OWASP LLM: Top 10 LLM Vulnerabilities & AI Security Practices for 2025

What is OWASP LLM?

OWASP LLM, also known as the OWASP Top 10 for Large Language Model Applications, is a focused effort by the OWASP Foundation to catalog and raise awareness about the unique security vulnerabilities found in applications built using LLMs. These risks are different from traditional web app threats and require new approaches to mitigation, testing, and secure development.

This framework supports developers, auditors, and AI product teams in identifying and resolving the most pressing LLM vulnerabilities—from prompt injection and model theft to insecure plugin design and data leakage.

Why LLM Vulnerabilities Matter

Large Language Models, such as ChatGPT or Claude, are increasingly embedded in business-critical systems—from customer service bots to finance, healthcare, and legal operations. These models introduce new attack surfaces due to their probabilistic nature, input/output complexity, and reliance on massive datasets.

Key vulnerabilities include:

• Prompt injection, where malicious inputs trick the model into executing unintended actions.
• Insecure outputs that result in XSS, phishing, or harmful content.
• Model exploitation, allowing adversaries to reverse-engineer, steal, or manipulate the LLM.

As these models gain more access to internal systems and automation tools, LLM vulnerabilities can lead to major data breaches, compliance issues, and reputation damage.

OWASP Top 10 for Large Language Model Applications (2025)

The OWASP LLM Top 10 was most recently updated in November 2024. It outlines the most significant threats impacting LLM-based applications, helping organizations secure their generative AI infrastructure.

1. Prompt Injection

Manipulating model behavior by injecting adversarial prompts, often leading to logic override or data disclosure.

2. Insecure Output Handling

LLM responses may include untrusted or dangerous content passed directly to users or other systems.

3. Training Data Poisoning

Corrupting the dataset used to train or fine-tune the LLM, introducing backdoors or biased behavior.

4. Denial of Service via Resource Abuse

Malicious users can overload LLMs with complex prompts, degrading performance or taking them offline.

5. Supply Chain Vulnerabilities

Dependencies on external plugins, APIs, or models can introduce hidden risks and attack paths.

6. Sensitive Information Disclosure

LLMs might leak confidential or proprietary data inadvertently included in training or logging.

7. Insecure Plugin Design

LLM plugins with excessive privileges or unsafe execution pathways can become major security liabilities.

8. Excessive Agency

Autonomous agents powered by LLMs may take unpredictable actions without sufficient oversight or constraints.

9. Overreliance on LLM Output

Blindly trusting AI-generated content without validation can lead to misinformation or compliance risks.

10. Model Theft

Attackers can replicate LLM behavior or extract model weights through API abuse or fine-tuning leakage.

For more detail, consult the official OWASP LLM Top 10 resource.

Exploring OWASP ML Top 10 vs. LLM Top 10

The OWASP ML Top 10 addresses broad machine learning risks, such as adversarial inputs, data poisoning, model inversion, and algorithmic bias. It’s ideal for teams working on computer vision, fraud detection, or classic predictive models.

In contrast, the OWASP Top 10 for Large Language Models focuses specifically on generative models and conversational AI. These systems are vulnerable to prompt injections, hallucinations, insecure outputs, and unauthorized plugin execution.

Understanding the difference helps you apply the right security posture depending on your AI use case.

Mitigation Strategies

Mitigating OWASP LLM risks requires a layered approach combining secure development, continuous testing, and real-time monitoring. Here are some best practices:

• Separate system and user prompts to prevent injection and confusion.
• Sanitize all inputs and outputs to avoid dangerous content execution.
• Monitor and rate-limit API access to control resource consumption and DoS risk.
• Vet training datasets and plugin code for integrity and safety.
• Enable detailed logging and alerting to detect unauthorized behavior or data leaks.
• Partner with audit and monitoring services like FailSafe to stay ahead of evolving threats.

FailSafe enhances LLM security by offering comprehensive audit reports and live anomaly detection, ensuring rapid mitigation when vulnerabilities surface post-deployment.

Conclusion

The OWASP LLM Top 10 represents a crucial evolution in the world of application security. As LLMs become foundational components in software and operations, their unique vulnerabilities demand equally specialized defense strategies.

Whether you’re building a chatbot, deploying AI-powered plugins, or scaling an enterprise-grade AI assistant, incorporating these LLM security best practices—and leveraging end-to-end protection platforms like FailSafe—can drastically reduce the risk of exploitation.

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