Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The AI ecosystem is rapidly changing, and with this growth comes unique challenges in securing the infrastructure and services that support it. In Part 1 of this series, we explored how attackers target the underlying resources that host and run AI applications, such as cloud infrastructure and storage. In this post, we'll look at threats that affect AI-specific resources in supply chains, which are the software and data artifacts that determine how an AI service operates.

In Parts 1 and 2 of this series, we looked at how attackers get access to and take advantage of the infrastructure and supply chains that shape generative AI applications. In this post, we'll discuss AI interfaces, which we define as the entry points and logic that determine how a user interacts with an AI application. These elements can include chat interfaces, such as AI assistants, and API endpoints for supporting services.

The swift adoption of generative AI (GenAI) by the software industry has introduced a new area of focus for security engineers: threats targeting the various components of their AI applications. Understanding how these areas are vulnerable to attacks will become increasingly significant as the space evolves. In this series, we'll look at common threats that target the following components of AI applications.

With innovation comes risk. As organizations race to build AI-first infrastructure, security is struggling to keep pace. Multi-Agentic Systems – those built on Large Language Models (LLMs) and Multi-Component Protocols (MCP) - bring immense potential, but also novel vulnerabilities that traditional tools weren’t designed to handle.