On the Feasibility of Using MultiModal LLMs to Execute AR Social Engineering Attacks

• URL: http://arxiv.org/abs/2504.13209v1
• Date: Wed, 16 Apr 2025 05:18:36 GMT
• Title: On the Feasibility of Using MultiModal LLMs to Execute AR Social Engineering Attacks
• Authors: Ting Bi, Chenghang Ye, Zheyu Yang, Ziyi Zhou, Cui Tang, Jun Zhang, Zui Tao, Kailong Wang, Liting Zhou, Yang Yang, Tianlong Yu,
• Abstract summary: We propose a framework for orchestrating AR-driven Social Engineering attacks using Multimodal Large Language Models.<n>Our results show that SEAR is highly effective at eliciting high-risk behaviors.<n>We identify notable limitations such as occasionally artificial'' due to perceived authenticity gaps.
• Score: 8.28564202645918
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Augmented Reality (AR) and Multimodal Large Language Models (LLMs) are rapidly evolving, providing unprecedented capabilities for human-computer interaction. However, their integration introduces a new attack surface for social engineering. In this paper, we systematically investigate the feasibility of orchestrating AR-driven Social Engineering attacks using Multimodal LLM for the first time, via our proposed SEAR framework, which operates through three key phases: (1) AR-based social context synthesis, which fuses Multimodal inputs (visual, auditory and environmental cues); (2) role-based Multimodal RAG (Retrieval-Augmented Generation), which dynamically retrieves and integrates contextual data while preserving character differentiation; and (3) ReInteract social engineering agents, which execute adaptive multiphase attack strategies through inference interaction loops. To verify SEAR, we conducted an IRB-approved study with 60 participants in three experimental configurations (unassisted, AR+LLM, and full SEAR pipeline) compiling a new dataset of 180 annotated conversations in simulated social scenarios. Our results show that SEAR is highly effective at eliciting high-risk behaviors (e.g., 93.3% of participants susceptible to email phishing). The framework was particularly effective in building trust, with 85% of targets willing to accept an attacker's call after an interaction. Also, we identified notable limitations such as ``occasionally artificial'' due to perceived authenticity gaps. This work provides proof-of-concept for AR-LLM driven social engineering attacks and insights for developing defensive countermeasures against next-generation augmented reality threats.

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