Generative AI Security: Challenges and Countermeasures

Generative AI's expanding footprint across numerous industries has led to both excitement and increased scrutiny. This paper delves into the unique security challenges posed by Generative AI, and outlines potential research directions for managing these risks. Generative AI (GenAI) systems enable users to quickly generate high-quality content. GenAI models are designed to understand and generate content with a degree of autonomy that surpasses traditional machine learning systems, providing novel capabilities to generate text and code, interact with humans and Internet services, generate realistic images, and understand visual scenes. This capability enables a broader range of applications, and in this way introduces new security challenges unique to these novel GenAI-integrated applications. In this paper we discuss the challenges and opportunities for the field, starting in this section with the security risks, including how GenAI models might become a target of attack, a "fool" that unintentionally harms security, or a tool for bad actors to attack others. While GenAI models have groundbreaking capabilities, they are also susceptible to adversarial attack and manipulation. Jailbreaking and prompt injection are two prominent threats to GenAI models and applications built using them. Jailbreaking is an emergent technique where adversaries use specially crafted prompts to manipulate AI models into generating harmful or misleading outputs (Chao et al., 2023; Wei et al., 2023; Liu et al., 2023d). This exploitation can lead to the AI system bypassing its own safety protocols or ethical guidelines.