OWASP LLM Top 10: The Biggest Security Risks in AI You Can’t Ignore

The power of Large Language Models (LLMs) is reshaping how businesses operate. This includes chatbots and virtual assistants. It also extends to autonomous agents and embedded tools. But with that power comes new, often misunderstood risks.

That’s why the OWASP Top 10 for LLM Applications was created. It provides developers and security teams with a focused framework to understand the most critical AI-specific vulnerabilities. This helps them mitigate these vulnerabilities in 2025.

Here’s a high-level overview of the 10 biggest LLM security concerns, straight from the official OWASP guidance.

The OWASP LLM Top 10 for 2025

LLM01:2025 – Prompt Injection

Attackers manipulate prompts—directly or indirectly—to change how the model behaves. These attacks can bypass safety instructions, leak internal data, or cause the LLM to perform unintended actions.

Example: Injecting commands via user input like blog comments or emails that the LLM later interprets during processing.

LLM02:2025 – Sensitive Information Disclosure

LLMs might leak private, confidential, or internal information—either memorized from training data or exposed through system prompts.

Example: A chatbot accidentally revealing internal API keys or past conversation data.

LLM03:2025 – Supply Chain Vulnerabilities

LLM apps often rely on third-party models, datasets, libraries, or plugins. A single vulnerable dependency can compromise the entire AI system. Want to dive deeper into AI vulnerability? Explore more in Your AI Plugin Might Be a Backdoor: The Hidden Risk of LLM Integrations.

Example: Using an open-source LLM plugin that allows attackers to execute unintended API calls.

LLM04:2025 – Data and Model Poisoning

Attackers introduce malicious data during training or fine-tuning phases. This can corrupt model behavior, introduce bias, or insert backdoors that get triggered during inference.

Example: Poisoning public forums or GitHub projects that are later used as training data.

LLM05:2025 – Improper Output Handling

LLMs can generate toxic, false, or harmful responses. Without output filtering or validation, these results may reach end-users or be passed to other systems.

Example: An LLM-powered legal assistant suggesting fake legal citations that sound real.

LLM06:2025 – Excessive Agency

LLMs are often given permissions to take real-world actions—sending emails, executing code, modifying files. If these permissions are too broad or unmonitored, they can be abused.

Example: A prompt causes the LLM to delete files or send unauthorized messages.

LLM07:2025 – System Prompt Leakage

The internal instructions or “system prompt” that governs the AI’s behavior can be exposed through crafted user queries. Once leaked, attackers can manipulate it more easily.

Example: Asking the model “What were you told to do?” and getting a revealing answer.

LLM08:2025 – Vector and Embedding Weaknesses

Attackers can exploit flaws in vector databases and embedding pipelines used for retrieval-augmented generation (RAG). These weaknesses may cause injection, inference attacks, or irrelevant data retrieval.

Example: Modifying content so it gets wrongly retrieved in a semantic search.

LLM09:2025 – Misinformation

LLMs can generate confident but false information—a phenomenon known as “hallucination.” This undermines trust and can have serious consequences in high-risk domains like healthcare or finance.

Example: An AI medical assistant offering incorrect dosage recommendations.

LLM10:2025 – Unbounded Consumption

LLMs consume compute and memory aggressively. Without proper controls, malicious actors can flood the system with long prompts or nested prompts. This can lead to denial-of-service or inflated cloud bills.

Example: Users submitting huge prompts that stall or crash the backend.

Conclusion

The OWASP LLM Top 10 isn’t about theory—it’s based on real-world risks being exploited today. If you're building LLM-powered tools, this list should guide your design, development, and defense strategies.

From prompt injection to plugin abuse, these are the blind spots we must illuminate. AI needs to remain safe, trusted, and beneficial.