NovaQ Documentation
  • Welcome to NovaQ
  • Overview
    • Description
    • Vision
    • Market Opportunity
  • NovaQ Protocol
    • What is the NovaQ Protocol?
    • Protocol Architecture
    • Security Design
    • How the Protocol Works?
  • Novaq Ecosystem
    • Model Integrity Verifier
    • AI Prompt Optimizer
    • Smart API Response Tester
  • Roadmap Development
    • Short-Term Goals
    • Mid-Term Goals
    • Long-Term Goals
  • Others
    • Tokenomics
    • Links
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  1. Novaq Ecosystem

Smart API Response Tester

Smart API Response Tester

“Stress-test your AI before the real world does.”

NovaQ’s Smart API Response Tester is a versatile module that simulates how AI or backend APIs behave under normal, edge-case, and hostile input conditions — just like automated security fuzzing, but for inference and logic endpoints.

Core Functionalities:

  • Edge & Adversarial Prompt Libraries: Uses a curated and generative set of prompts that simulate:

    • Overlong queries

    • Ambiguous, contradictory, or recursive input

    • Injection-style attacks (e.g., prompt leakage)

    • Nonsensical, misformatted, or multi-language input

  • Latency & Failure Scenario Simulation: The tester measures how fast and reliably your system responds across various scenarios:

    • Cold-start latency

    • Load-induced slowdowns

    • Incomplete data outputs

    • Retry and fallback mechanisms

  • WASM-Like Logic Execution: Testing scripts behave like WebAssembly modules — light, secure, modular. These simulate interaction flows and failure branches with minimal overhead, ideal for stateless testing of production or staging environments.

  • Quantum-Adversarial Testing Models: Inspired by post-quantum cryptanalysis, this tester applies entropy-based prompt corruption to simulate how AI systems might degrade under unpredictable or high-noise environments.

Integrations:

  • Compatible with OpenAI, Anthropic, Hugging Face, and any REST-based inference endpoint.

  • Can run mock attacks on decentralized inference APIs to test protocol robustness.

Why It Matters:

  • Prevent embarrassing API crashes or hallucinations during production.

  • Ensure AI behavior is consistent even when users behave unexpectedly.

  • Prove resilience to quantum-era AI adversaries.

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Last updated 4 days ago