Skip to content

Security: tunisch/AuthentiScan

Security

SECURITY.md

Security Model: AuthentiScan Prototype

This document provides a technical overview of the security architecture and trust assumptions for the AuthentiScan project. As an experimental prototype deployed on the Stellar Testnet, it is designed to demonstrate concepts rather than provide production-grade security guarantees.

Trust Architecture

The system utilizes a layered trust model where security is anchored at the ledger level.

graph TD
    subgraph "Client Environment (Untrusted)"
        U[End User]
        B[Local Hash Calculation]
    end

    subgraph "External Logic (Freighter Interface)"
        W[Freighter Wallet<br/>Transaction Auth]
        RPC[Soroban RPC<br/>Network Gateway]
    end

    subgraph "Stellar Testnet (Immutable Core)"
        SC[Smart Contract<br/>Prototype Logic]
        L[Stellar Ledger<br/>Permanent State]
    end

    U --> B
    B -->|content_hash + result| W
    W -->|signed transaction| RPC
    RPC --> SC
    SC -->|ledger write| L

    style SC fill:#d35400,stroke:#fff,stroke-width:2px
    style L fill:#27ae60,stroke:#fff,stroke-width:2px
Loading

Security Properties (Prototype)

Property Implementation Method Status
Content integrity SHA-256 Content Hash ✅ Demonstrated
Record immutability Stellar Ledger + Write-Once Logic ✅ Demonstrated
Submission Auth Freighter Wallet require_auth ✅ Enforced
Duplicate Prevention Hash-Based Storage Keys ✅ Enforced
Data Privacy Local Hashing (No video data on-chain) ✅ By Design

Key Disclosures

  • Non-Judicial Utility: The Verification Records anchored by this system are experimental indicators and do not constitute legal or judicial proof of authenticity.
  • Probabilistic Findings: Results from the Prototype AI Analysis Module are estimates. The blockchain anchors the finding, not the absolute truth.
  • No Formal Audit: This project has not undergone a professional third-party security audit.
  • Identity Sensitivity: The Content-Based Identity is derived from exact bitstreams. Visual duplicates with different encodings will produce different hashes.

Known Constraints

Constraint Explanation Impact
Re-encoding sensitivity Modern video platforms transcode uploads, changing the byte sequence. Visual duplicates may resolve to different Verification Records.
Simulated Indicators Forensic metrics are currently generated by a Prototype AI Analysis Module. Demonstrated vision of capability rather than real forensic detection.
Client Side Risks Hashing occurs in the user's browser environment. Potential for malicious input if the browser environment is compromised.

© 2026 AuthentiScan — Experimental Research Prototype

There aren’t any published security advisories