OWASP Large Language Model Application Security Top 10: Generative AI Security Risk Assessment Guide

The OWASP LLM Application Security Top 10 list is an authoritative security risk assessment framework for developers and security experts, focusing on identifying and mitigating key security risks in large language model applications. It is currently maintained by the GenAI Security Project and is being updated for the 2026 version. This list provides systematic guidance for LLM application security, helping organizations address the unique security challenges posed by generative AI.

With the rapid development of generative AI technology, LLM applications have permeated various industries, but they also bring unique security challenges. As a world-renowned application security organization, OWASP has launched the Top10 security list for LLM applications, providing authoritative guidance for developers, data scientists, and security experts. This project is maintained by the OWASP GenAI Security Project, a global open-source initiative whose mission is to make application security visible and help individuals and organizations make informed decisions about security risks related to LLM applications.

The main audiences of the OWASP LLM Security Top10 include application developers (designing and building LLM applications/plugins), data scientists (considering security in model training and deployment), and security experts (assessing and hardening LLM application security). The list serves as both an introductory guide for beginners and a reference for experienced professionals. It shares commonalities with the traditional OWASP Top10 but is not redundant, delving into the unique impacts of vulnerabilities in LLM applications.

The project's core objective is to provide an easy-to-understand and adoptable guide to help address potential security risks in LLM applications. Key methodologies include: 1. Unique Vulnerability Analysis: Studying the different risk characteristics and exploitation methods of traditional vulnerabilities in LLM environments; 2. Adaptive Remediation Strategies: Adjusting traditional remediation strategies to the characteristics of LLMs and providing specific security recommendations; 3. Bridging the Security Gap: Closing the gap between general application security principles and LLM-specific challenges, establishing a smooth transition path.

The project adopts a strict open-source governance model where all changes must go through Pull Requests, and direct pushes to the main branch are blocked. Community contribution channels include GitHub Issues (for feedback and suggestions), Pull Requests (for contributing code and documentation), and the #team-genai-top-10-llm channel on OWASP Slack (for real-time discussions). The 2026 version update is currently in progress, with detailed sprint plans and milestones. Contributors can refer to CONTRIBUTING.md under the 2026 directory.

For organizations building or maintaining LLM applications, this list provides the following values: 1. Risk Assessment Foundation: Offering a standardized starting point for security audits and risk assessments, systematically identifying key risks to prioritize; 2. Security Awareness Enhancement: The clear Top10 ranking helps teams understand common threats and enhance overall security awareness; 3. Compliance Reference: Serving as a reference for formulating internal security standards; 4. Training Resource: Used for security training of development teams to quickly grasp core concepts.

The OWASP LLM Security Top10 focuses on LLM application security and complements other frameworks (both internal and external to OWASP) rather than replacing them, avoiding generalization issues. The project uses the Creative Commons Attribution-ShareAlike 4.0 International License, allowing free use, modification, and distribution, provided that the terms of the license are followed.

The OWASP LLM Security Top10 represents the industry's systematic understanding and response efforts to the security risks of generative AI. The evolution of LLM technology brings constantly changing security threats, and the project's open-source nature and active community ensure that the list is updated in a timely manner to reflect the latest situation. It is recommended that teams involved in LLM application development incorporate it into the secure development lifecycle, establish a culture of security awareness, and maintain risk vigilance while innovating.