Phantom Force: Injecting Adversarial Tactile Perceptions into Embodied Intelligence via EMI
Authors: Zirui Kong, Youqian Zhang, Sze Yiu Chau
Summary
arXiv:2605. 13492v1 Announce Type: new Abstract: Embodied intelligent robots rely on tactile sensors to interact with the physical world safely.
Relevance
Read next because Phantom Force: Injecting Adversarial Tactile Perceptions into Embodied Intelligence via EMI overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Coupling evil personas with wrong answers fails to protect Qwen2.5-7B from EM-induced alignment collapse — and the apparent capability ordering across coupling conditions is mostly eval contamination (LOW confidence)", clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)". Matching terms: text, rect, class, strong, rate, model. Source: arxiv cs.CR (Cryptography and Security).
Threat model
Potential threat/caveat for clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)": this item discusses adversarial.
Abstract
arXiv:2605.13492v1 Announce Type: new 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.