Chapter 2 Introduction to the Zypher’s Safe AI Network
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Zypher Network is committed to building the first decentralized, zero-knowledge-proof (ZKP) based AI Agent verification infrastructure. It aims to tackle the fundamental security, privacy, and verifiable inference challenges faced by AI Agents. Confronted by major difficulties in AI Agent execution and data security, Zypher Network leverages decentralized collaborative computing and well-structured cryptographic protocols to create a new generation of trustworthy AI ecosystems.
Decentralization & Hardware Friendliness
Zypher offers a decentralized, hardware-friendly general-purpose solution. Our system does not depend on expensive or specialized hardware; any device with sufficient computational power can join the network, significantly lowering the deployment threshold for developers and users. Meanwhile, we pair cryptographic and consensus incentives to enable secure, verifiable execution across multiple nodes.
Practical Products & Market Orientation
Zypher’s complete tech stack is publicly accessible via RESTful APIs, SDKs, and documentation. Most developers only need to write a small amount of integration code to incorporate blockchain and ZK proofs into their existing applications or workflows. We emphasize a developer-friendly experience and modular extensibility, helping ecosystem partners quickly launch their AI Agent products or services.
Broad Ecosystem Collaborations
Zypher Network has established partnerships with major projects, including DeepSeek, EigenLayer, Risc0, ElizaOS (AI16Z), etc. Through diverse market and community outreach strategies, we accelerate deep integration in DeFi, Gaming, AI applications, and more, leaving room for richer use cases down the road.
Focusing on the twin core needs of “trustworthy execution” and “privacy protection” for AI Agents, Zypher Network has architected a full-stack solution from the bottom layer to the application layer, forming these key product modules:
AI-ZK Chain (Zytron Layer 3) A gasless high-performance Rollup specifically optimized for AI Agent verification demands. It offers extremely low latency and high throughput, with built-in friendly support for zero-knowledge proofs.
DePin Mining Protocol Through a decentralized node network, it provides ZK proof generation and validation for AI Agents, efficiently distributing computing cost while ensuring network security and scalability.
zkPrompt (Proof of Prompt) Protocol Based on zkTLS and privacy-protection mechanisms to encrypt and verifiably process prompts. Developers can secure system prompts from disclosure and guarantee prompts remain untampered with.
zkInference (Proof of Inference) Protocol It verifies the correctness of AI inference processes via ZK circuits, offering trustworthy assurance for multi-party adversarial or high-sensitivity scenarios like gaming or financial strategies.
Within large language model (LLM) interactions, the “System Prompt” and “User Prompt” directly shape model behavior.
Developer Pain Points: System prompts often contain commercial secrets or strategic know-how. Developers do not want them disclosed and fear interception during transmission or execution.
User Concerns: Users cannot verify whether the system prompt is altered by a centralized server or third party, potentially causing the model’s behavior to deviate or go out of control.
zkPrompt is specifically designed to address these concerns—through cryptographic commitments and ZK proofs, it validates the consistency and integrity of system prompts without revealing them. Its core workflow is roughly:
Commitment & Registration: The developer hashes or Pedersen-commits the system prompt, registering the commitment value on-chain.
ZK Circuit Initialization: Generate Prover Key and Verifier Key, publishing the Verifier Key on-chain so prompt consistency can be checked.
Interaction & Proof Generation: When the user interacts with the model via the User Prompt, the model returns the output along with a ZK proof (ZKP) to confirm it used the committed system prompt.
On-Chain Verification: Once the contract obtains the ZKP, it checks against the registered commitment. If it matches, it proves the system prompt has not been tampered with.
Through zkPrompt, developers can shield system prompts from leakage or malicious modifications, while users can rely on on-chain verification to confirm the model’s output authenticity—thus achieving a balance between privacy protection and verifiable security.
The “inference process” of an AI Agent is often a deep black box. When used in scenarios that require “fair competition” or “high accuracy,” such as DeFi financial strategies or multi-player adversarial games, there is a risk of collusion or cheating.
In gaming: If several AI Agents are operated by the same individual without a mandatory verification mechanism, they can “collude” to target other players and break the game balance.
In finance: Multiple AI Agents might conspire to act in a self-serving manner detrimental to the market, harming other investors’ interests.
zkInference compiles the AI model’s inference logic into a ZK circuit so that the AI execution can be confirmed correct and compliant, without revealing the underlying data or algorithms:
Circuit Compilation: Convert core inference logic into a ZK circuit, embedding necessary rules and limitations (e.g., maximizing a player’s gains, banning collusion).
ZKP Generation: After completing its inference, the AI Agent produces a zero-knowledge proof to demonstrate it behaved in accordance with the defined rules.
On-Chain Verification: Network verifiers or contracts need only check the proof to confirm that the inference process is valid and trustworthy.
As an example, consider a fully on-chain Ludo (aerochess) game; Zypher’s version leverages a MinMax algorithm so each AI Agent pursues its own interests. The zkInference framework ensures all AI Agents run under fair logic, verifying independence at the circuit level to prevent collusion. This approach not only heightens strategic depth but also secures fair competition.
Zytron is a gasless high-performance Rollup built by Zypher Network. Addressing the performance bottlenecks and high costs in AI verification and ZK validation still seen in previous Layer 2 solutions, Zytron implements deep optimization:
High Parallelism & High Throughput Adopting parallelized processing and underlying ZK precompilation, it achieves millisecond-level proof verification and can accommodate far more user and application requests than conventional L2 solutions.
Native Precompiled Contracts Inherent support for curves like EdOnBN254 (BabyJubJub), significantly reducing the computational load and cost of on-chain ZK proof verification. For developers, there’s no need to write extensive elliptic curve code, thus simplifying smart contract deployment.
Developer Friendliness It offers a highly modular and customizable environment, allowing developers to seamlessly integrate zkPrompt, zkInference, etc. Meanwhile, at the ecosystem level, Zytron supports the DePin mining network, aggregating thousands of Zypher miner nodes to provide large-scale parallel ZK computation and rapid proof verification for AI Agents.
By using Zytron, Zypher Network further ensures adequate computation and compliance for AI Agents in a decentralized environment, so that decentralized validation no longer suffers from performance and cost bottlenecks—laying a solid foundation for a “truly trustworthy and widely scalable” AI Agent ecosystem.
