Injected and Delivered: Fabricating Implicit Control over Actuation Systems by Spoofing Inertial Sensors
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Abstract (arXiv)
Inertial sensors provide crucial feedback for control systems to determine motional status and make timely, automated decisions. Prior efforts tried to control the output of inertial sensors with acoustic signals. However, their approaches did not consider sample rate drifts in analog-to-digital converters as well as many other realistic factors. As a result, few attacks demonstrated effective control over inertial sensors embedded in real systems. This work studies the out-of-band signal injection methods to deliver adversarial control to embedded MEMS inertial sensors and evaluates consequent vulnerabilities exposed in control systems relying on them. Acoustic signals injected into inertial sensors are out-of-band analog signals. Consequently, slight sample rate drifts could be amplified and cause deviations in the frequency of digital signals. Such deviations result in fluctuating sensor output; nevertheless, we characterize two methods to control the output: digital amplitude adjusting and phase pacing. Based on our analysis, we devise non-invasive attacks to manipulate the sensor output as well as the derived inertial information to deceive control systems. We test 25 devices equipped with MEMS inertial sensors and find that 17 of them could be implicitly controlled by our attacks. Furthermore, we investigate the generalizability of our methods and show the possibility to manipulate the digital output through signals with relatively low frequencies in the sensing channel.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2018.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 2; 2024: 1; 2025: 2; 2026: 1.
10 papers.
- 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attackscited by 101
- 2022Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patchescited by 57
- 2025Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attackscited by 32
- 2023APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigationcited by 10
- 2023Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunitiescited by 9
- 2021Signal Injection Attacks against CCD Image Sensorscited by 6
- 2021SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systemscited by 4
- 2024SpecGuard: Specification Aware Recovery for Robotic Autonomous Vehicles from Physical Attackscited by 2
- 2025ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attackscited by 1
- 2026Banshee: Target Switch Attacks on Gimbal-Stabilized Visual Tracking Systems via Acoustic Injectioncited by 0
- 2026Banshee: Target Switch Attacks on Gimbal-Stabilized Visual Tracking Systems via Acoustic Injection10 Jul 2026
- 2025ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attacks27 Jun 2025
- 2025Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks18 Feb 2025
- 2024SpecGuard: Specification Aware Recovery for Robotic Autonomous Vehicles from Physical Attacks27 Aug 2024
- 2023Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities12 Oct 2023
- 2023APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation2 Mar 2023
- 2022Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches11 Jul 2022
- 2021Signal Injection Attacks against CCD Image Sensors19 Aug 2021
- 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks17 Jun 2021
- 2021SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems19 Mar 2021