The widespread application of large vision language models has significantly raised safety concerns. In this project, we investigate text prompt injection, a simple yet effective method to mislead these models. We developed an algorithm for this type of attack and demonstrated its effectiveness and efficiency through experiments. Compared to other attack methods, our approach is particularly effective for large models without high demand for computational resources.
This paper is crucial in understanding the vulnerabilities of large vision language models to text prompt injection attacks, a methodology that poses significant implications in the field of AI security. By providing a robust algorithm and experimental validation, the research contributes to the growing discussion on the safety and reliability of AI systems, especially in applications involving multimodal inputs. The insights presented pave the way for future investigations into both offensive and defensive strategies against such vulnerabilities.
This paper will be of great interest to AI security researchers due to its exploration of a relatively under-examined attack vector in the rapidly evolving field of multimodal AI. The findings highlight significant security risks associated with text prompt injection in VLMs, which could have real-world ramifications in numerous applications. Researchers focused on adversarial attacks, model robustness, and safety mechanisms can leverage the methods and insights provided to develop new defenses, improve model resilience, and build safer AI systems.