# One for All and All for One: GNN-based Control-Flow Attestation for Embedded Devices

arXiv: https://arxiv.org/abs/2403.07465 · HTML: https://mlsanity.com/paper/2403.07465/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 65 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Understanding graph embedding methods and their applications](https://mlsanity.com/paper/2012.08019/) (2020)
- [C-FLAT: Control-FLow ATtestation for Embedded Systems Software](https://mlsanity.com/paper/1605.07763/) (2016)
- [LO-FAT: Low-Overhead Control Flow ATtestation in Hardware](https://mlsanity.com/paper/1706.03754/) (2017)
- [OAT: Attesting Operation Integrity of Embedded Devices](https://mlsanity.com/paper/1802.03462/) (2018)
- [A Verified Architecture for Proofs of Execution on Remote Devices under Full Software Compromise](https://mlsanity.com/paper/1908.02444/) (2019)
- [ReCFA: Resilient Control-Flow Attestation](https://mlsanity.com/paper/2110.11603/) (2021)
- [Block Oriented Programming: Automating Data-Only Attacks](https://mlsanity.com/paper/1805.04767/) (2018)

## Built on by (most cited first, 1 of 1)

- [Walma: Learning to See Memory Corruption in WebAssembly](https://mlsanity.com/paper/2603.24167/) (2026)

## Abstract (arXiv)

Control-Flow Attestation (CFA) is a security service that allows an entity (verifier) to verify the integrity of code execution on a remote computer system (prover). Existing CFA schemes suffer from impractical assumptions, such as requiring access to the prover's internal state (e.g., memory or code), the complete Control-Flow Graph (CFG) of the prover's software, large sets of measurements, or tailor-made hardware. Moreover, current CFA schemes are inadequate for attesting embedded systems due to their high computational overhead and resource usage. In this paper, we overcome the limitations of existing CFA schemes for embedded devices by introducing RAGE, a novel, lightweight CFA approach with minimal requirements. RAGE can detect Code Reuse Attacks (CRA), including control- and non-control-data attacks. It efficiently extracts features from one execution trace and leverages Unsupervised Graph Neural Networks (GNNs) to identify deviations from benign executions. The core intuition behind RAGE is to exploit the correspondence between execution trace, execution graph, and execution embeddings to eliminate the unrealistic requirement of having access to a complete CFG. We evaluate RAGE on embedded benchmarks and demonstrate that (i) it detects 40 real-world attacks on embedded software; (ii) Further, we stress our scheme with synthetic return-oriented programming (ROP) and data-oriented programming (DOP) attacks on the real-world embedded software benchmark Embench, achieving 98.03% (ROP) and 91.01% (DOP) F1-Score while maintaining a low False Positive Rate of 3.19%; (iii) Additionally, we evaluate RAGE on OpenSSL, used by millions of devices and achieve 97.49% and 84.42% F1-Score for ROP and DOP attack detection, with an FPR of 5.47%.