Black-Box Attack for IRS-Aided Communications with BER-Only Feedback
Abstract
Intelligent reflecting surface (IRS) has emerged as a promising wireless technology for enhancing legitimate communications. In this paper, we investigate an IRS-assisted multiuser MISO downlink network in which an attacker reconfigures the IRS to degrade legitimate data transmission. Specifically, we consider a black-box attack setting in which the attacker has access to neither channel information nor symbol-level observations and can observe only the BER reported by each user. The objective of the attacker is to identify an IRS configuration that maximizes the minimum BER among all users under a limited budget of non-repeated tests. To address this BER-only black-box attack problem, we propose PRIS, an online search method that learns effective phase transitions from previous trials and progressively narrows the search from large-block modifications to single-element refinement. Specifically, an annealed user-balancing reward and prioritized replay are incorporated to exploit noisy BER feedback. Numerical results demonstrate that PRIS achieves a higher minimum BER than existing benchmarks.
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BibTeX
@misc{tu2026blackbox,
title = {{Black-Box Attack for IRS-Aided Communications with BER-Only Feedback}},
author = {Zhengkai Tu and Xinyue Xiang and Jimmy Huang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.23112},
eprint = {2609.23112},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.23112},
}
RIS
TY - UNPB
TI - Black-Box Attack for IRS-Aided Communications with BER-Only Feedback
AU - Zhengkai Tu
AU - Xinyue Xiang
AU - Jimmy Huang
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2609.23112
UR - https://arxiv.org/abs/2609.23112
AB - Intelligent reflecting surface (IRS) has emerged as a promising wireless technology for enhancing legitimate communications. In this paper, we investigate an IRS-assisted multiuser MISO downlink network in which an attacker reconfigures the IRS to degrade legitimate data transmission. Specifically, we consider a black-box attack setting in which the attacker has access to neither channel information nor symbol-level observations and can observe only the BER reported by each user. The objective of the attacker is to identify an IRS configuration that maximizes the minimum BER among all users under a limited budget of non-repeated tests. To address this BER-only black-box attack problem, we propose PRIS, an online search method that learns effective phase transitions from previous trials and progressively narrows the search from large-block modifications to single-element refinement. Specifically, an annealed user-balancing reward and prioritized replay are incorporated to exploit noisy BER feedback. Numerical results demonstrate that PRIS achieves a higher minimum BER than existing benchmarks.
ER -