Building Secure LLM Applications: A Practical 14-Point Checklist Based on OWASP 2026

Posted on Nov 2 • Originally published at aml-hassan-abd-el-hamid.github.io

I started writing this article while reading the OWASP Top 10 for LLM Applications 2026. I wanted to learn more about how to build secure LLM-powered applications, and that series of articles was my best friend while doing so. The OWASP Top 10 for LLM Applications 2026 is a collection of 10 articles that discuss 10 different security issues related to LLM-powered applications.

Each article covers an issue and a list of strategies to help prevent it -with a very good list of references that you should defiantly check-. I was taking notes while reading that series to build a to-do list of elements to check every time I build an LLM-powered app, to make sure that my app follows the basic security rules. Once the list was done, I decided to share it so others could use it and maybe contribute to it!

I still highly recommend reading all ten articles to learn more about each security issue in detail. You can download the PDF containing the articles from here. I divided my list into 4 buckets with 14 points (a lot of points, I know 😄).

I discuss every point below, but you can see the entire list without details here:

1- Least Privilege / Minimize Scope, Tools & Permissions and enforce that 2- Server-Side, Chunk/Tenant-Level Access Control for Retrieval (RAG/Vector) 3- Human-in-the-Loop Approval for High-Impact/Irreversible Actions 4- Sandboxing & Isolation of Untrusted Data/Tools/Execution

5- No Secrets or Sensitive Data in Prompts/Hidden Context 6- Input Validation & Sanitization 7- Trust-Boundary Separation & Provenance Labeling

8- Output Schema Validation, Encoding & Safe Rendering 9- Grounding / Verify-Before-Act (“Claim-Check-Act”)

10- Rate Limiting, Quotas & Spending/Resource Caps 11- Know & Verify What You’re Building On 12- Test Your Defenses Like an Attacker Would 13- Monitoring, Logging, Anomaly Detection & Incident Response 14- Protect Your Data Wherever It Goes

Why: This practice is useful for minimizing the damage that can be caused by prompt injection. Even if malicious instructions are sneaked into the LLM’s input, they won’t be as harmful because the LLM doesn’t have enough leverage to cause major damage. It also guards against unbounded consumption, as this practice limits how much damage a runaway loop or recursive call can actually do, even if it fires thousands of times.

Narrowing access only helps if that boundary is actually enforced: if the LLM itself is trusted to decide whether a specific action is appropriate, a convincing injection or hallucination can still talk it into misusing the access it does have. That check needs to happen in your own application code every time a tool is called, not in the model’s judgment.

This is actually complementary to point 1. It's not enough to limit what the LLM can access through its tools; you also need to make sure that the retrieval system itself only returns information that the current user is allowed to see.

This is especially important in multi-tenant applications, where different users or organizations share the same database. Without proper access control, one user might be able to retrieve another user's private information, even if the LLM itself doesn't have any direct access to it.

LLMs can hallucinate, misunderstand instructions, or be manipulated through prompt injection. And when an LLM-powered application has access to tools that can perform real-world actions, even a small mistake can have serious consequences.

Requiring human approval before performing high-impact or irreversible actions adds another layer of protection. It gives someone the opportunity to review what the LLM is about to do before the action actually happens.

This practice is especially important when your application allows code execution. You don't want generated code to have direct access to your production environment, sensitive files, or internal services.

LLMs can generate incorrect code, suggest non-existent packages, or produce something malicious after being manipulated through prompt injection. Running that code in an isolated environment limits the damage it can cause, even if something goes wrong.

Including secrets, credentials, or API keys in your system prompts can introduce two major problems:

The important thing here is that the LLM shouldn't need to know your secrets in the first place. Your application should handle sensitive information without exposing it to the model.

This practice is important for preventing attacks such as prompt injection, but it also helps prevent users from using your application in ways it wasn't designed for.

You should have a way to understand what the user is trying to do and check whether that request falls within the intended use cases of your application before passing it to the LLM.

This point is different from point 1. Point 1 is about limiting what the LLM can do even if it gets manipulated, while this point is about making that manipulation harder in the first place.

The problem is that an LLM doesn't naturally understand the difference between instructions and data. Both are just tokens in its input. So, a malicious instruction hidden inside a retrieved document or a web page might look just like a legitimate command to the model.

By keeping external content structurally separate from your system instructions and clearly labeling where that content comes from, you make it harder for the model to confuse untrusted data with instructions it should follow.

This practice is important for several reasons:

LLMs can confidently produce incorrect information, and sometimes that information can be outdated even if it was correct at some point.

This practice is about making sure that important claims are checked against a real and reliable source before your application treats them as facts or takes action based on them.

In other words, don't let the LLM's confidence be the only reason you trust something!

This practice becomes especially important when your other defenses against prompt injection and unintended behavior fail.

An LLM-powered application can consume a lot of resources, especially when agents are involved. A single request might trigger repeated model calls, recursive tool calls, or an endless loop. Without proper limits, a relatively small issue can turn into a very expensive one.

Having strict and reasonable limits on how much your application can consume helps contain the damage before it gets out of control.

When building an LLM-powered application, you're not only relying on your own code. You're also relying on external models, packages, datasets, and sometimes websites.

The problem is that these resources can be tampered with. A model might contain a backdoor, a dataset might have been poisoned, or an external package might introduce malicious behavior.

You need to verify the resources you're building on instead of assuming that everything publicly available is trustworthy.

You might need a friend to help you with this one!

Testing your application only against attacks you're already familiar with can give you a false sense of security. Your defenses might look great on paper but fail when someone discovers a different way to attack your system.

You need to approach testing from the attacker's perspective. Try to understand how someone could manipulate your application, bypass its defenses, or exploit the way its different components interact. That's how you find out whether your security measures actually work instead of simply assuming they do.

You need to know what's happening inside your application, and you need to be able to investigate what happened when something goes wrong.

Keeping proper logs and monitoring your system helps you understand its behavior, identify performance degradation, and catch suspicious activities before they turn into bigger problems.

For example, an unusual spike in tool calls or resource consumption might be an early sign of an attack or a recursive loop. If you're monitoring these patterns, you have a chance to intervene before the damage gets worse.

Your application's data needs protection, not just when it's stored, but also when it's being transferred or processed.

You should encrypt sensitive information wherever possible and use confidential computing when you need to protect data during processing. These measures work alongside access controls and input/output validation; they don't replace them.

If you got to the end of the article, then I want to congratulate you and say thank you so much! I know that's a lot of points to go through:)

I hope you enjoyed reading this article, and more importantly, I hope this checklist can be useful whenever you're building an LLM-powered application.

And if you have any additional points you'd like to add, or any comments or feedback, please let me know! I'd love to hear them.

My LinkedIn: Aml Hassan Esmail | LinkedIn