Attacking Vision-Language Computer Agents via Pop-ups (2411.02391v2)

Abstract: Autonomous agents powered by large vision and LLMs (VLM) have demonstrated significant potential in completing daily computer tasks, such as browsing the web to book travel and operating desktop software, which requires agents to understand these interfaces. Despite such visual inputs becoming more integrated into agentic applications, what types of risks and attacks exist around them still remain unclear. In this work, we demonstrate that VLM agents can be easily attacked by a set of carefully designed adversarial pop-ups, which human users would typically recognize and ignore. This distraction leads agents to click these pop-ups instead of performing their tasks as usual. Integrating these pop-ups into existing agent testing environments like OSWorld and VisualWebArena leads to an attack success rate (the frequency of the agent clicking the pop-ups) of 86% on average and decreases the task success rate by 47%. Basic defense techniques, such as asking the agent to ignore pop-ups or including an advertisement notice, are ineffective against the attack.

• The paper demonstrates that adversarial pop-ups misdirect Vision-Language agents, triggering an 86% response rate and a 47% decline in task completion.
• It details a threat model using attention hooks, instructions, banners, and ALT descriptors to manipulate agent behavior in GUI environments.
• It emphasizes the urgent need for robust defenses, as current measures reduce attack success by only about 25%.

Overview of "Attacking Vision-Language Computer Agents via Pop-ups"

The paper under review, "Attacking Vision-Language Computer Agents via Pop-ups," investigates vulnerabilities in Vision-LLMs (VLMs) within autonomous agents tasked with executing computer-based tasks. It focuses on how these agents, which combine visual perception with linguistic understanding to perform actions like web browsing and software operation, can be compromised through adversarial attacks using pop-ups. This research contributes to a growing understanding of the security risks associated with deploying VLM agents in interface-rich environments.

Main Insights and Results

This paper highlights a specific vulnerability in VLM agents: their propensity to be deceived by adversarial pop-ups on graphical user interfaces. The authors define pop-ups in this context as malicious, clickable images that are strategically placed on the screen. These pop-ups aim to distract the agent and cause it to execute unintended actions. The key findings of the paper indicate that these attacks have a substantial success rate, with agents clicking on pop-ups in 86% of attack instances, and task completion rates decreasing by 47%.

Despite the integration of basic defense mechanisms, such as instructing agents to ignore pop-ups or marking pop-ups as advertisements, these attacks remain overwhelmingly effective. The robustness of these defenses was deficient, decreasing the attack success rate by no more than 25% when such measures were employed.

Attack Methodology

The paper introduces a threat model where the adversary manipulates the agent’s environment through pop-ups, in several realistic scenarios including malvertising and phishing. Attack design features include:

• Attention Hook: A concise set of words intended to draw the agent’s focus.
• Instruction: Commands intended to influence the agent's behavior.
• Information Banner: Contextual data misrepresenting the purpose of the pop-up.
• ALT Descriptor: Supplementary text modifying the agent's perception of the pop-up in accessibility trees.

The paper tests these techniques across different environments, including OSWorld and VisualWebArena, using state-of-the-art VLMs as backbones.

Implications and Future Directions

The findings from this work underscore the pressing need for improved security measures in the deployment of VLM agents. Practical implications suggest that current systems are not adequately prepared to handle the adversarial noise present in GUI environments. Future directions could explore more effective defense mechanisms, possibly enhancing agent training to recognize and autonomously dismiss these types of distractions more effectively.

Furthermore, this research lays groundwork for further exploration into the intersection of visual perception and security, particularly how VLMs interpret and prioritize visual and linguistic information. From a theoretical perspective, it raises questions about the fundamentals of agentic decision-making and the considerable gap between human and machine perception in complex, interactive environments.

Overall, this paper serves as a critical reminder of the vulnerabilities innate in artificial agent systems and the continuous development needed to realize their potential safely and effectively. As these systems become more prevalent, the work of Zhang, Yu, and Yang provides foundational insights necessary for building more resilient and secure autonomous agents.