## 2 Related Work
It is based on the RATS architecture proposed by the IETF, focusing on configuring the verifier with provisioning endorsements and trust anchors such as keys and certificates. Further, the Verifier is capable of validating evidence or attestation results using appraisal policies. It supports attestation for both the ARM CCA on the cca-ssd platform and the ARM PSA on the psa-iot platform. There is a need for TinyML devices to provide claims and follow the RATS format to enhance trust and security in embedded environments. Operating in resource-constrained, dynamic, and heterogeneous settings with models from various vendors, TinyML systems can use claims to communicate detailed, structured evidence of their state. This allows verifiers to assess their trustworthiness effectively. Verifiers can query the device state to extract evidence such as model parameters, firmware versions, or runtime behavior, and link these data with reference values or appraisal policies. GuaranTEE [29] is a solution that focuses on providing attestable and private ML execution on devices using ARM CCA, ensuring that ML models operate securely and privately within TEEs. The CCA architecture is specifically designed for high-performance processors like the Samsung Exynos 2200 and Qualcomm Snapdragon 8 series, which are based on Armv9-A architecture. This architecture is not adopted in resource-constrained embedded processors due to its higher performance requirements and associated costs. Moreover, the authors claim that the attestation mechanism as described in the CCA specification cannot be implemented on Arm's Fixed Virtual Platforms because the environment lacks the hardware security engine necessary to generate the platform attestation token. Table 1 below compares the attestation solutions discussed above, which are designed to ensure the integrity and trustworthiness of software executed on an MCU. It highlights each solution's objective, targeted platforms, and their applicability to ML or TinyML attestation. In this work, we introduce an ML attestation mechanism designed for edge devices and TinyML applications, integrating the PSA initial attestation token with a specialized ML attestation token (ML-EAT). This approach provides an end-to-end solution for systems where machine learning models need to be remotely verified, updated, and maintained, ensuring both platform integrity and model authenticity. The PSA token confirms platform \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Attestation Solution** & **Goal** & **Targeted Platforms** & **ML-Oriented** & **Suited for TinyML Attestation** \\ \hline PASTA & Attestation protocol for autonomous embedded systems & ESP32-PICO-KIT & No & No, attests only the device as a whole \\ \hline SEDA & Collective attestation protocol for large-scale swarms & Not specified & No & No, attests only the device as a whole \\ \hline SMART & External verification mechanism over an internal CPU memory region & AVR, MSP430 architectures & No & Can provide a basic HMAC attestation over the model weights, without any other details \\ \hline TrustLite & Generic hardware-enforced software isolation mechanism & Xlininx Virtex-6 FPGA & No & Can be used as a foundation layer to create a trustlet application that further implements TinyML attestation \\ \hline GuaranTEE & Attestation of ML models in dynamic trusted execution environments & High-performance platforms (e.g., Snapdragon 8 Gen) & Yes & Not suitable for resource-constrained devices \\ \hline This proposal & Attestation of ML models in resource-constrained devices & Low/Middle-range microcontrollers, TrustZone-aware that follow PSA hardware guidelines & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of attestation solutions for ensuring software integrity. Applicability to TinyML models. integrity, while the ML-EAT token attests to the correctness of the machine learning model running on the device.