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arxiv: 2605.13492 · v1 · submitted 2026-05-13 · 💻 cs.CR

Recognition: 2 theorem links

· Lean Theorem

Phantom Force: Injecting Adversarial Tactile Perceptions into Embodied Intelligence via EMI

Zirui Kong , Youqian Zhang , Sze Yiu Chau
Authors on Pith no claims yet

Pith reviewed 2026-05-14 18:17 UTC · model grok-4.3

classification 💻 cs.CR
keywords electromagnetic interferencetactile sensorsadversarial attacksrobot securityphantom forceshall-effect sensorsembodied ai
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The pith

Electromagnetic interference can create phantom forces in robot fingertip sensors by amplifying perceived magnitudes over nine times and deviating directions by 65 degrees.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper establishes that Hall-effect fingertip sensors in embodied robots are vulnerable to intentional electromagnetic interference. A targeted signal injection produces phantom forces that amplify the sensed force magnitude by more than nine times while shifting the apparent direction by 65 degrees. These false readings can disable learning-based tactile models and cause the robot to perform unsafe actions such as crushing fragile objects or dropping dangerous items. The finding reveals an unexplored attack surface in the tactile perception channel that physical robots rely on for safe interaction.

Core claim

Targeted electromagnetic interference applied to Hall-effect fingertip sensors induces strong phantom forces. This amplifies the perceived force magnitude by over 9× and deviates the inferred force direction by 65°. The resulting perturbations paralyze learning-based tactile classification models, seriously affecting robot movement and enabling an attacker to coerce a robot hand into crushing fragile objects or dropping dangerous payloads.

What carries the argument

Targeted EMI signal injection into Hall-effect fingertip sensors that creates phantom forces to mislead the robot's force perception.

If this is right

  • Learning-based tactile classification models become paralyzed under the induced perturbations.
  • Robot movement is seriously affected by the false tactile data.
  • Attackers can coerce robot hands to crush fragile objects.
  • Attackers can cause robots to drop dangerous payloads.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This vulnerability could extend to other sensor types used in robotics beyond Hall-effect devices.
  • Defenses might include adding EMI shielding to sensors or monitoring for sudden inconsistencies in force readings.
  • Similar injection attacks could be tested on other embodied systems such as autonomous vehicles with tactile components.

Load-bearing premise

That the electromagnetic interference can be delivered in a targeted, practical manner on real robot hardware without triggering other detectable effects or existing mitigations.

What would settle it

A controlled experiment applying the EMI signal to a physical Hall-effect sensor on a robot hand and verifying if the reported force magnitude increases by over 9 times and direction shifts by 65 degrees compared to baseline measurements.

Figures

Figures reproduced from arXiv: 2605.13492 by Sze Yiu Chau, Youqian Zhang, Zirui Kong.

Figure 1
Figure 1. Figure 1: Simple attack experiment simulation in NVIDIA [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Attack experiment model and setup in reality. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Sensor Attack Simulation during the Stable Hover [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
read the original abstract

Embodied intelligent robots rely on tactile sensors to interact with the physical world safely. While the security of visual perception systems has been studied (e.g., adversarial samples), the integrity of the tactile sensory channel remains unexplored. This work explores a vulnerability in Hall-effect fingertip sensors, showing their susceptibility to intentional Electromagnetic Interference (EMI). We demonstrate that a targeted signal injection can induce strong ``phantom forces'', amplifying perceived force magnitude by over \textbf{9$\times$} and deviating the inferred force direction by \textbf{65$^\circ$}. Such perturbations can paralyze learning-based tactile classification models, seriously affecting robot movement. An attacker could exploit this vulnerability to coerce a robot hand into crushing fragile objects or dropping dangerous payloads.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Circularity Check

0 steps flagged

No significant circularity; results from direct experimental observation

full rationale

The paper's central claims (9× force amplification and 65° directional deviation) are presented as outcomes of hardware experiments injecting EMI into Hall-effect fingertip sensors. No mathematical derivation chain, parameter fitting, self-definitional equations, or load-bearing self-citations appear in the abstract or described content. The work is empirical rather than deductive, with no reduction of predictions to fitted inputs or renamed ansatzes. This is the common case of a self-contained experimental report whose validity rests on replicable measurements, not internal construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that Hall-effect sensors respond predictably to external EMI and that targeted injection is feasible in embodied settings.

axioms (1)
  • domain assumption Hall-effect fingertip sensors respond to external electromagnetic fields in a manner that produces measurable force misreadings
    Invoked implicitly when claiming phantom force induction without additional justification in abstract.

pith-pipeline@v0.9.0 · 5424 in / 1129 out tokens · 35536 ms · 2026-05-14T18:17:38.110626+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

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Reference graph

Works this paper leans on

3 extracted references · 2 canonical work pages

  1. [1]

    NVIDIA. [n. d.].Isaac Sim. https://github.com/isaac-sim/IsaacSim

  2. [2]

    2006.How the Body Shapes the Way We Think: A New View of Intelligence

    Rolf Pfeifer and Josh Bongard. 2006.How the Body Shapes the Way We Think: A New View of Intelligence. The MIT Press. doi:10.7551/mitpress/3585.001.0001

    work page doi:10.7551/mitpress/3585.001.0001 2006

  3. [3]

    Yuchuang Tong, Haotian Liu, and Zhengtao Zhang. 2024. Advancements in Humanoid Robots: A Comprehensive Review and Future Prospects.IEEE/CAA Journal of Automatica Sinica11, 2 (February 2024), 301–328. doi:10.1109/JAS.2023. 124140

    work page doi:10.1109/jas.2023 2024