Trusted Execution Environments (TEEs) provide hardware-based secure computing
through isolated execution zones within processors where sensitive code and
data remain protected even from privileged system software. Think of them as a
vault inside your CPU where sensitive code runs protected from everything else.This isolation addresses computational integrity by ensuring that code
executes exactly as intended without tampering, while privacy is maintained
through encryption of both data in memory and computational results.
Secure Enclaves
Encrypted and isolated regions of memory and computation, inaccessible to
the OS and other applications.
Hardware Isolation
CPU extensions like Intel SGX or ARM TrustZone to enforce hardware-level isolation.
Performant for general computation Suitable for real-time applications Hardware-based security guarantees Requires trust in chip manufacturers Vulnerable to hardware attacks
Zero-Knowledge Proofs
Pure cryptographic guarantees No hardware trust assumptions Stronger theoretical security High computational overhead Limited to specific computations
The zkTLS protocol enables proving properties about TLS (used by HTTPS)
connections using zero-knowledge proofs, allowing a client to demonstrate to a
verifier that they successfully established a secure connection with a
particular server and exchanged specific data, without revealing the private
communication contents.This centralized trust model fundamentally conflicts with blockchain’s
decentralized trust assumptions, where no single entity should have privileged
authority and all protocol rules should be verifiable by any participant.
While TEEs like Intel SGX and ARM TrustZone provide robust security guarantees
for CPU-based operations, they lack native support for securing GPU compute
workloads, creating a critical gap for blockchain applications that require
high-performance parallel processing.This limitation is particularly acute for applications like AI model inference
and cryptographic operations that could benefit from GPU acceleration. The
absence of GPU support in TEEs means that any computation requiring GPU
acceleration must either be performed outside the trusted environment
(sacrificing security guarantees) or be executed much less efficiently on CPUs
within the enclave.Additionally, the lack of standardized protocols for GPU attestation means that
even if hardware manufacturers were to implement GPU TEE support, significant
work would be needed to integrate these capabilities into existing blockchain
verification frameworks.
Ritual introduces a TEE-based proof of TLS provenance which improves upon
zkTLS’s trust model by having the TLS connection terminate inside a secure
enclave.The TEE can generate cryptographic attestations proving that a legitimate TLS
connection was established and specific data was exchanged, while keeping
private keys and session data confidential. Rather than trusting a central
verifier, participants would only need to trust the TEE manufacturer’s
attestation keys (which, while still centralized, presents a smaller attack
surface and aligns better with existing web
PKI trust
assumptions).The TEE could validate TLS certificates against multiple root certificate
authorities and generate succinct proofs of valid certificate chains, allowing
decentralized verification of TLS properties without exposing private
communication. This approach provides many of the same guarantees as zkTLS but
with trust assumptions that are more compatible with blockchain protocols.
Ritual introduces an innovative approach to extend TEE-based verifiable
computation to GPU workloads, specifically focusing on AI inference.The protocol uses a CPU-based TEE to orchestrate and attest to GPU operations,
creating a secure bridge between the trusted CPU environment and the untrusted
but high-performance GPU.The TEE verifies inputs, manages cryptographic operations, and validates GPU
computation results by checking specific properties and invariants of the GPU
output - essentially creating a verified sandbox for GPU workloads.This architecture enables Ritual to maintain the security properties of TEEs
while unlocking the massive parallel processing capabilities of GPUs for AI
inference. For instance, the TEE can verify model weights, validate input
preprocessing, and ensure the integrity of the inference results without
requiring direct GPU attestation capabilities.This hybrid approach provides a practical solution for bringing GPU-accelerated
AI inference into the realm of verifiable computation, enabling new use cases in
decentralized AI inference while maintaining strong security guarantees through
the TEE’s attestation capabilities.