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Introduction

The frenzy of interest of Large Language Models (LLMs) following of mass-market pre- trained chatbots in late 2022 has been remarkable. Businesses, eager to harness the potential of LLMs, are rapidly integrating them into their operations and client facing offerings. Yet, the breakneck speed at which LLMs are being adopted has outpaced the establishment of comprehensive security protocols, leaving many applications vulnerable to high-risk issues.

The absence of a unified resource addressing these security concerns in LLMs was evident. Developers, unfamiliar with the specific risks associated with LLMs, were left scattered resources and OWASP’s mission seemed a perfect fit to help drive safer adoption of this technology.

Who is it for?

Our primary audience is developers, data scientists, and security experts tasked with designing and building applications and plug-ins leveraging LLM technologies. We aim to provide practical, actionable, and concise security guidance to help these professionals navigate the complex and evolving terrain of LLM application security.

The Making of the List

Creating the OWASP Top 10 for LLM Applications list was a significant undertaking, built on the collective expertise of an international team of nearly 500 experts with over 125 active contributors. Our contributors come from diverse backgrounds, including AI companies, security companies, ISVs, cloud hyperscalers, hardware providers, and academia.

We brainstormed for a month and proposed potential vulnerabilities, with team members writing up 43 distinct threats. Through multiple rounds of voting, we refined these proposals to a concise list of the ten most critical vulnerabilities. Dedicated sub-teams scrutinized each vulnerability and subjected it to public review, ensuring the most comprehensive and actionable final list.

Each of these vulnerabilities, along with examples, prevention tips, attack scenarios, and references, was further scrutinized and refined by dedicated sub-teams and subjected to public review, ensuring the most comprehensive and actionable final list.

Relating to other OWASP Top 10 Lists

While our list shares DNA with vulnerability types found in other OWASP Top 10 lists, we do not simply reiterate these vulnerabilities. Instead, we delve into the unique implications these vulnerabilities have when encountered in applications utilizing LLMs.

Our goal is to bridge the divide between general application security principles and the specific challenges posed by LLMs. This includes exploring how conventional vulnerabilities may pose different risks or might be exploited in novel ways within LLMs, as well as how traditional remediation strategies need to be adapted for applications utilizing LLMs.

About Version 1.1

While our list shares DNA with vulnerability types found in other OWASP Top 10 lists, we do not simply reiterate these vulnerabilities. Instead, we delve into these vulnerabilities’ unique implications when encountered in applications utilizing LLMs. Our goal is to bridge the divide between general application security principles and the specific challenges posed by LLMs.

The group’s goals include exploring how conventional vulnerabilities may pose different risks or be exploited in novel ways within LLMs and how developers must adapt traditional remediation strategies for applications utilizing LLMs.

The Future

The v1.1 of the list will not be our last. We expect to update it on a periodic basis to keep pace with the state of the industry. We will be working with the broader community to push the state of the art, and creating more educational materials for a range of uses. We also seek to collaborate with standards bodies and governments on AI security topics. We welcome you to join our group and contribute.


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Steve Wilson
Project Lead, OWASP Top 10 for LLM Applications
Twitter/X: @virtualsteve


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Ads Dawson
v1.1 Release Lead & Vulnerability Entries Lead, OWASP Top 10 for LLM Applications
LinkedIn: /in/adamdawson0
GitHub: @GangGreenTemperTatum

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Translations

Translations are now available!.