Environmental Injection Attacks against GUI Agents in Realistic Dynamic Environments

Graphical User Interface (GUI) agents are increasingly deployed to interact with online web services, yet their exposure to open-world content renders them vulnerable to Environmental Injection Attacks (EIAs). In these attacks, an attacker can inject crafted triggers into a website to manipulate the behavior of other users’ GUI agents. In this paper, we find that most existing EIA studies fall short of realism. In particular, they fail to capture the dynamic nature of real-world websites, often assuming that a trigger’s on-screen position and surrounding visual context remain largely consistent between training and testing. To better reflect practice, we introduce a realistic dynamic-environment threat model in which the attacker is a regular user and the trigger is embedded within a dynamically changing environment. Under this threat model, existing approaches largely fail, suggesting that their effectiveness in exposing GUI agent vulnerabilities has been overestimated. To expose the hidden vulnerabilities of existing GUI agents effectively, we propose Chameleon, an attack framework with two key components designed for dynamic environments. (1) To synthesize more realistic training data, we introduce LLM-Driven Environment Simulation, which automatically generates diverse, high-fidelity webpage simulations that mimic the variability of real-world dynamic environments. (2) To optimize the trigger more effectively, we introduce Attention Black Hole, which converts attention weights into explicit supervisory signals. We evaluate Chameleon on six realistic websites and four representative LVLM-powered GUI agents. Across these settings, it significantly outperforms existing methods. Ablation studies confirm that both components are critical to performance, and a closed-loop sandbox experiment further demonstrates that Chameleon can successfully hijack agent behavior in conditions that closely mirror real-world usage. Our results uncover a critical, previously underexplored vulnerability of GUI agents in realistic dynamic environments and establish a robust foundation for future research on defenses for open-world GUI agent systems.