DualMirage: Hunting Stealthy Multimodal LLM Agents via CAPTCHAs with Contour and Adversarial Illusions

Abstract

The rapid advancement of Multimodal Large Language Models (MLLMs) has given rise to sophisticated autonomous agents capable of performing complex, human-like tasks across the web. However, this also introduces significant security risks, particularly from stealthy MLLM agents that can evade conventional detection mechanisms by mimicking human behavior. In this paper, we propose DualMirage, a novel CAPTCHA framework that proactively counters and identifies stealthy agents by exploiting fundamental disparities between human and machine perception. DualMirage employs a dual-pronged strategy: (1) Contour Illusions, which utilize cognitive principles to generate illusory contours that humans perceive effortlessly yet pose interpretation challenges for MLLMs; and (2) Adversarial Illusions, which embed human-imperceptible perturbations optimized to mislead the visual encoders of target MLLMs and thereby elicit characteristic, identifiable model responses. Evaluations on five state-of-the-art MLLMs demonstrate that DualMirage achieves an average 95.8% human success rate while blocking MLLM agents (up to 100% agent blocking rate), outperforming existing CAPTCHAs. Furthermore, DualMirage induces models to expose identities actively, achieving 58.8% white-box and 21.9% black-box attack success rates, proving effective against stealthy multimodal agents.