Emerging Risks with Embedded LLM in Applications

Large Language Models (LLMs) like OpenAI's GPT and Google's Bard have swept the tech landscape with their transformative capabilities for helping organizations overcome resource constraints and accelerate the pace of innovation. But as these AI technologies find their way into various applications, it has become apparent that they come with a new breed of security headaches. In response, OWASP have revealed their Top 10 for Large Language Models, which includes Prompt Injections, Insecure Output Handling, Training Data Poisoning, Denial of Service, Supply Chain, Permission Issues, Data Leakage, Excessive Agency, Over-reliance, and Insecure Plugins. While some of these risks are general to AppSec, others are more specific and require extra attention. In this article we'll elaborate on a few of these weaknesses and how they can affect your organization.

Legit Security is the first AppSec solution to secure Generative AI-based applications and bring visibility, security, and governance into code-generating AI. In this article, we'll dive into the risks of embedding LLMs into your applications, such as providing a chat interface backed by LLMs for your application users. We'll cover other AI-based vulnerabilities in future blogs. 

The post Emerging Risks with Embedded LLM in Applications appeared first on Security Boulevard.