OWASP Top 10 for LLMs in 2025: Key Risks and How to Secure LLM Applications

As artificial intelligence (AI) continues to integrate into industries ranging from customer service to software development, the security risks associated with these technologies are becoming more evident. Large Language Models (LLMs), a subset of AI that powers applications like chatbots and content generation tools, have seen widespread adoption. However, as with any evolving technology, vulnerabilities are emerging alongside innovation.

To address these risks, the Open Web Application Security Project (OWASP) has compiled a list of the Top 10 security threats that affect LLM applications. This list serves as a crucial resource for developers, architects, and enterprises looking to enhance their AI security posture. In this blog, we’ll explore why securing LLMs is important, review the latest OWASP Top 10 list, and provide real-world examples of how these vulnerabilities manifest in AI-driven applications.

 

Why AI Security Matters

Securing AI applications is more than just a best practice – it’s essential. As large language models (LLMs) take on critical roles in automated decision-making, financial analysis, and even medical diagnosis, the stakes for security have never been higher. A single breach could expose sensitive data, manipulate outputs, or grant unauthorized access, leading to serious consequences.

Meanwhile, threat actors are constantly adapting, uncovering new ways to exploit LLM applications and API-based LLMs. From prompt manipulation and unauthorized code execution to data leaks, the risks are evolving just as fast as the technology itself. To stay ahead, enterprises need a proactive approach to security—one that anticipates threats before they become costly incidents.

The OWASP Top 10 for Large Language Models in 2025

The OWASP Top 10 list provides a structured overview of the most pressing security concerns related to LLM applications. Each category highlights a specific type of vulnerability, along with real-world examples of exploitation. Let’s take a closer look at these risks and how they impact AI security.

 

1. Prompt Injection

 

Description: Malicious users manipulate input prompts to alter an LLM’s behavior in unintended ways.

• Example: A chatbot designed to provide customer support can be tricked into revealing internal system commands by carefully crafted input prompts.

 

2. Data Leakage

 

Description: Sensitive data stored within an LLM can be extracted through specific queries or inference techniques.

• Example: An AI-powered assistant trained on internal business documents unintentionally discloses confidential information when prompted with cleverly worded questions.

 

3. Inadequate Sandboxing

 

Description: Poorly isolated execution environments can allow attackers to execute unauthorized code or gain control over system functions.

• Example: An LLM-enabled code generation tool inadvertently grants access to system-level commands, leading to security breaches.

 

4. Excessive Agency

 

Description: When LLMs are given too much autonomy, they can take unintended actions, leading to security and operational risks.

• Example: An AI-driven process automation system makes financial transactions without proper oversight, causing financial losses.

 

5. Supply Chain Vulnerabilities

 

Description: API-based LLMs and applications depend on third-party models, libraries, and datasets, which can introduce security risks if compromised.

• Example: An AI model trained using third-party datasets unknowingly integrates backdoor exploits that allow attackers to manipulate outputs.

 

6. Unbounded Consumption

 

Description: LLMs processing unfiltered user inputs can cause excessive resource consumption, leading to denial-of-service scenarios.

• Example: A public-facing AI chatbot is overwhelmed by recursive prompts that cause system crashes due to excessive memory usage.

 

7. Injection into Vectors and Embeddings

 

Description: Attacks that manipulate embedding-based AI functions, such as Retrieval-Augmented Generation (RAG), to distort outputs.

• Example: A malicious actor modifies a knowledge base used by an AI-powered legal assistant, causing it to provide misleading or harmful legal advice.

 

8. System Prompt Leakage

 

Description: Internal prompts used to instruct the LLM can be exposed, revealing sensitive implementation details or system logic.

• Example: Users interacting with a virtual assistant extract hidden system instructions, allowing them to bypass content restrictions.

 

9. Overreliance on AI Outputs

 

Description: Excessive trust in AI-generated outputs without human validation can lead to misinformation or flawed decision-making.

• Example: A news outlet publishes AI-generated articles without review, leading to the spread of incorrect information.

 

10. Insecure Plugin or API Integrations

 

Description: External integrations with LLMs can introduce security vulnerabilities if not properly secured.

• Example: A finance application using an API-based LLM is exploited to approve fraudulent transactions due to weak authentication protocols.

 

Conclusion and Call to Action

As AI adoption grows, BreachLock understands that providing offensive security solutions such as penetration testing for LLMs is critical as the risks associated with LLM deployment increases. The OWASP Top 10 for LLM applications provides a critical framework for understanding and mitigating these threats. Enterprises must prioritize security by:

• Implementing robust access controls and authentication mechanisms for LLM applications.
• Regularly auditing and monitoring AI models for anomalies or security breaches through continuous security testing and penetration testing for LLMs.
• Applying sandboxing techniques to prevent unauthorized code execution.
• Ensuring human oversight in critical AI-driven decision-making processes.
• Securing APIs and external integrations to minimize attack vectors with adversarial emulations through human-driven and continuous penetration testing for API-based LLMs.

Securing LLMs isn’t just about preventing threats—it’s about ensuring AI remains a reliable and trustworthy asset. As these models take on increasingly critical roles, the risks of prompt manipulation, data leaks, and unauthorized access continue to grow.

Enterprises that stay ahead of these challenges can fully embrace AI’s potential without compromising security. Now is the time to adopt best practices, invest in AI security research, and build a resilient AI infrastructure. The future of AI depends on it—how prepared is your organization?

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