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The paper introduces Multimodal-SEA, a benchmark of 608 expert-designed tasks that test personal AI agents against multimodal social engineering attacks. It covers 14 domains, 152 attack scenarios, and four difficulty levels, reflecting Chinese and English cultural contexts. Seven multimodal models were evaluated, all showing vulnerability with attack success rates ranging from 7.1% to 34.5%, and multimodal content increased success rates despite higher verification coverage.
arXiv:2503. 15560v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly vulnerable to sophisticated multi-turn manipulation attacks, where adversaries strategically build context through seemingly benign conversational turns to circumvent safety measures and elicit harmful or unauthorized responses.
arXiv:2608. 15893v1 Announce Type: new Abstract: The rise of social media bots poses a persistent threat, enabling misinformation, opinion manipulation, and the erosion of trust in online platforms.
MobileInject is a benchmark that evaluates the security of multimodal large language model (MLLM)-based mobile agents against multimodal injection attacks. It offers a high‑fidelity interactive framework with 11 scenarios, 9 risk categories, and 108 executable risk instances, and introduces Cross‑Modal Consistency Injection along with two advanced attack methods—Multi‑Role Collaborative Deception and Reasoning‑Camouflage Injection—to intensify trust manipulation and reasoning hijacking. Experiments show that mobile agents are broadly vulnerable, especially to early‑stage reasoning hijacking, establishing MobileInject as a systematic tool for studying and improving mobile agent security.
arXiv:2505. 14289v2 Announce Type: replace Abstract: Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) are increasingly deployed yet vulnerable to Environmental Injection Attacks (EIAs).
The paper investigates whether multimodal large language models (MLLMs) can generate and detect realistic multimodal fake news on social media. Using a multi‑agent framework—comprising a story agent, an image agent, and a critic agent—the authors produced over 9,000 paired multimodal news posts across science, health, and entertainment domains. They benchmarked 16 open‑ and closed‑source MLLMs for automated detection and found that most models fall far short of human accuracy, especially in identifying image authenticity, highlighting the need for stronger defenses against social media fake news.