Security in LLM Applications – What Businesses Need to Know

The decision to integrate an LLM into your product or operations is often made before security has even come up. Everyone wants to move fast. But the security challenges that come with LLM applications are unlike what most organizations are used to – and they require a different way of thinking.

LLM applications are not ordinary applications

If you are building or planning to build an LLM application, you need to account for a type of security challenge that traditional AppSec does not cover. Conventional application security is built on predictability. An application does what it is programmed to do. Vulnerabilities are found, patched, and gone.

LLM applications work differently. The model is non-deterministic – the same input does not always produce the same output. It interprets, reasons, and makes decisions based on context. That makes it powerful, but it also means it cannot be tested and secured the same way as traditional code.

On top of that, LLM applications introduce attack surfaces that simply do not exist in conventional systems. Data flowing into the model – user input, documents, database results, web content – can be manipulated to influence the model's behavior. Agents with tool access can take actions in other systems on the model's behalf – and if manipulated, those actions can be difficult or impossible to reverse. Output can contain malicious code or expose sensitive information from the system prompt that was never meant to reach the user.

LLM applications require a complementary security layer built for their unique characteristics.

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Read our guide: AI Security for Businesses — Risks and How to Test Them

Architectural risk points

Your LLM application likely has several of these layers, and each one introduces its own risks.

The frontend and user input is the most obvious entry point. Without validation and sanitization of incoming data, the application is open to direct prompt injection attacks.

Prompt construction is where system configuration, user input, and retrieved data are assembled before being sent to the model. If this layer is not handled correctly, an attacker can influence how the prompt is composed – and by extension, what the model does.

The RAG pipeline retrieves relevant data from internal sources and adds it to the context. Every data source pulled in is a potential vector for indirect prompt injection. Malicious instructions embedded in a document or database can manipulate the model's responses.

Model calls and API handling involve sending sensitive data to an external provider. This raises questions around data protection, logging, and who has access to what.

Agent tools are the most sensitive layer. An agent with access to file systems, databases, email, or external APIs can cause real damage if manipulated. Limit the blast radius through strict permissions and sandboxing.

Output rendering is the final layer – and one that is frequently overlooked. If the model's output is rendered directly in an interface without sanitization, it can contain malicious code: XSS, injections, or instructions that affect downstream systems.

Data security – prompts, training data, and output

Every time your application calls an LLM, data is sent to an external provider. It is important to understand what happens to that data.

The major providers – OpenAI, Anthropic, Google – have clear policies around data handling for API calls. As a rule, API data is not used to train models without explicit consent. But it is your responsibility to understand the terms, particularly if you are handling personal data or sensitive business information.

The system prompt is a common source of unintentional data leakage. Internal instructions, API keys, or configuration details placed in the system prompt can be exposed if the application is vulnerable to prompt injection. Treat the system prompt as sensitive configuration – not a safe hiding place.

Logging and caching of prompts and output create additional risk points. If logs contain sensitive user data, they need to be protected to the same standard as any other sensitive data in your environment.

Access control in LLM systems

The question of who can prompt your model – and what the model is allowed to do – is central and often underestimated in early implementations.

At the user level: do all users have the right to see the same information in the model's responses? If the application retrieves data from multiple sources with different permission levels, controls need to be in place to ensure users cannot extract information they are not entitled to – directly or through the model's responses.

At the agent level: the principle of least privilege applies just as strictly here as in any other system design. An agent should have access to the tools and data sources it needs for its task – nothing more. Every additional permission is a potential liability if the agent is compromised or manipulated.

Also consider privilege escalation: can a manipulated agent gain elevated permissions? Can it reach systems it was never meant to access? Those questions need answers before you go live.

Read more: AI security for businesses

Monitoring and detection

LLM applications require a different kind of monitoring than traditional systems. Logging requests and responses is not enough – you need to understand what is actually happening in the interactions.

What should be logged: incoming prompts, system prompt versions, data sources retrieved in RAG pipelines, tool calls made by agents, output, and any errors. This gives you the ability to reconstruct what happened in the event of an incident.

Suspicious patterns to watch for: unusually long prompts, repeated attempts to extract the system prompt, unexpected tool calls, output that deviates significantly from expected behavior.

LLM observability is a growing area with dedicated tools for instrumenting and monitoring AI applications. Integrate them into your existing monitoring infrastructure rather than treating AI systems as a special case.

Development process – security from the start

Security in LLM applications is much harder to bolt on after the fact than to build in from the beginning. It requires security thinking to be present at every stage of development.

Threat modeling should happen early. What are the realistic attack scenarios against your specific application? Which data sources are being pulled in? What does the agent have access to? What is the actual damage potential if something goes wrong?

Security requirements in user stories ensure that security is not treated as a separate workstream but as an integral part of what is being built.

Testing in CI/CD should include automated testing against known prompt injection patterns, and regression tests that catch changes in security behavior when the model or prompt is updated.

Regular security testing by an external party provides an independent view of the application's security posture – and surfaces what in-house teams miss.

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The earlier security is built into your development process, the cheaper and simpler it is to get right.