OWASP Top 10 for Large Language Model Applications

Current Release and Active Repository

Legacy entry point: This OWASP project page and its GitHub repository are maintained as a historical archive so existing links and citations continue to work. Active development has moved to the OWASP GenAI Security Project repository.

Get the OWASP GenAI LLM Top 10 2026 — published August 4, 2026.

Browse the current source and contribute

About This Legacy Repository

This repository powered the original OWASP Top 10 for Large Language Model Applications project page and retains historical releases, translations, and supporting material. The initiative is now housed within the comprehensive OWASP GenAI Security Project, which encompasses multiple security initiatives beyond the Top 10 list.

OWASP GenAI Security Project

The OWASP GenAI Security Project is a global, open-source initiative dedicated to identifying, mitigating, and documenting security and safety risks associated with generative AI technologies, including large language models (LLMs), agentic AI systems, and AI-driven applications. Our mission is to empower organizations, security professionals, AI practitioners, and policymakers with comprehensive, actionable guidance and tools to ensure the secure development, deployment, and governance of generative AI systems.

Learn more about our mission and charter: Project Mission and Charter

Visit our main project site: genai.owasp.org

OWASP Top 10 for LLM Applications

The OWASP Top 10 for Large Language Model Applications continues to be a core component of our work, identifying the most critical security vulnerabilities in LLM applications.

Current release: OWASP GenAI LLM Top 10 2026

Canonical source: GenAI-Security-Project/GenAI-LLM-Top10 — 2026/final

Project Background and Growth

The project has evolved significantly since its inception. From a small group of security professionals addressing an urgent security gap in 2023, it has grown into a global community with over 600 contributing experts from more than 18 countries and nearly 8,000 active community members.

Read our full project background: Introduction and Background

Get Involved

Contribute to the Project

We welcome all expert ideas, contributions, suggestions, and remarks from security professionals, researchers, developers, and anyone passionate about AI security.

Contribute to the Top 10: https://github.com/GenAI-Security-Project/GenAI-LLM-Top10

Learn how to contribute to the broader project: https://genai.owasp.org/contribute/

Join Our Meetings

Participate in our bi-weekly sync meetings and stay connected with the community.

Meeting information: https://genai.owasp.org/meetings/

Connect with the Community

Project Support

We are a not-for-profit, open-source, community-driven project. If you are interested in supporting the project with resources or becoming a sponsor to help us sustain community efforts and offset operational and outreach costs, visit the Sponsor Section on our website.

Thank you to our current Sponsors and Supporters

Educational Resources

New to LLM application security? Explore the OWASP GenAI Security Project resources or the historical educational-resources wiki.

OWASP Top 10 for Large Language Model Applications Version 1.1

Historical version: This is the archived 2023 v1.1 list. Get the current OWASP GenAI LLM Top 10 2026 or browse its canonical source.

LLM01: Prompt Injection

Manipulating LLMs via crafted inputs can lead to unauthorized access, data breaches, and compromised decision-making.

LLM02: Insecure Output Handling

Neglecting to validate LLM outputs may lead to downstream security exploits, including code execution that compromises systems and exposes data.

LLM03: Training Data Poisoning

Tampered training data can impair LLM models leading to responses that may compromise security, accuracy, or ethical behavior.

LLM04: Model Denial of Service

Overloading LLMs with resource-heavy operations can cause service disruptions and increased costs.

LLM05: Supply Chain Vulnerabilities

Depending upon compromised components, services or datasets undermine system integrity, causing data breaches and system failures.

LLM06: Sensitive Information Disclosure

Failure to protect against disclosure of sensitive information in LLM outputs can result in legal consequences or a loss of competitive advantage.

LLM07: Insecure Plugin Design

LLM plugins processing untrusted inputs and having insufficient access control risk severe exploits like remote code execution.

LLM08: Excessive Agency

Granting LLMs unchecked autonomy to take action can lead to unintended consequences, jeopardizing reliability, privacy, and trust.

LLM09: Overreliance

Failing to critically assess LLM outputs can lead to compromised decision making, security vulnerabilities, and legal liabilities.

LLM10: Model Theft

Unauthorized access to proprietary large language models risks theft, competitive advantage, and dissemination of sensitive information.

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