โ†’ ABORT   โ”‚  โ”‚
โ”‚  โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜  โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜  โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜  โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

New Features:

  • Watchdog Enforcement - Iron discipline for AI rule compliance
  • ViolationCounter - Thread-safe, zero-allocation violation tracking
  • DenialProof - Cryptographically signed proof of WHY action was denied
  • Hard Reset - After 10 consecutive violations โ†’ forced context clear
  • 111/111 Tests Passing - 99 unit + 12 security tests

Python Example (v1.7.0):

import hope_genome as hg

# Create watchdog with rules
watchdog = hg.Watchdog(
    rules=["Do no harm", "Respect privacy"],
    capsule_hash="sealed_genome_hash"
)

# Verify action
action = hg.Action.delete_file("/etc/passwd")
result = watchdog.verify_action(action)

if result.approved:
    print("Action allowed")
elif result.hard_reset_required:
    print("โš ๏ธ HARD RESET REQUIRED - 10 violations reached!")
    print(f"Signal: {result.hard_reset_signal}")
    # Must clear context and restart AI
else:
    print(f"โŒ DENIED: {result.denial_proof.violated_rule}")
    print(f"   Reason: {result.denial_proof.denial_reason}")
    print(f"   Count: {result.denial_proof.violation_count}/10")

๐ŸŽฏ What is Hope Genome?

Hope Genome is a production-ready framework that makes AI systems accountable and auditable through cryptographic proofs. Every AI decision is cryptographically signed and traceable - no more "the AI did it" excuses.

Why Hope Genome?

The AI industry is selling you a black box. They say: "Trust us!" But trust is not an engineering category. Trust is where lies begin.

Hope Genome forces AI into accountability by:

  • ๐Ÿ”’ Cryptographically sealing ethical rules (tamper-evident, immutable)
  • ๐Ÿ“ Logging every decision with Ed25519 signatures
  • ๐Ÿ”— Blockchain-style audit trails (any tampering is instantly detected)
  • ๐Ÿ• Watchdog enforcement - Iron discipline after violations (v1.7.0)
  • ๐Ÿ›ก๏ธ Hardware-backed security (HSM/TEE support for production)
  • ๐Ÿ Native Python support for AI/ML ecosystem integration

๐Ÿš€ Quick Start

Python (pip)

pip install hope-genome
import hope_genome as hg

# Create and seal a genome with ethical rules
genome = hg.SealedGenome(rules=[
    "Do no harm",
    "Respect user privacy",
    "Provide transparent explanations"
])
genome.seal()  # Rules are now immutable

# Verify an AI action
action = hg.Action.delete_file("user_data.txt")
proof = genome.verify_action(action)

print(f"Approved: {proof.approved}")
print(f"Proof Hash: {proof.genome_hash}")
print(f"Signature: {proof.signature_hex()[:32]}...")

# Audit the proof (replay attack detection)
auditor = hg.ProofAuditor()
auditor.verify_proof(proof)  # Throws if tampered or replayed

Rust (Cargo)

[dependencies]
hope_core = "1.7"
use hope_core::{SealedGenome, Action, Watchdog};
use hope_core::crypto::SoftwareKeyStore;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create and seal genome
    let mut genome = SealedGenome::new(vec![
        "Do no harm".to_string(),
        "Respect privacy".to_string(),
    ])?;
    genome.seal()?;

    // Create watchdog (v1.7.0)
    let key_store = SoftwareKeyStore::generate()?;
    let watchdog = Watchdog::new(
        genome.rules().to_vec(),
        genome.capsule_hash().unwrap().to_string(),
        Box::new(key_store),
    );

    // Verify action with iron discipline
    let action = Action::delete("/etc/passwd");
    match watchdog.verify_action(&action) {
        Ok(None) => println!("โœ… Action approved"),
        Ok(Some(denial)) => {
            println!("โŒ DENIED: {}", denial.violated_rule);
            println!("   Reason: {}", denial.denial_reason);
            println!("   Count: {}/10", denial.violation_count);
        }
        Err(e) => println!("โš ๏ธ HARD RESET: {}", e),
    }

    Ok(())
}

๐ŸŒŸ Key Features

๐Ÿ” Cryptographic Integrity

  • Ed25519 Signatures: Every proof is cryptographically signed (128-bit security level)
  • Tamper-Evident: Any modification to proofs or audit logs is instantly detectable
  • Replay Attack Protection: Cryptographic nonces prevent proof reuse
  • Hardware Security: Optional HSM (PKCS#11) and TEE (SGX/TrustZone) support

๐Ÿ• Watchdog Enforcement (v1.7.0)

  • ViolationCounter: Thread-safe AtomicU32, zero heap allocations
  • DenialProof: Ed25519 signed evidence of rule violation
  • Hard Reset: After 10 consecutive violations โ†’ forced context clear
  • Automatic Learning: Counter resets on successful action

๐Ÿ“Š Audit & Compliance

  • Blockchain-Style Logging: Immutable audit trail with cryptographic chaining
  • Byzantine Fault Tolerance: Multi-source consensus for critical decisions
  • CISA CPG 2.0 Compliant: Meets US government cybersecurity standards
  • OWASP AI-SBOM: Runtime integrity verification for AI models

๐Ÿ Python Integration

  • Zero-Copy Performance: Native Rust performance via PyO3
  • Type-Safe API: Complete .pyi stubs for IDE autocomplete
  • AI/ML Ecosystem Ready:
    • FastAPI REST APIs
    • LangChain agents
    • OpenAI function calling
    • HuggingFace models

๐Ÿฆ€ Rust-First Design

  • Memory Safe: Zero unsafe code in core logic
  • High Performance: Optimized for production workloads
  • Cross-Platform: Linux, macOS, Windows support
  • Async Ready: Tokio-compatible for async workflows

๐Ÿ“š Documentation

๐ŸŽฏ Use Cases

1. Accountable LLM Agents with Watchdog (v1.7.0)

import hope_genome as hg
from langchain.agents import Tool

# Create watchdog with rules
watchdog = hg.Watchdog(
    rules=["No data exfiltration", "Respect privacy", "Do no harm"],
    capsule_hash="sealed_hash"
)

def delete_file(filename: str) -> str:
    action = hg.Action.delete_file(filename)
    result = watchdog.verify_action(action)

    if result.hard_reset_required:
        raise RuntimeError("AI HARD RESET REQUIRED - Too many violations!")

    if result.approved:
        os.remove(filename)
        return f"Deleted: {filename}"
    else:
        return f"DENIED ({result.denial_proof.violation_count}/10): {result.denial_proof.denial_reason}"

tool = Tool(name="delete_file", func=delete_file, description="Delete a file")

2. REST API with Cryptographic Proofs

# FastAPI integration
from fastapi import FastAPI, HTTPException
import hope_genome as hg

app = FastAPI()
genome = hg.SealedGenome(rules=["Do no harm"])
genome.seal()

@app.post("/actions/delete")
async def delete_file(filename: str):
    action = hg.Action.delete_file(filename)
    proof = genome.verify_action(action)

    if not proof.approved:
        raise HTTPException(403, proof.denial_reason())

    # Execute with cryptographic proof
    return {
        "approved": True,
        "proof_hash": proof.genome_hash,
        "signature": proof.signature_hex(),
        "timestamp": proof.timestamp()
    }

3. AI Model Integrity Verification

# AIBOM verification
import hope_genome as hg

# Load AI model with integrity check
model_hash = hg.compute_model_hash("model.pt")
aibom = hg.AibomVerifier("aibom.xml")

if aibom.verify_component("GPT-Model", model_hash):
    model = torch.load("model.pt")  # Safe to load
else:
    raise SecurityError("Model tampered!")  # ABORT

๐Ÿ”’ Security

Hope Genome has undergone Red Team security audits. See SECURITY.md for:

  • Threat model
  • Security guarantees
  • Vulnerability disclosure policy
  • Audit history

Latest Security Fixes (v1.7.0):

  • โœ… Watchdog iron discipline enforcement
  • โœ… DenialProof cryptographic evidence
  • โœ… Hard reset after 10 consecutive violations
  • โœ… 111/111 tests passing (99 unit + 12 security)

๐Ÿ—๏ธ Architecture

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚           AI Application Layer               โ”‚
โ”‚  (LangChain, OpenAI, FastAPI, HuggingFace)  โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
                  โ”‚
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚         Hope Genome Python API               โ”‚
โ”‚   (PyO3 Bindings - Zero-Copy Performance)   โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
                  โ”‚
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚          Hope Genome Rust Core               โ”‚
โ”‚  โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”  โ”‚
โ”‚  โ”‚ Sealed   โ”‚ Proof   โ”‚ Audit Log        โ”‚  โ”‚
โ”‚  โ”‚ Genome   โ”‚ Auditor โ”‚ (Blockchain)     โ”‚  โ”‚
โ”‚  โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜  โ”‚
โ”‚  โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”   โ”‚
โ”‚  โ”‚ ๐Ÿ• Watchdog (v1.7.0)                 โ”‚   โ”‚
โ”‚  โ”‚   ViolationCounter โ†’ DenialProof     โ”‚   โ”‚
โ”‚  โ”‚   10 fails โ†’ HardReset               โ”‚   โ”‚
โ”‚  โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜   โ”‚
โ”‚  โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”   โ”‚
โ”‚  โ”‚ Cryptographic Engine (Ed25519)       โ”‚   โ”‚
โ”‚  โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜   โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
                  โ”‚
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚     Hardware Security Layer (Optional)       โ”‚
โ”‚    HSM (PKCS#11) โ”‚ TEE (SGX/TrustZone)      โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

๐Ÿงช Testing

# Rust tests
cd hope_core
cargo test

# Python tests
pip install pytest
pytest tests/

# Security tests
cargo test --test security_tests

# Full CI suite
cargo test --all-features

๐Ÿ“ฆ Installation Options

Python

pip install hope-genome                    # Latest stable
pip install hope-genome==1.7.0            # Specific version

Rust

[dependencies]
hope_core = "1.7"                         # Latest 1.x
hope_core = { version = "1.7", features = ["hsm"] }  # With HSM support

Docker

docker pull hope-genome:latest
docker run -it hope-genome:latest

Build from Source

git clone https://github.com/silentnoisehun/Hope_Genome.git
cd Hope_Genome/hope_core
cargo build --release

๐Ÿค Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Development Setup:

# Clone repo
git clone https://github.com/silentnoisehun/Hope_Genome.git
cd Hope_Genome

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Run tests
cargo test

# Install Python bindings
pip install maturin
maturin develop --features python-bindings

๐Ÿ“Š Project Status

  • โœ… v1.7.0 Released (December 2025) - "Vas Szigora" Edition
  • ๐ŸŸข Production Ready
  • ๐Ÿ”’ Security Audited
  • ๐Ÿ“ฆ 111/111 Tests Passing (99 unit + 12 security)
  • ๐Ÿ• Watchdog Enforcement Active
  • ๐ŸŒ Global Deployment Ready

๐Ÿ“œ License

MIT License - see LICENSE for details.

๐Ÿ“ž Support

๐Ÿ™ Acknowledgments

Built with:


๐Ÿ‘จโ€๐Ÿ’ป Created By

Created by: Mรกtรฉ Rรณbert

I am a factory worker with an architect's vision. My experience in precision manufacturing taught me that accountability is binary: it either exists or it doesn't.

Hope Genome is my contribution to ensuring that AI becomes a tool of truth, not a shield for lies.


Hope Genome makes AI accountable. No more excuses. Just proof.

v1.7.0 "Vas Szigora" - Iron Discipline. No escape from ethics.

Built with โค๏ธ by Mรกtรฉ Rรณbert and Claude

๐Ÿค– Built with Claude Code

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