OWASP Top 10 for LLM Applications

What is OWASP Top 10 for LLM

The OWASP Top 10 list is a security framework from the Application Security Project OWASP community that identifies the most critical risks in large language model (LLM) systems. It defines common attack patterns and security weaknesses in AI-powered applications, helping organizations build safer generative AI systems. To better understand these risks, it is helpful to examine them individually.

Prompt Injection

Prompt injection attacks occur when an attacker manipulates system prompts or user inputs to override instructions in an LLM. Attackers may trick the model into revealing sensitive data, ignoring policies, or generating harmful LLM outputs. To prevent prompt injections, it’s essential to validate inputs and monitor responses carefully.

Insecure Output Handling

Insecure output handling occurs when applications directly trust LLM outputs without validation. Outputs that are used as code, SQL, or markup can be dangerous. Attackers could exploit them to cause remote code execution, cross-site scripting, or unauthorized actions.

Training Data Poisoning

Training data poisoning involves injecting malicious or biased data into pre-trained models or training datasets. This can create hidden backdoors, skewed outputs, or inconsistent behavior that persists across model usage.

Model Denial of Service

Model denial of service (DoS) refers to attacks that overload LLM systems with expensive or repetitive prompts. These attacks increase compute cost, degrade performance, and may exceed rate limits, disrupting service for legitimate users.

supply Chain Vulnerabilities

Supply chain vulnerabilities arise from risks introduced through third-party open source models, APIs, datasets, or plugins. Compromised dependencies or unverified components can introduce hidden security flaws into LLM applications.

Sensitive Information Disclosure

Sensitive information disclosure occurs when LLMs unknowingly expose confidential information, such as API keys, internal documents, or other sensitive data. This often results from poor data handling, insecure prompts, or untested integrations.

Insecure Plugin Design

Insecure plugin design refers to weakly secured integrations between LLMs and external tools. Poor access control, excessive permissions, or unsafe APIs can allow attackers to trigger unauthorized actions through the model.

Excessive Agency

Excessive agency occurs when LLMs are granted too much autonomy to perform real-world actions without human oversight. This increases the risk of unintended operations in systems such as finance, communications, or infrastructure.

Overreliance

Overreliance describes situations where users fully trust large language models (LLMs) outputs without verification. Because models can hallucinate or generate incorrect information, blind trust can lead to security, operational, or compliance failures.

Model Theft

Model theft involves unauthorized extraction or replication of proprietary LLMs. Attackers may use API abuse, query-based reconstruction, or reverse engineering to steal pre-trained models or model behavior.

How can organizations secure LLMs?

Input validation and output control

Organizations should validate all user inputs to reduce the risk of prompt injection attacks and to help ensure that LLM outputs are safe before execution or display. Because LLMs can interpret inputs in unpredictable ways, their responses should always be treated as untrusted and carefully checked. This is especially important when outputs interact with code, databases, or system commands, where unvalidated responses could trigger security vulnerabilities or unintended actions.

Access control and least privilege

LLM-connected tools, APIs, and plugins should be granted only the minimum permissions necessary for their functions, following the principle of least privilege. Limiting permissions reduces the potential damage if a prompt is compromised or an integration is abused. Implementing strong access control measures ensures that each user or component can only perform actions they are explicitly authorized to, further minimizing security risks.

Data isolation and privacy protection

Sensitive data should be separated from general LLM prompts. Organizations should avoid exposing confidential information in model context windows and apply encryption and strict data governance policies. These practices help reduce the risk of sensitive information disclosure, maintain regulatory compliance, and protect user and business data from unintended leaks.

Monitoring and anomaly detection

Continuous logging and monitoring can reveal abnormal usage. Examples include repeated high-cost queries, suspicious prompt behavior, or unusual API spikes that can be detected. Early detection reduces the impact of denial of service (DoS) and abuse attempts.

Secure development lifecycle for AI systems

AI applications should follow a structured security testing, including red teaming, adversarial prompt testing, and dependency review. Regular audits of training data poisoning, LLM security, and integrations help identify emerging risks.

Web Application Firewall (WAF) protection

A Web Application Firewall (WAF) should be deployed in front of LLM APIs and AI gateways to filter malicious traffic. A WAF can help block prompt injection attacks, enforce rate limits, and reduce automated abuse or denial-of-service attacks targeting LLM endpoints.

FAQ

Why is OWASP Top 10 for LLM Applications important?

OWASP Top 10 for LLM Applications helps organizations identify and prioritize critical AI security risks, improve secure development practices, and reduce vulnerabilities in large language model systems, APIs, plugins, and third-party integrations.

How do LLM security vulnerabilities differ from traditional application vulnerabilities?

LLM security vulnerabilities arise from unpredictable language behavior, prompt manipulation, and potential data leaks. Meanwhile, traditional vulnerabilities usually target specific code errors, misconfigured systems, or network weaknesses.

What are the most common security risks in LLM applications?

Prompt injection, insecure output handling, training data poisoning, sensitive information disclosure, excessive agency, and supply chain vulnerabilities are among the most common security risks affecting large language model applications.

What is prompt injection?

Prompt injection is an attack technique where crafted user inputs manipulate an LLM. Such attacks can cause the model to ignore system prompts or reveal sensitive information by exploiting how models interpret natural language instructions.