Beyond the LLM Top 10: Securing AI Applications, Agents, and the Ecosystems Behind Them

Enterprise AI has rapidly evolved from standalone chatbots into complex ecosystems of agents, tools, Model Context Protocol (MCP) servers, retrieval pipelines, and interconnected data sources. While security teams have invested heavily in addressing the risks outlined in the OWASP Top 10 for LLM Applications, the reality is that today's AI applications extend far beyond a simple conversation between a user and a model. As AI systems gain the ability to take actions, invoke tools, access sensitive data, and interact with other systems, the threat surface expands dramatically.

This white paper explores why AI applications represent the next major evolution of application security and why traditional controls alone are no longer enough. It examines how the industry has progressed from securing web applications and APIs to securing AI-driven applications whose behaviour is shaped dynamically at inference time through prompts, context, retrieved content, and tool interactions.

Drawing on real-world incidents, legal precedents, public breach reports, and emerging security frameworks, the paper highlights the growing risks associated with prompt injection, sensitive data disclosure, hidden context exposure, improper output handling, and unbounded consumption, while also addressing a new generation of threats affecting agentic applications and MCP-based architectures. These include command injection, tool poisoning, identity and privilege abuse, and insecure agent-to-agent interactions.

The paper introduces the concept of the AI Execution Path — the complete chain of interactions between applications, models, agents, MCP servers, tools, and data sources. It argues that effective AI security requires visibility and control across this entire ecosystem rather than focusing solely on model inputs and outputs.

Readers will gain practical guidance on building an AI security programme around three core pillars: discovery, risk assessment, and governance. The paper outlines how organisations can identify shadow AI, understand agent relationships and privileges, establish behavioural baselines, and implement runtime protections that address both current LLM risks and emerging agentic threats.