Blockchain-Enabled Agents using zkTLS

The emergence of blockchain-enabled AI agents has introduced exciting possibilities for decentralized autonomous systems. However, our previous discussions about production-ready blockchain AI agents revealed a significant limitation: while blockchain networks are designed to run on retail computers to maintain decentralization, modern AI agents often require high-end GPUs for running large language models. This fundamental tension threatens to centralize what should be a decentralized system.

This article explores how Zero-Knowledge Transport Layer Security (zkTLS) can help resolve this contradiction by enabling secure, verifiable interactions with external AI services while maintaining the decentralized ethos of blockchain networks.

The Challenge: Decentralization vs. Computational Requirements

In traditional blockchain systems, nodes and validators operate on consumer-grade hardware, ensuring broad participation and true decentralization. However, the integration of AI capabilities, particularly large language models, demands substantial computational resources that exceed what typical retail computers can provide. This creates a centralization pressure that goes against blockchain's core principles.

zkTLS: A Bridge Between Web2 and Web3

Zero-Knowledge Transport Layer Security (zkTLS) offers a potential solution to this dilemma. By enabling blockchain systems to securely interact with external AI services while providing cryptographic proof of the interaction, zkTLS allows us to maintain decentralization while leveraging powerful AI capabilities.

How zkTLS Works

zkTLS is a hybrid protocol that combines traditional TLS encryption with zero-knowledge proofs. It creates a secure gateway between Web2 private data (in our case, AI service APIs) and the Web3 ecosystem. The protocol ensures that:

1. The communication with the AI service is secure and authenticated

2. The responses from the AI service can be cryptographically verified

3. Sensitive information remains private while still providing proof of the interaction

Implementation Paradigms

zkTLS can be implemented through several different models, each with its own advantages and trade-offs:

TEE Model

Trusted Execution Environment (TEE) technology has been a cornerstone of secure computing for decades, as we explored in detail in our previous article. In the context of zkTLS, TEE provides a secure foundation for handling sensitive cryptographic operations and data verification.

The model enables zkTLS by utilizing a secure, isolated component within modern CPUs. This enclave provides a trusted environment for performing sensitive computations while shielding them from the broader system. When a client needs to verify server data, the process occurs within this protected environment, ensuring security and authenticity.

The process begins with the prover feeding raw server data into the TEE. Within the enclave, the TEE validates message content, handles authentication, and generates encrypted proofs. These proofs are then sent directly to the third-party protocol requesting verification, guaranteeing the authenticity of the server’s response.

This model is highly efficient. The TEE acts as the verifier, minimizing computational and network overhead. Its hardware-based security simplifies the process, eliminating the need for complex multi-party protocols or external verification services.

However, reliance on specific hardware presents limitations. Not all devices support TEE functionality, restricting accessibility. Additionally, TEEs, while secure, are not immune to sophisticated side-channel attacks targeting hardware vulnerabilities.

Projects like Town Crier and Clique exemplify the TEE model. Town Crier uses TEEs for secure oracle operations, while Clique leverages TEE-TLS for direct, verifiable off-chain TLS calls from smart contracts.

The TEE model offers high performance but faces challenges with hardware dependency and compatibility, driving research into broader zkTLS alternatives.

MPC Model

The Multi-Party Computation (MPC) model represents a sophisticated approach to implementing zkTLS, building upon fundamental concepts of secure computation that we explored in our previous discussion of secret sharing schemes. While that article focused on secret sharing for key management, the MPC model for zkTLS extends these principles to create a secure, distributed system for verifying TLS sessions.

The model enables a collaborative process between a Client and a Notary to securely establish a shared public key for TLS handshakes. This key is used to generate the Pre-Master Secret and Session Keys required for secure communication with a Server. The model innovatively splits the session key between the Client and Notary, transforming them into a "super-client" that complies with standard TLS protocols. From the Server’s perspective, this arrangement appears as a typical TLS connection, requiring no changes to existing infrastructure.

At session end, the Client creates an authenticated commitment to plaintext data, verified and signed by the Notary without accessing content. Privacy is preserved while maintaining verifiability. The Client then produces a zero-knowledge proof disclosing only necessary information.

Despite its strengths, the MPC model faces challenges, including high network latency, scaling difficulties, and significant resource demands. Each TLS session consumes a base 20MB, with additional costs for data processing. For example, handling a 1KB request and 100KB response can require uploading 32MB.

Projects like TLSNotary and DECO have advanced the MPC model, with DECO incorporating zk-SNARKs for selective proof claims without revealing sensitive data. While resource-intensive, the model is invaluable for scenarios like bypassing server restrictions on multi-IP requests, ensuring security and privacy.

Proxy Model

The Proxy Model introduces a novel approach to implementing zkTLS by leveraging familiar browser proxy functionality in a new way. In this model, when a user attempts to access a website, their browser routes the request through an HTTPS proxy. While this resembles traditional proxy servers, the key difference lies in the proxy's role. Instead of actively relaying or modifying the data, the proxy acts as a witness, observing and verifying the encrypted data exchange without decrypting or altering it.

The verification process is designed to ensure security while protecting sensitive data. During the initial connection, the proxy monitors the encrypted traffic between the client and server, but it does not have access to the decrypted content. This design preserves the confidentiality of the communication. After the session concludes, the client shares a portion of the session key with the proxy, allowing it to verify the communication's authenticity without revealing any private information. The client then generates a zero-knowledge proof for the response data, ensuring that both the proxy and any other interested parties can verify the TLS session’s contents without exposing sensitive data.

Reclaim Protocol, developed by the Questbook team in 2023, is a notable implementation of the Proxy Model. It uses an HTTP Proxy to witness both the handshake and subsequent data transfers, validating critical elements such as domain name verification and response integrity.

Despite its advantages, the Proxy Model faces challenges, including potential performance bottlenecks when proxy servers are under high load and the risk of server blocking. Additionally, generating high-performance zero-knowledge proofs, particularly when dealing with large inputs, can be computationally intensive. However, the model offers lower latency than the MPC Model, making it well-suited for large-scale applications. Ongoing work focuses on optimizing memory usage and proof generation efficiency to improve compatibility with modern browsers.

Hybrid Mode

The Hybrid Mode marks a significant advancement in zkTLS implementation, blending the strengths of both the MPC and Proxy models to overcome their individual limitations. This mode acknowledges that no single approach can meet the diverse challenges of modern web infrastructure, offering a more versatile and robust solution by adapting to different scenarios.

The key insight behind Hybrid Mode is its intelligent switching mechanism, which adjusts to varying conditions like network latency or server policies. Just as a thermostat adapts to temperature changes, the Hybrid Mode monitors conditions in real time and seamlessly switches between operational modes to ensure optimal performance. This adaptive approach guarantees that the system remains functional across a wide range of contexts.

A standout feature of Hybrid Mode is its approach to zero-knowledge proofs. It employs interactive Zero-Knowledge Proofs for memory efficiency in browsers with strict limitations. When needed, the system can switch to non-interactive Zero-Knowledge Proofs for on-chain verification, maintaining both security and efficiency regardless of the use case.

zkPass has played a pivotal role in advancing the Hybrid Mode. In 2022, their work on optimizing the MPC Model led to innovations like Silent OT and Stacked GC, which improved efficiency. Building on this, zkPass introduced VOLEitH technology in 2023, transforming ZK algorithms into non-interactive formats, enabling fast proof generation directly in browsers.

The practical implementation of the Hybrid Mode operates dynamically, selecting the appropriate mode based on network conditions, server policies, and client capabilities. However, managing multiple modes and ensuring intelligent switching requires sophisticated engineering and extended development timelines. Despite these challenges, the Hybrid Mode’s adaptability and efficiency make it ideal for large-scale zkTLS applications. It balances communication efficiency and computational resources, forming a solid foundation for future developments in verifiable network applications.

Application to AI Agents

The integration of zkTLS with blockchain-based AI agents opens up exciting possibilities for building truly decentralized systems that can leverage powerful AI capabilities. Let's explore how this works in practice, using real-world examples to illustrate the concepts.

Consider a decentralized application that needs to use ChatGPT's API for natural language processing. In a traditional setup, this would present a challenge: the blockchain network needs to trust that the AI responses actually came from OpenAI's API and weren't manipulated. This is where zkTLS provides an elegant solution.

When an AI agent needs to query the ChatGPT API, the process unfolds in several stages. First, the blockchain network maintains its decentralized nature by running lightweight nodes that handle the core blockchain operations. When AI capabilities are needed, instead of trying to run complex models locally, the system makes an API call to OpenAI's servers. Here's where zkTLS adds its magic: it generates cryptographic proof that the interaction with OpenAI's API actually occurred and that the response wasn't tampered with.

# Traditional API call

response = openai.Moderation.create(

    input="Text to moderate"

)

# With zkTLS verification

verified_response = zkTLS.verify_api_call(

    endpoint="https://api.openai.com/v1/moderations",

    request_data={"input": "Text to moderate"},

    api_key=OPENAI_API_KEY

)

The zkTLS layer provides cryptographic proof that:

1. The request was actually sent to OpenAI's API (not a fake endpoint)

2. The response came from OpenAI's servers (not a malicious actor)

3. The content wasn't modified in transit

This verification becomes particularly powerful when working with more complex AI services. Consider another example using Azure's Computer Vision API for image analysis. The blockchain network needs to verify not only that the API was called but also that the specific model version and parameters were used:

# Azure Computer Vision API call with zkTLS verification

verified_vision_response = zkTLS.verify_api_call(

    endpoint="https://your-resource.cognitiveservices.azure.com/vision/v3.2/analyze",

    request_data={

        "url": "image_url",

        "visualFeatures": ["Categories", "Description", "Objects"]

    },

    api_credentials=AZURE_CREDENTIALS

)

This architecture brings several powerful advantages. First, it allows the blockchain network to remain truly decentralized, as nodes don't need expensive GPUs to participate. Instead, they only need to verify the zkTLS proofs, which is a much lighter computational task. Second, it enables the network to access state-of-the-art AI models without compromising on security or decentralization principles.

The system can also scale AI capabilities independently of the blockchain infrastructure. For example, if a better language model becomes available, the network can switch to using it without requiring any changes to the underlying blockchain architecture. All that's needed is to update the API endpoint in the zkTLS verification layer.

Conclusion

zkTLS provides a crucial bridge between the decentralized world of blockchain and the computationally intensive requirements of modern AI systems. By enabling verifiable interaction with external AI services, it allows us to build truly decentralized AI agents without compromising on capabilities or security. As both blockchain and AI technologies continue to evolve, this architecture may become increasingly important for maintaining the balance between decentralization and computational power.