Artificial intelligence has introduced one of the biggest shifts in enterprise technology since organizations first moved to the cloud. Large Language Models (LLMs) are now embedded into chatbots, search platforms, software development tools, productivity applications, customer service platforms, and increasingly autonomous AI agents. Rather than simply generating text, these systems can access enterprise data, interact with business applications, execute workflows, and make decisions based on the information they retrieve.

While these capabilities create enormous opportunities, they also introduce an entirely new category of security risks. Traditional application security remains just as important as it has always been, but AI systems behave differently from conventional software. They interpret natural language, reason over context, retrieve information from multiple sources, and can influence or automate business processes. Those characteristics create attack surfaces that simply did not exist in traditional web or desktop applications.

This is where the OWASP Top 10 for Large Language Model Applications becomes valuable.

Much like the original OWASP Top 10 helped organizations understand the most significant security risks facing web applications, the OWASP Top 10 for LLMs provides a structured framework for identifying and addressing the risks that are unique to AI-powered systems. It gives developers, architects, security professionals, governance teams, and business leaders a common language for discussing AI security while providing practical guidance on the areas that deserve the greatest attention.

Why We Needed a New Top 10

The original OWASP Top 10 has been one of the most influential security awareness resources for web applications. Categories such as Broken Access Control, Injection, Security Misconfiguration, and Vulnerable Components remain just as relevant today as they were when they were first introduced.

The challenge is that Large Language Models introduce risks that cannot easily be categorized using traditional application security guidance.

Consider a prompt injection attack. The application itself may contain no software vulnerability. The infrastructure may be fully patched. Identity controls may be working exactly as intended. Yet an attacker may still be able to manipulate the AI into leaking sensitive information simply by changing the context of a conversation.

Similarly, an AI agent with excessive permissions may faithfully execute actions it should never have been allowed to perform. From a traditional security perspective, nothing has technically been compromised. The AI simply followed the instructions it was given.

These examples demonstrate why AI security requires its own threat model.

AI Systems Are More Than Models

One of the biggest misconceptions surrounding AI security is that organizations only need to secure the model itself. In reality, a modern AI application is usually made up of multiple components working together.

A typical enterprise AI solution may include a Large Language Model, system prompts, Retrieval-Augmented Generation (RAG), vector databases, enterprise search, connected APIs, AI agents, identity platforms, and multiple business applications. An attacker rarely focuses on just one of these areas. Instead, they look for weaknesses across the entire solution.

For example, an AI assistant connected to SharePoint is not simply answering questions based on what it learned during training. It may retrieve documents in real time, summarize their contents, call additional services, or trigger automated workflows. Securing the language model alone is therefore only one part of protecting the overall solution. Organizations must also consider how information is retrieved, how permissions are enforced, how outputs are validated, and what actions the AI is allowed to perform on behalf of users.

This broader architectural view is one of the reasons the OWASP Top 10 has become such an important reference for AI security. Rather than focusing exclusively on prompt engineering or jailbreak attacks, it examines the complete lifecycle of an AI application and identifies the areas where organizations are most likely to encounter security issues.

The OWASP Top 10 for Large Language Model Applications

The latest OWASP Top 10 for Large Language Model Applications reflects how AI systems are actually being designed and deployed today. Rather than focusing solely on the language model, the framework considers the complete AI application, including Retrieval-Augmented Generation (RAG), AI agents, connected tools, vector databases, enterprise data sources, APIs, and supporting infrastructure.

Each category highlights a different area where organizations should evaluate risk as part of designing, deploying, or assessing AI-powered solutions.

LLM01: Prompt Injection

Prompt injection remains the highest-ranked risk because it targets the way an AI system interprets instructions. Attackers attempt to manipulate prompts or retrieved content so the model ignores its intended behavior, leaks information, or performs actions it should never execute. This includes both direct prompt injection from users and indirect prompt injection through documents, emails, web pages, or other retrieved content.

LLM02: Sensitive Information Disclosure

Large Language Models frequently process confidential information, including customer data, intellectual property, internal documentation, and business records. Weak access controls, poor data governance, or insecure application design can result in the AI exposing information to users who should never have access to it.

LLM03: Supply Chain

AI applications depend on much more than the model itself. Third-party models, open-source frameworks, plugins, APIs, external datasets, and cloud services all become part of the AI supply chain. A weakness anywhere in that chain can affect the security and integrity of the entire application.

LLM04: Data and Model Poisoning

AI systems rely on trusted information to generate reliable responses. Attackers may attempt to poison training data, fine-tuning datasets, embedding stores, or knowledge bases so the model produces inaccurate, biased, or malicious results. For RAG-based applications, poisoned documents can influence responses without changing the underlying model.

LLM05: Improper Output Handling

Many AI systems generate output that is consumed by other applications, whether that is source code, SQL queries, API requests, scripts, or business documents. If those outputs are trusted without validation or sanitization, they can introduce vulnerabilities into downstream systems. AI-generated output should always be treated as untrusted until it has been validated.

LLM06: Excessive Agency

Modern AI agents are increasingly capable of executing workflows, calling APIs, modifying data, approving requests, and interacting with enterprise systems. Excessive agency occurs when an AI is granted more authority than it genuinely requires, increasing the potential impact if the system is manipulated or behaves unexpectedly. Applying least privilege is just as important for AI agents as it is for human users.

LLM07: System Prompt Leakage

System prompts contain the hidden instructions that define how an AI application behaves. If attackers can expose these prompts, they gain valuable insight into the application’s internal design, operating instructions, and security controls. While prompt leakage may not immediately compromise a system, it often provides intelligence that enables more effective attacks.

LLM08: Vector and Embedding Weaknesses

Enterprise AI applications increasingly depend on vector databases and embedding models to retrieve relevant information. Weaknesses within these systems may allow attackers to manipulate retrieval results, poison indexed content, or influence how the model interprets information. Securing the retrieval layer is therefore just as important as securing the model itself.

LLM09: Misinformation

Large Language Models can generate responses that appear highly confident despite being factually incorrect. Hallucinations, biased information, outdated knowledge, or manipulated content can all lead to misinformation. Organizations should ensure that AI-generated information is appropriately validated before it influences business decisions or automated processes.

LLM10: Unbounded Consumption

Large Language Models require significant computing resources, making them vulnerable to resource exhaustion attacks. Attackers may intentionally generate excessive requests, extremely large prompts, or computationally expensive workloads that increase operational costs or reduce system availability. Appropriate rate limiting, usage monitoring, and resource controls are essential to maintaining reliable AI services.

Collectively, these ten categories demonstrate that AI security extends well beyond protecting the language model itself. Secure AI applications require attention to identity, permissions, data governance, retrieval pipelines, connected services, application architecture, and operational controls.

The OWASP Top 10 provides organizations with a practical framework for evaluating each of these areas and identifying where additional security measures may be required.

Benefits for Organizations

The OWASP Top 10 provides organizations with far more than a list of AI vulnerabilities. It establishes a common framework that allows development teams, architects, security professionals, governance teams, and business leaders to evaluate AI systems using a shared understanding of risk.

From a development perspective, the framework helps teams build security into AI solutions from the beginning rather than attempting to add protections after deployment. Architects can use it during solution design to identify trust boundaries, define permissions, and reduce unnecessary exposure. Security teams can use it during threat modeling and AI red teaming exercises, while governance teams can align security policies and risk management processes around recognized industry guidance.

The framework is equally valuable during security assessments. Rather than testing an AI application using random prompts or isolated scenarios, assessors can systematically evaluate each risk category, identify weaknesses, determine potential business impact, and prioritize remediation efforts. This creates a far more consistent and repeatable assessment process while helping organizations focus on the areas that present the greatest risk.

A Starting Point, Not the Finish Line

It is important to understand what the OWASP Top 10 is designed to achieve. It is not a compliance standard, a certification, or a detailed implementation guide. Instead, it is a security awareness framework that identifies the most significant risks facing AI-powered applications today. Much like the original OWASP Top 10, it provides a starting point for understanding where vulnerabilities are most likely to exist and where organizations should focus their attention.

As AI continues to evolve, so too will the techniques used to attack it. New models, autonomous agents, retrieval systems, and AI-powered business processes will continue introducing new security challenges. The OWASP Top 10 provides a strong foundation for understanding those challenges and helps organizations build AI solutions that are secure by design rather than relying solely on reactive security measures after deployment.

Ultimately, successful AI security is not about protecting a single model.

It is about understanding the complete AI ecosystem, recognizing where risks exist, and implementing the people, processes, and technical controls needed to manage those risks throughout the application lifecycle. The OWASP Top 10 for Large Language Model Applications provides one of the best frameworks currently available for achieving exactly that.

Read more about the OWASP Top 10 for Large Language Models

https://genai.owasp.org/llm-top-10/

https://genai.owasp.org/resource/owasp-genai-llm-top-10-2026