Understanding the OWASP Top 10 LLM Risks for Safer AI Use

AI is being built into tools and workplaces at a pace nobody expected. It drafts content, answers questions, connects with systems to complete tasks and even autonomous executions using agents recently. This growth looks exciting on the surface, but it quietly opens up many weak points, which we ignore very often until it is too late.

The OWASP Top 10 LLM Risks is a guide that puts those weak points in focus. It does not read like a research paper, but more like a field manual. Each item on the list is based on what has already been seen in real systems. For leaders and teams who are exploring AI, this list works like a map. It helps them see where the trouble begins, so they can prepare before problems reach users.

1. Prompt injection

Prompt injection is one of the easiest ways an attacker can control a model. It works by hiding instructions inside text that looks ordinary. When the model reads the text, it accepts the hidden line as part of the input.

Imagine receiving a long email with an invisible instruction inside it. You read the mail aloud without noticing the line. The instruction has already been followed. A model behaves in the same way when it processes such crafted input.

This risk is powerful because it needs very little skill. Anyone who can place content where the model can read it has a chance to succeed. The damage depends on what the model has access to. If it is linked to files, databases, or tools, the attacker can misuse those connections.

The way to reduce this risk is to treat every input as untrusted. Filters, checks, and isolation layers help stop malicious prompts before they reach the model.

2. Sensitive information disclosure

Sensitive information disclosure happens when the model gives out private data without warning. This may include customer records, company secrets, or even personal details from training data.

The leak may look like a normal response, but it can have heavy results. A chatbot may reveal another user’s details. A support assistant may expose contract terms. Once this information is out, there is no way to pull it back.

The problem often grows when models are directly linked with databases. If a query is crafted carefully, the AI can fetch and share rows of sensitive data. In other cases, the model recalls something it saw during training and shares it as if it is part of its answer.

To control this, outputs must be checked for sensitive information. Data boundaries must be clear. Leaders should decide early on what type of data an AI system should never touch.

3. Supply chain risks

AI systems rarely stand alone. They are built on layers of models, libraries, and datasets. Supply chain risk enters when one of those layers is weak or tampered with.

When teams download pre-trained models or public datasets without review, they inherit whatever problems are inside them. An unverified model could contain hidden behaviors. A library may include code that has backdoors. These issues may stay quiet until the system is in production.

The spread is silent. Once a weak part enters the chain, every system that depends on it carries the same weakness. This is why supply chain problems are so dangerous. They multiply without notice.

Teams need to track the origin of every external part they use. Verified sources, signed updates, and regular audits help reduce the chance of hidden risks creeping in through the supply chain.

4. Data and model poisoning

Data and model poisoning is when harmful or misleading data is added to the training pipeline. The attacker plants poisoned samples inside public sources, or during fine-tuning, and waits for them to be absorbed.

When the model is trained, those poisoned records blend with the rest of the dataset. The poisoned data does not look suspicious during normal checks. The trap only springs when a special input is used later.

This allows the attacker to control how the model behaves. It may respond in a biased way or show content that was never expected. In serious cases, the poisoning creates a hidden backdoor that the attacker can trigger anytime.

The best defense is careful review of training data. Curated sets, audits, and regular monitoring of outputs help in spotting poisoned behavior early.

5. Improper output handling

Improper output handling occurs when teams trust AI responses as if they are always safe. A model can generate text that looks normal but carries unsafe elements.

For example, code generation tools may output scripts that open security holes. An assistant may produce markup with hidden commands. If developers paste this output directly into another system, the unsafe part becomes active.

The risk comes from misplaced trust. An AI response is not automatically safe. It should be checked in the same way user input is checked. Outputs must pass through filters before being used anywhere important.

This is a discipline problem more than a technical one. Teams must build the habit of treating every AI output as untrusted until validated.

6. Excessive agency

Excessive agency happens when a model is given wide control over connected tools. In some cases, the AI can call APIs, edit files, or perform actions without limits.

When this setup is left unchecked, a small misinterpretation can cause large issues. If the model is asked to clean up, it might delete useful files. If it is connected to financial tools, it may send requests that were not meant to go through.

The solution is simple. Give the AI access only to the tools it actually needs. Keep a review layer in place for actions that have a big impact. This keeps sensitive tasks under human control.

7. System prompt leakage

System prompts are the hidden instructions that guide a model’s behavior. They often contain rules and safety lines that shape how the model responds.

When an attacker manages to pull these prompts out, they gain insight into the system’s boundaries. With that knowledge, they can plan inputs that bypass or break the rules.

Leakage often happens through clever questioning. The model tries to answer honestly and ends up revealing parts of its system prompt. This can weaken the entire setup.

The way forward is to design prompts carefully, hide sensitive instructions where possible, and monitor for strange queries that try to force leakage.

8. Vector store weaknesses

Vector stores are used to extend models with external context. They store embeddings that allow fast retrieval of related data.

This setup is useful but also risky. If someone manages to inject harmful embeddings into the store, the model will retrieve and use them. The model does not know that the context is unsafe. It simply treats it as valid and adds it into its answers.

The weakness is often overlooked because vector stores are seen as just storage. In reality, they can act as an entry point for attackers.

Defenses include strict access control, validation of stored data, and review of what the model retrieves.

9. Hallucinations and misinformation

Hallucination is when a model generates content that looks correct but is false. It is one of the most common problems in AI systems.

When the model makes up information, users may accept it as true. In personal use, this is only a small inconvenience. In business or regulated industries, it becomes a serious risk. False legal references, wrong medical details, or inaccurate financial data can all cause harm.

The reason hallucinations happen is because the model predicts patterns. It does not confirm facts. Until better methods are found, hallucination will remain a natural risk of LLMs.

The way to manage this is to pair models with reliable sources, design them to admit uncertainty, and make users aware that every response should be verified.

10. Unbounded consumption

Unbounded consumption is about resource use. Every model query consumes memory, processing power, and tokens. If there are no limits, a user or attacker can overload the system.

This creates problems for cost and performance. Bills may rise without warning, and systems may slow down for genuine users. It can also be used as a way to knock services offline.

The answer is to set quotas and apply usage limits. Monitoring tools can raise alerts when consumption patterns look unusual. This keeps the system available and costs predictable.

Closing thoughts

The OWASP Top 10 LLM Risks is a practical guide for anyone serious about using AI safely. These risks are already visible in real deployments. Ignoring them does not make them disappear.

Teams that study these risks and prepare for them build systems that last longer and win more trust. Those who skip them end up fixing failures after damage is already done.