# Acoustic Cybersecurity: Exploiting Voice-Activated Systems

arXiv: https://arxiv.org/abs/2312.00039 · HTML: https://mlsanity.com/paper/2312.00039/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 45 references that are in the map.
- First posted on arXiv 23 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [DolphinAtack: Inaudible Voice Commands](https://mlsanity.com/paper/1708.09537/) (2017)
- [CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition](https://mlsanity.com/paper/1801.08535/) (2018)
- [Single and Multi-Speaker Cloned Voice Detection: From Perceptual to Learned Features](https://mlsanity.com/paper/2307.07683/) (2023)
- [Are iPhones Really Better for Privacy? Comparative Study of iOS and Android Apps](https://mlsanity.com/paper/2109.13722/) (2021)
- [The Insecurity of Home Digital Voice Assistants -- Amazon Alexa as a Case Study](https://mlsanity.com/paper/1712.03327/) (2017)
- [A Survey on Amazon Alexa Attack Surfaces](https://mlsanity.com/paper/2102.11442/) (2021)
- [On the Feasibility of Acoustic Attacks Using Commodity Smart Devices](https://mlsanity.com/paper/2001.07157/) (2020)

## Abstract (arXiv)

In this study, we investigate the emerging threat of inaudible acoustic attacks targeting digital voice assistants, a critical concern given their projected prevalence to exceed the global population by 2024. Our research extends the feasibility of these attacks across various platforms like Amazon's Alexa, Android, iOS, and Cortana, revealing significant vulnerabilities in smart devices. The twelve attack vectors identified include successful manipulation of smart home devices and automotive systems, potential breaches in military communication, and challenges in critical infrastructure security. We quantitatively show that attack success rates hover around 60%, with the ability to activate devices remotely from over 100 feet away. Additionally, these attacks threaten critical infrastructure, emphasizing the need for multifaceted defensive strategies combining acoustic shielding, advanced signal processing, machine learning, and robust user authentication to mitigate these risks.