GuardCloudIMA: Extending Hardware Root of Trust to AI Workloads
How we built a privacy-preserving integrity measurement architecture for secure AI inference using Merkle Trees and Zero-Knowledge Proofs.
GuardCloud Research Team
December 2025
In This Article
- 1. Introduction: Why Hardware Root of Trust Matters
- 2. TPM and Linux IMA: The Foundation
- 3. The AI Infrastructure Challenge
- 4. The AI Challenge: Why Traditional IMA Falls Short
- 5. Introducing GuardCloudIMA
- 6. Privacy-Preserving Attestation with ZK-SNARKs
- 7. Integration with Trusted Execution Environments
- 8. Conclusion
1. Introduction: Why Hardware Root of Trust Matters
In modern computing environments—whether cloud, edge, or IoT—establishing trust is fundamental to security. But how do you trust a system that could be compromised at any layer? The answer lies in building a chain of trust anchored in tamper-resistant hardware.
A hardware root of trust provides the foundation for:
- ✓Boot & firmware security: Detect bootkits and rootkits by verifying every component loaded at startup
- ✓Remote attestation: Cryptographically prove a system's state to remote verifiers
- ✓Secrets protection: Bind encryption keys to a known-good system state
- ✓Zero-trust architecture: Tie network access and workload deployment to verified system integrity
The chain of trust starts at the hardware level—specifically the Trusted Platform Module (TPM)—and extends upward through each layer of the system.
2. TPM and Linux IMA: The Foundation
The Trusted Platform Module
The TPM is a tamper-resistant hardware component embedded in the motherboard. It provides secure cryptographic operations and protected storage through Platform Configuration Registers (PCRs)—special registers that can only be modified through a one-way extend operation.
Key TPM operations include:
SHA256(PCR[i] || new_value) — extends the measurement chain
Signed attestation of PCR values for remote verification
Bind data to specific PCR states
Linux Integrity Measurement Architecture (IMA)
IMA has been part of the Linux kernel since 2009. It measures and verifies the integrity of system components before they're loaded or executed.
IMA's core functionality includes:
- •Measure: Calculate SHA-256 hash of files before execution
- •Store: Maintain an append-only measurement list
- •Attest: Provide TPM-backed remote attestation
- •Appraise: Optional enforcement mode to block unauthorized code
3. The AI Infrastructure Challenge
Modern AI deployments have evolved from simple single-model servers to complex multi-model inference pipelines.
The Core Problem
All AI workloads share the same Linux kernel—and therefore the same IMA subsystem. This means a single kernel vulnerability becomes a single point of failure for all models.
4. The AI Challenge: Why Traditional IMA Falls Short
The "Userspace Fiction"
AI workloads are often described as isolated environments, but this isolation is largely a "userspace fiction." From the kernel's perspective, AI inference processes don't exist as first-class objects.
This architectural reality creates three critical gaps:
Limited AI Workload Autonomy
IMA uses centralized policy control—one policy for the entire system. Different AI workloads have different requirements.
Privacy Concerns
IMA's combined measurement list exposes all AI workloads' software inventories. This violates model confidentiality.
Lack of Granular Verification
Verifying a single AI workload requires processing the entire system's measurement list—potentially millions of entries.
5. Introducing GuardCloudIMA
GuardCloudIMA
Extends the hardware root of trust to AI workloads—without sacrificing autonomy, privacy, or verification efficiency.
The Two-Tree Architecture (2TA)
At the heart of GuardCloudIMA is the Two-Tree Architecture (2TA)—a novel approach that maintains both per-workload integrity trees and a system-wide aggregation.
AI Workload Trees (Tci)
Each AI workload maintains its own Merkle tree of file measurements.
- • Per-model measurement policies
- • Isolated integrity verification
- • O(log n) proof generation
System Tree (TM)
AI workload roots are aggregated into a system-wide Merkle tree.
- • Unified system integrity anchor
- • Single TPM PCR for all AI workloads
- • Composable verification
6. Privacy-Preserving Attestation with ZK-SNARKs
GuardCloudIMA uses ZK-SNARKs to prove AI workload integrity without revealing what's inside.
The Protocol
Request Attestation
The verifier sends a request with expected AI workload root and a fresh nonce.
Collect Witness Data
The prover finds the Merkle path from the AI workload's tree to root(TM).
Generate ZK Proof
The ZK-SNARK circuit proves a valid Merkle path exists—without revealing it.
Verify Response
The verifier checks: ZK proof validity, TPM signature, and nonce match.
What the Verifier Learns ✓
- • The AI workload's root hash is valid
- • It's part of the system tree
- • The TPM signed root(TM)
What Stays Private ✗
- • Individual file hashes
- • Merkle tree structure
- • Other AI workloads' data
7. Integration with Trusted Execution Environments
GuardCloudIMA is designed to work seamlessly with modern Trusted Execution Environments (TEEs).
VM-Level TEEs (SEV-SNP, TDX)
- ✓Granular per-model verification inside the VM
- ✓AI workload autonomy within TEE boundaries
- ✓End-to-end integrity chain from TPM to AI model
Process-Level TEEs (SGX)
- ✓Measurements stay inside the enclave
- ✓Only root(Tci) shared with kernel
- ✓Even untrusted kernel sees only roots
8. Conclusion
GuardCloudIMA represents a fundamental rethinking of how integrity measurement works in AI infrastructure environments.
Autonomy
Per-model policies and measurement trees
Privacy
ZK proofs hide model architectures
Efficiency
O(log n) verification at any granularity