Large Language Models (LLMs) are transforming software development. From AI-powered chatbots and coding assistants to Retrieval-Augmented Generation (RAG) systems and autonomous agents, AI is becoming an integral part of modern applications.

However, integrating an LLM introduces security risks that traditional application security practices alone may not adequately address.

Consider an AI assistant connected to an internal knowledge base. What happens if a malicious document instructs the AI to reveal confidential information? What if an AI agent executes an unauthorized database query based on a manipulated prompt?

These scenarios highlight why securing AI applications requires a different approach.

The OWASP Top 10 for LLM Applications provides developers with a framework for identifying and mitigating critical security risks in LLM-powered systems.

The 10 major security risks

The 2026 edition identifies the following risks:

  1. Prompt Injection — Manipulating an LLM through malicious instructions.
  2. Sensitive Information Disclosure — Exposing confidential information through AI systems.
  3. Excessive Agency — Giving AI systems too much authority or autonomy.
  4. Supply Chain — Risks from compromised models, dependencies, datasets, or other components.
  5. Data and Model Poisoning — Corrupting data used for training, fine-tuning, or retrieval.
  6. Unbounded Consumption — Excessive resource usage, service disruption, or unexpected costs.
  7. Misinformation — Generating or presenting false information as reliable.
  8. Hidden Context Exposure — Revealing internal prompts, instructions, or other non-user-facing context.
  9. Vector and Embedding Weaknesses — Security problems in vector databases and embedding-based retrieval.
  10. Improper Output Handling — Trusting AI-generated output without appropriate validation.

 

A practical secure AI architecture

A secure LLM application should enforce security at multiple layers:

  1. User authentication: Identify the user and establish their identity.
  2. Authorization: Determine which data and actions the user may access.
  3. Input validation: Enforce size, type, and format restrictions.
  4. LLM orchestration: Send only necessary context to the model.
  5. Retrieval security: Filter documents according to verified permissions.
  6. Tool controls: Restrict operations and validate every proposed action.
  7. Output validation: Check generated content before using it.
  8. Monitoring: Record relevant events, control resource consumption, and detect suspicious behavior.

 

The model is only one component of the security boundary. The application must enforce the rules that the model cannot reliably enforce itself.

Securing LLM applications requires more than writing a good system prompt. Developers must consider the complete system: data sources, retrieval pipelines, models, tool integrations, permissions, generated output, and operational monitoring.

The OWASP Top 10 provides a useful starting point for building this security into the development lifecycle.

References and Further Reading

  1. OWASP GenAI Security Project
  2. OWASP Top 10 for LLM Applications — 2026
  3. OWASP GenAI Security Project — GitHub

 

This article is intended for educational purposes. Refer to the official OWASP documentation for the latest security guidance.