OWASP LLM Top 10: A Practical Guide for Security Teams

Ever watched a simple chatbot get tricked into sharing confidential company information or running an unwanted script? Large Language Models are helping organizations automate customer support, create content, review documents, and improve workplace productivity faster than ever. While these advanced systems create new opportunities for businesses, they also bring security challenges that traditional cybersecurity tools were not designed to manage. Advisory specialists at T3 help organizations strengthen AI governance by improving visibility across systems, managing technology risks, and creating safeguards that support responsible AI adoption. 

Understanding the OWASP LLM Top 10 gives security teams a clear roadmap to identify common vulnerabilities, address security threats early, and build safer AI environments before serious incidents occur.

Key Takeaways

• The OWASP LLM Top 10 identifies the most common security risks affecting Large Language Model applications.

• Security measures need to be put in place for AI systems that go beyond cybersecurity.

• Organizations can mitigate security risks associated with AI through governance, testing, and periodic reviews.

• AI applications should be considered in the context of their entire life cycle within security teams.

• Strong governance creates greater visibility and accountability for enterprise AI systems.

What Is the OWASP LLM Top 10?

The OWASP LLM Top 10 is a security awareness resource created to help organizations understand the most important risks associated with Large Language Model applications. It highlights vulnerabilities that can affect AI systems during development, deployment, and day-to-day use.

Unlike traditional application security guidance, this framework focuses on challenges that are unique to AI-powered systems. It prompts organisations to be aware of security issues at an early stage and minimise the risk for their business before the widespread adoption of AI solutions.

Why is LLM Security Important?

Large Language Models may be handling sensitive data, including financial information, customer data, and internal systems. In the absence of effective governance, these applications can lead to disclosure of sensitive information or react in a manner that raises operational or compliance issues.

The following are some common security risks associated with LLM:

• Unauthorized access to information that is sensitive or confidential.

• Prompt injection attacks that manipulate AI responses

• Insecure integrations with external applications

• Exposure of confidential business information

• Excessive permissions that allow unauthorised or unintended actions

Managing these risks also supports stronger AI risk management by helping organizations identify weaknesses before they affect business operations.

The OWASP LLM Top 10 Explained

The framework outlines a number of areas of security that organizations need to be aware of prior to deploying AI applications.

These risks are reviewed to identify areas that may need to have better governance or security controls.

How Security Teams Can Reduce AI Risks

Security should be part of every stage of AI deployment rather than being added after systems are already in use. To enhance the safety of AI usage, regular reviews, governance processes, and testing are employed.

Security teams should take the following measures:

• Restrict access to sensitive information for AI.

• Regularly check the monitor prompts and the AI-generated results.

• Discuss access, approval, and other permissions for AI use.

• Document AI systems throughout the organisation.

• Regular security testing and governance reviews.

Maintaining an AI asset inventory also gives organizations better visibility into where AI systems are being used. AI risks can also be assessed by businesses in advance and vulnerabilities identified before AI applications hit critical areas of business operations.

Good governance, backed by an AI governance framework, can enable organizations to create safer AI environments and help them achieve long-term goals.

How AI Governance Supports LLM Security

While security measures are crucial, it’s equally essential to have a robust governance framework in place to effectively protect against AI threats. To ensure organisations know what is happening to AI systems, and how potential risks are being addressed, they need clear responsibilities, monitoring and review processes.

An effective enterprise AI governance approach improves visibility across AI applications while promoting accountability between technical and business teams.

Key governance practices include:

• Defining ownership for AI systems and related security processes

• Reviewing AI applications before deployment

• Developing procedures on acceptable use of AI

• Monitoring the changes of the AI systems over time

• Maintaining documentation for security and compliance reviews

For organizations aiming to meet regulations regarding AI, it is crucial to take security, privacy, and governance into account during the entire lifecycle of AI.

AI governance consulting services can assist companies in establishing formal procedures to handle AI dangers. Moreover, AI model testing and assurance enable teams to assess AI systems prior to wider implementation.

Common Pitfalls in LLM Security

Security problems often arise because of the rapid adoption of AI tools without adequate control measures. To develop more effective protection strategies, teams need to be aware of the common pitfalls.

Common problems are:

• Using AI applications without security audits.

• Permitting access to confidential information.

• If not properly controlled, the input and output of AI can be harmful.

• Using third-party AI tools without proper evaluation

• Not assigning ownership for AI systems

Ignoring these areas can increase exposure to security threats and reduce confidence in AI adoption. Following AI security best practices helps organizations create safer processes while reducing risks linked to AI usage.

Building a Secure AI Environment

Building secure AI systems involves implementing technology controls, governance protocols, and periodic assessments. Security teams need to audit the use of AI from the design phase until it is in use.

To enhance the security of AI, organizations can:

• Developing clear policies for AI use
• Checking system permissions on a regular basis.
• Keep track of the performance and behavior of AI.
• Assessing third-party AI providers
• Updating security processes as AI systems evolve

Businesses can leverage AI to maintain the right level of security, transparency, and accountability with a strong focus on Responsible AI adoption.

The Role of Testing and Assessment in LLM Security

As models, data sources, and connected tools evolve, the risks associated with AI systems may change. Regular evaluations are therefore essential. Testing can help organizations realize their strengths and weaknesses first, before they develop into bigger business concerns.

Security teams can use assessment processes to review:

• Model behavior
• Data handling practices
• Access permissions
• Application vulnerabilities
• Compliance requirements

When combined with effective governance, security reviews can help organisations strengthen the controls and oversight applied to AI systems. Regular evaluation also contributes to improved AI risk management, and it enables better control of the AI environment.

Final Thoughts

Large Language Models are becoming an important part of modern business operations, but their adoption also introduces new security responsibilities. Understanding the OWASP LLM Top 10 helps organizations identify common vulnerabilities and create stronger safeguards around AI applications.

Building secure AI systems requires more than technical controls. To responsibly manage AI, organizations must have governance processes, risk reviews, and monitoring.

T3 helps organisations strengthen AI governance by improving visibility, risk assessment, and oversight across enterprise AI systems.

Ready to improve your organization’s AI security strategy? Partner with T3 to build stronger governance processes, evaluate AI risks, and create a more secure foundation for responsible AI adoption.

FAQs

1. What is the OWASP LLM Top 10?

The OWASP LLM Top 10 is a security resource to identify common vulnerabilities in LLM apps. It aids organizations in comprehending and mitigating AI-specific security threats.

2. Why are LLM security risks different from traditional cybersecurity risks?

LLM applications introduce unique challenges such as prompt manipulation, data exposure, and unexpected model behavior that require additional security considerations.

3. What are some methods to mitigate LLM security risks?

Organizations can minimize the risks by establishing governance processes, auditing AI applications, monitoring outputs, access control, and adherence to AI security best practices.

4. Why is AI governance important for LLM security?

AI governance enables organizations to clarify roles, implement security measures, track the performance of AI systems, and ensure effective management of their AI interactions.

5. What is the role of AI testing in enhancing security?

AI testing can uncover vulnerabilities, analyze system performance and ensure that AI-driven applications are secure and compliant with governance policies.