# A Unified Hardware-based Threat Detector for AI Accelerators

arXiv: https://arxiv.org/abs/2311.16684 · HTML: https://mlsanity.com/paper/2311.16684/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2023.

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

- [Runtime Detection of Adversarial Attacks in AI Accelerators Using Performance Counters](https://mlsanity.com/paper/2503.07568/) (2025)

## Abstract (arXiv)

The proliferation of AI technology gives rise to a variety of security threats, which significantly compromise the confidentiality and integrity of AI models and applications. Existing software-based solutions mainly target one specific attack, and require the implementation into the models, rendering them less practical. We design UniGuard, a novel unified and non-intrusive detection methodology to safeguard FPGA-based AI accelerators. The core idea of UniGuard is to harness power side-channel information generated during model inference to spot any anomaly. We employ a Time-to-Digital Converter to capture power fluctuations and train a supervised machine learning model to identify various types of threats. Evaluations demonstrate that UniGuard can achieve 94.0% attack detection accuracy, with high generalization over unknown or adaptive attacks and robustness against varied configurations (e.g., sensor frequency and location).