🛡 OWASP Top 10 For LLM Applications

Learn the most important risks affecting AI systems and how security teams evaluate modern LLM applications.

🏗 Traditional App vs AI App

Traditional Application:

User → Application → Database

AI Application:

User → AI → RAG → Tools → APIs → Database

The attack surface becomes much larger.

📖 Why OWASP Created This List

Security teams needed guidance for:

• AI Chatbots
• AI Agents
• RAG Systems
• Enterprise AI Platforms
• Autonomous Workflows

The OWASP LLM Top 10 highlights the most critical risks.

🚨 Key OWASP LLM Risks

🎯 Risk #1: Prompt Injection

An AI system receives instructions from users.

The challenge:

Attackers may attempt to manipulate how the AI behaves.

This is one of the most important AI security risks today.

We’ll cover it in the next chapter.

🔓 Risk #2: Sensitive Information Disclosure

Organizations sometimes connect AI to:

• Internal Documents
• Customer Records
• Source Code
• Business Data

Poor controls may expose information unintentionally.

📦 Risk #3: Supply Chain Risks

Modern AI systems often depend on:

• Models
• Plugins
• Libraries
• APIs
• Vector Databases

Every dependency introduces trust decisions.

☣️ Risk #4: Data Poisoning

AI systems rely heavily on data.

If bad or manipulated data enters the system:

• Outputs may become unreliable
• Decisions may be affected
• Trust may be reduced

📤 Risk #5: Improper Output Handling

Many developers trust AI output automatically.

This can create risk.

AI-generated output should be validated before being used by applications or workflows.

🤖 Risk #6: Excessive Agency

AI Agents may have access to:

• Email
• Cloud Systems
• Databases
• Business Applications

Too many permissions create risk.

This mirrors the cybersecurity principle of Least Privilege.

📜 Risk #7: System Prompt Leakage

Many AI systems contain hidden instructions.

Examples:

• Business Rules
• Workflows
• Operational Logic

Organizations often want these protected.

🗂 Risk #8: Vector Database Risks

RAG systems frequently use:

Vector Databases

These may contain:

• Internal Documents
• Policies
• Knowledge Bases

Access control remains critical.

👨‍💻 AI Security Architect Checklist

• What data can the AI access?
• What tools can the AI use?
• What permissions exist?
• What outputs are trusted?
• How is sensitive data protected?

These are core AI security review questions.

🏗 Secure AI Model

Validation should occur before and after the model.

🏆 Key Lesson

AI systems introduce new capabilities.

New capabilities create new risks.

Secure AI Requires Security By Design

🎯 Prompt Injection Attacks

Learn the most important AI security risk today, how prompt injection works conceptually, why it affects AI systems, and how defenders design protections against it.