Blog
Understanding the OWASP Top 10 for LLM Applications and Agentic AI
July 31, 2026
time
Large language models (LLMs) have quickly become part of modern healthcare. Organizations are using them to draft clinical documentation, summarize patient records, power virtual assistants, automate administrative tasks, and improve patient engagement. While these capabilities offer significant benefits, they also introduce security risks that traditional application security frameworks were never designed to address.
To help organizations identify and mitigate these risks, the Open Worldwide Application Security Project (OWASP) developed the OWASP Top 10 for LLM Applications, followed by expanded guidance for Agentic AI systems. These resources have become some of the most widely referenced security checklists for organizations building or deploying AI applications.
For healthcare providers, understanding these risks is essential to protecting patient data, maintaining trust, and strengthening AI governance.
The OWASP Top 10 for LLM Applications is a community-driven list of the most significant security risks affecting applications powered by large language models.
Unlike traditional web application security guidance, which focuses on issues such as SQL injection or cross-site scripting, the LLM Top 10 addresses risks unique to generative AI, including prompt injection, sensitive information disclosure, insecure plugin integrations, and overreliance on AI-generated content.
The framework helps developers, security teams, compliance professionals, and healthcare organizations understand where AI systems may be vulnerable and how those risks can be managed.
Healthcare AI applications frequently process sensitive information and support workflows that directly affect patient care.
Examples include:
If these systems are not properly secured, they may expose confidential information, generate inaccurate responses, or perform unintended actions.
The OWASP framework provides a practical checklist for identifying these risks before deployment.
<numberList>
Prompt injection occurs when a user attempts to manipulate an AI model into ignoring its intended instructions.
Attackers may try to influence the model through carefully crafted prompts that override safety controls or alter system behavior.
Healthcare organizations should test whether AI consistently follows organizational policies even when presented with unexpected or adversarial inputs.
LLMs may unintentionally expose confidential information if appropriate safeguards are not in place.
Potential risks include:
Organizations should implement strict access controls, data filtering, and monitoring to reduce the likelihood of unauthorized disclosure.
Many AI applications rely on multiple external components, including foundation models, APIs, plugins, cloud providers, and third-party libraries.
A weakness anywhere in this ecosystem can affect the overall security of the application.
Healthcare organizations should evaluate vendors, subcontractors, and software dependencies as part of their AI risk management process.
Training data influences how AI systems behave.
If malicious or low-quality data enters the development process, model performance and reliability may suffer.
Organizations should implement controls that verify data quality, protect training datasets, and monitor model behavior after deployment.
AI-generated responses should never be treated as automatically trustworthy.
If applications process model outputs without validation, attackers may exploit those responses to trigger unintended actions or introduce security issues into downstream systems.
Human review and output validation remain essential, especially in healthcare environments.
Some AI systems can perform actions on behalf of users, such as accessing records, sending messages, or interacting with external systems.
Without appropriate restrictions, an AI application may receive more authority than necessary.
Healthcare organizations should apply the principle of least privilege and require approval for high-impact actions.
System prompts define how an AI application should behave.
If these instructions become visible to users, attackers may gain insight into security controls or application logic.
Developers should protect system prompts in the same way they protect sensitive configuration information.
Many modern AI applications use vector databases to retrieve relevant information.
If these databases are not properly secured, attackers may retrieve confidential documents or manipulate search results.
Access controls, encryption, and monitoring are important safeguards for retrieval systems.
Large language models sometimes generate inaccurate or fabricated information that appears credible.
In healthcare, incorrect information may affect clinical documentation, patient communication, or operational decisions.
Organizations should establish review processes for AI-generated content and avoid relying solely on automated responses in high-risk situations.
Poorly designed AI applications may consume excessive computing resources through repeated requests or inefficient workflows.
This can increase operational costs, reduce system availability, and create opportunities for denial-of-service attacks.
Rate limiting, usage monitoring, and resource management help reduce this risk.
</numberList>
Many AI systems no longer respond only to user prompts.
They can now plan tasks, make decisions, access external tools, retrieve information, and complete multi-step workflows with limited human involvement.
These systems are commonly referred to as agentic AI.
While this autonomy increases productivity, it also introduces additional security challenges.
The newer OWASP guidance for agentic AI expands traditional LLM security by focusing on risks introduced when AI systems can act independently.
Examples include:
<numberList>
AI agents should not perform sensitive actions without clearly defined authorization and oversight.
Organizations should establish approval processes for activities that affect patient information or business operations.
Many AI agents interact with external systems such as electronic health records, scheduling platforms, databases, and APIs.
Each integration should be evaluated to ensure the AI cannot misuse connected tools.
Attackers may attempt to influence the decisions made by autonomous AI agents.
Organizations should validate AI-generated actions before execution, particularly when clinical or operational outcomes are involved.
Agentic AI should operate using only the permissions required for its assigned responsibilities.
Excessive permissions increase the impact of compromised or manipulated AI systems.
Healthcare organizations should ensure that qualified personnel remain involved in reviewing important AI-generated decisions.
Human oversight is especially important for workflows affecting patient care, privacy, or regulatory compliance.
</numberList>
The OWASP Top 10 is most effective when combined with broader AI governance practices.
Healthcare organizations often pair it with:
Many AI security tools, including automated red teaming platforms, organize their testing around OWASP risk categories. This makes the framework a practical foundation for evaluating AI applications throughout their lifecycle.
Healthcare providers can reduce AI security risks by following several practical steps.
These practices help strengthen both security and governance.
Generative AI is changing how healthcare organizations deliver services, manage information, and improve operational efficiency. As AI capabilities expand, so do the risks associated with deploying these systems.
The OWASP Top 10 for LLM Applications provides a practical framework for understanding the most common security challenges facing AI-powered applications, from prompt injection and sensitive information disclosure to excessive agency and insecure integrations. The newer guidance for agentic AI extends these principles to systems capable of making decisions and performing actions with greater autonomy.
For healthcare organizations, the framework offers more than a security checklist. It provides a foundation for building trustworthy AI systems that protect sensitive information, support responsible innovation, and maintain patient confidence. When combined with strong governance, vendor due diligence, continuous monitoring, and regular AI security testing, the OWASP guidance can help organizations adopt AI more safely and responsibly.
Have a project in mind? We'd love to hear about it. Tell us what you're building and let's explore what's possible.
hello@globalnodes.com
+91 9873388887
