ACM CCS 2026

GhostTac: Manipulating Tactile Sensors without Physical Contact

Kun Wang1Xuancun Lu1Ruochen Zhou2*Kai Wang1Tongjun Ye1Yihao Shao1Chen Yan1Xiaoyu Ji1Wenyuan Xu1

1Zhejiang University   2The Hong Kong University of Science and Technology
*Corresponding author

Overview of the GhostTac attack: contactless electromagnetic interference affects tactile feedback and robotic tasks
Figure 1. GhostTac demonstrates how contactless electromagnetic interference can manipulate tactile measurements or induce denial of service, creating risks for robotic systems.

Abstract

Tactile sensors are integral to modern robotic systems, enabling robots to perceive and interact with the physical environment through tactile feedback. However, the physical-layer security of tactile sensors has received little attention. We present GhostTac, the first contactless attack, to the best of our knowledge, that manipulates tactile sensing through electromagnetic interference (EMI). GhostTac exploits nonlinear rectification and limited-bandwidth amplification, converting carefully crafted EMI signals into persistent DC offsets that bypass onboard filtering and induce stable measurement deviations. It enables fine-grained, controllable manipulation of sensor outputs by shaping the spatial distribution and magnitude of interference at targeted locations. Such manipulation can induce harmful robot behaviors, including excessive force that may damage objects or injure people. We evaluate GhostTac on 10 sensor modules and two dexterous hands, covering 15 tactile sensors of different types, and demonstrate consistent effectiveness across all tested devices. Three case studies involving tactile grasping, slip detection, and material classification further illustrate its practical impact on real robotic tasks. These findings reveal a new physical attack vector against tactile sensing in robotic systems.

Background

From tactile array to tactile information

Tactile sensing begins at the sensor array and proceeds through amplification, digitization, and microcontroller processing. This shared architecture is the basis for understanding how an external disturbance can affect the reported tactile information.

Tactile sensor architecture from sensing array through amplification, ADC, and MCU
Overview of the tactile sensing system architecture: the sensing array is read through amplifying circuits, digitized by an ADC, and processed by an MCU.

01 / TACTILE SENSING SYSTEM

Physical contact becomes a digital tactile signal through a layered sensing and processing path.

Pressure changes at the TX-RX intersections alter the sensor output. The array is scanned, conditioned by the amplifying circuit, digitized, and finally translated into tactile information for the robotic system.

Principle

Why the disturbance can become a stable error

The paper identifies a circuit-level mechanism that explains why high-frequency EMI can affect low-frequency tactile measurements.

02 / CIRCUIT-LEVEL EFFECT

Coupling, rectification, and amplification produce a persistent offset.

An external signal can couple into the sensing circuit. Nonlinear behavior in the amplifier input stage rectifies part of the signal, after which amplification preserves a DC component that appears as a deviation in the tactile measurement.

Circuit-level illustration of EMI coupling, rectification, amplification, and measurement deviation
Principle illustration from the paper: EMI coupling, rectification, and amplification lead to a stable measurement deviation.
GhostTac workflow from controlled signal design to demonstrated robotic task impacts
System-level overview of GhostTac. The paper connects controlled EMI coupling with sensing manipulation and its demonstrated effects on robotic tasks.

03 / SYSTEM-LEVEL WORKFLOW

The circuit-level effect can propagate to tactile-driven robotic tasks.

At the system level, the paper links controlled sensing-side deviations to demonstrations in grasping, slip detection, and material classification. This figure provides the overall research workflow without exposing operational parameters.

Demonstrations

Robotic task case studies

All existing demonstration videos and original setup images are retained below. They are grouped by task to make comparisons easier to follow.

01 Grasping8 demonstrations

Positive interference can lead to object dropping; negative interference can lead to excessive grasping. The original demonstrations cover bottles, an artificial hand, and a paper cup.

Normal grasping of a medical glass bottle
Dropping a medical glass bottle under positive interference
Normal grasping of a medical IV bottle
Dropping a medical IV bottle under positive interference
Normal grasping with an artificial hand
Overgrasping an artificial hand under negative interference
Normal grasping of a paper cup
Overgrasping a paper cup under negative interference
02 Slip detection2 demonstrations

By altering the sensing result, the attack can generate a false slip event or suppress the indication of a real slip.

Slip detection experiment setup
Slip detection setup
False slip generation
Real slip suppression
03 Material classification2 demonstrations

The demonstrations compare the variation in tactile measurements under normal and interference conditions during material classification.

Material classification experiment setup
Material classification setup
Measurement variation under normal conditions
Measurement variation under attack conditions
04 Real-world scenarios6 demonstrations

These demonstrations show pre-positioned and passing-by scenarios, each with normal, positive-interference, and negative-interference conditions.

Pre-positioned scenario: normal operation
Pre-positioned scenario: positive interference
Pre-positioned scenario: negative interference
Passing-by scenario: normal operation
Passing-by scenario: positive interference
Passing-by scenario: negative interference