Large Language Models have become the foundation of modern AI products.

They're powering:

• Chatbots
• Coding assistants
• AI agents
• Search systems
• Enterprise copilots

But as organizations move AI systems into production, a new reality is becoming clear:

AI systems introduce entirely new security risks.

A single prompt, poisoned document, malicious plugin, or leaked training dataset can turn a helpful assistant into a security incident.

That's why OWASP's updated Top 10 for Large Language Models matters so much. It highlights the most common ways attackers exploit LLM-powered systems today and what developers should do about them.

Why LLM Security Is Different

Traditional software follows predefined rules.

LLMs don't.

Instead, they generate responses based on probabilities, context, and learned patterns.

That flexibility makes them powerful.

It also makes them unpredictable.

The challenge is that attackers can often influence every stage of this process.

1. Prompt Injection

Prompt injection remains the number one risk facing LLMs.

Despite years of research, it remains one of the hardest vulnerabilities to eliminate.

The problem occurs because models struggle to distinguish between:

• Instructions
• User input
• External content

An attacker can submit prompts that override the intended behavior of the model.

For example:

More dangerous are indirect prompt injections.

These attacks hide malicious instructions inside:

• Documents
• Web pages
• Emails
• PDFs

The user appears legitimate, but the content being processed contains hidden instructions that manipulate the model.

Defenses

• Strong system prompts
• AI firewalls and gateways
• Output filtering
• Red-team testing
• Prompt injection penetration testing

2. Sensitive Information Disclosure

Many organizations train or augment models using sensitive data.

Examples include:

• Customer records
• Financial information
• Healthcare data
• Internal business documents
• Intellectual property

Without proper controls, attackers may extract that information through carefully crafted prompts.

Another risk is model extraction.

An attacker repeatedly queries the model and gradually reconstructs proprietary information over time.

This is sometimes called a:

Model Inversion Attack

Defenses

• Data sanitization
• Strong access controls
• Encryption
• Output monitoring
• AI security posture management

3. Supply Chain Vulnerabilities

Most organizations don't build models from scratch.

Instead, they depend on:

• Open-source models
• Datasets
• Libraries
• Infrastructure
• Third-party tools

Every dependency introduces risk.

A compromise anywhere in that chain can affect the final system.

Defenses

• Vendor verification
• Provenance tracking
• Security scanning
• Red-team testing
• Regular patching

4. Data and Model Poisoning

Data is the lifeblood of an LLM.

If attackers poison training data, they can influence future model behavior.

Sometimes the manipulation is subtle.

A small amount of bad data can introduce:

• Bias
• Incorrect reasoning
• Hidden behaviors
• Reduced reliability

Poisoning can also affect RAG systems.

If attackers compromise retrieval sources, the model may consistently produce inaccurate responses.

Defenses

• Verify data sources
• Restrict modification access
• Implement change controls
• Audit RAG datasets
• Continuously validate outputs

5. Improper Output Handling

Many organizations directly consume LLM outputs.

Examples include:

• Generated code
• SQL queries
• Browser content
• Infrastructure configurations

This creates downstream risk.

If the model produces unsafe output, it may introduce:

• Cross-site scripting (XSS)
• SQL injection
• Remote code execution
• Security vulnerabilities

Defenses

Never trust model output automatically.

Validate and sanitize everything before execution.

6. Excessive Agency

Modern AI systems increasingly have access to:

• APIs
• Tools
• Databases
• Infrastructure
• Real-world systems

The more power an agent has, the greater the potential damage if it is compromised.

This is known as:

Excessive Agency

A successful prompt injection against a highly privileged agent can quickly become catastrophic.

Defenses

• Least privilege access
• Approval workflows
• Sandboxed execution
• Permission boundaries

7. System Prompt Leakage

System prompts often contain:

• Rules
• Policies
• Internal logic
• API keys
• Credentials

If exposed, attackers gain valuable information about how the system operates.

In some cases, attackers can trick the model into revealing parts of the system prompt itself.

Defenses

• Never store secrets in prompts
• Use external secret management
• Filter sensitive outputs
• Audit prompt content regularly

8. Vector and Embedding Weaknesses

Many AI applications rely on:

• Vector databases
• Embeddings
• Retrieval-Augmented Generation (RAG)

These systems help models access external knowledge.

But they can also become attack vectors.

If attackers poison RAG documents, they may influence future responses and decision-making.

Defenses

• Validate retrieved content
• Verify knowledge sources
• Monitor retrieval pipelines
• Separate trusted and untrusted content

9. Misinformation

Even when no attacker is involved, LLMs can still generate inaccurate information.

Hallucinations remain one of the biggest operational challenges in AI.

The danger increases when organizations make decisions based on generated outputs.

Developers should treat model outputs as:

Recommendations, not facts.

Defenses

• Human review
• Fact checking
• Source validation
• Multi-step verification

10. Unbounded Consumption

The final OWASP risk is:

Unbounded Consumption

This is essentially a denial-of-service problem.

Attackers can overwhelm AI systems through:

• Massive prompt volumes
• Long-running requests
• Expensive reasoning tasks
• Large context windows

Some organizations call this:

Denial of Wallet

Because even if the service stays online, excessive usage can create enormous AI infrastructure costs.

Defenses

• Rate limiting
• Token quotas
• Request budgets
• Usage monitoring
• Cost controls

Security Is Becoming an AI Requirement

The most important lesson from the OWASP Top 10 is simple:

AI security is now application security.

The same organizations building AI agents, copilots, and chat systems must also think about:

• Access control
• Data protection
• Supply chain security
• Monitoring
• Governance

Ignoring security is no longer an option.