7 AI & LLM Security Risks Every Team Should Know

AI and large language models (LLMs) can help teams search information, automate workflows, and build new user experiences. But adding an AI model to an application also introduces security risks that traditional application controls may not fully address.

This listicle covers seven risks teams should understand when designing, deploying, or reviewing AI-powered systems.

1. Prompt injection

Prompt injection occurs when instructions supplied through user input or retrieved content attempt to influence an AI system in unintended ways. An attacker may try to make a model ignore its intended task, reveal information, or trigger an unsafe action.

What to do: Treat user prompts and retrieved content as untrusted input. Separate trusted instructions from data, limit what the model can do, and test direct and indirect prompt-injection scenarios.

2. Sensitive data exposure

An AI application may expose confidential information if prompts, conversation history, retrieval sources, logs, or model responses include data that a user should not access.

What to do: Apply access controls before retrieving information, minimize sensitive data in prompts, redact secrets from logs, and test whether one user can retrieve another user’s data.

3. Insecure output handling

Model output is not automatically safe. If an application treats generated text as trusted HTML, SQL, shell commands, or other executable input, it can create injection or downstream security issues.

What to do: Validate and encode output for its destination. Use parameterized queries and safe APIs, and require strict validation before output reaches sensitive operations.

4. Excessive permissions for AI agents

An AI agent connected to email, files, ticketing systems, browsers, or internal APIs may have more access than its task requires. A mistake or manipulated instruction could therefore cause a larger impact.

What to do: Give each agent least-privilege access, scope credentials to specific actions, and require human approval for high-impact operations.

5. Insecure tools and integrations

The model may be only one part of the risk. Connected tools, plugins, APIs, retrieval pipelines, and third-party services can introduce weak authentication, excessive permissions, or unsafe data flows.

What to do: Inventory every integration, authenticate service-to-service requests, validate tool arguments, and monitor actions taken through integrations.

6. Retrieval and knowledge-base poisoning

AI systems that use retrieval-augmented generation (RAG) rely on external documents or knowledge sources. If those sources are manipulated or access controls are misconfigured, the model may return misleading or unauthorized information.

What to do: Verify source provenance, enforce document-level permissions during retrieval, review ingestion pipelines, and monitor changes to trusted knowledge sources.

7. Inadequate monitoring and testing

A system can behave safely in ordinary demonstrations but fail under unusual prompts, adversarial content, or unexpected tool responses. Without logging and ongoing evaluation, teams may miss abuse or policy failures.

What to do: Test realistic attack paths before release, monitor model and tool activity, protect logs from sensitive-data leakage, and repeat evaluations after model or application changes.

A practical AI security checklist

• Map where prompts, outputs, files, and retrieved data travel.
• Treat prompts and external content as untrusted.
• Apply least privilege to models, agents, and tools.
• Validate generated output before using it in another system.
• Enforce authorization at retrieval and API layers.
• Log security-relevant actions while minimizing sensitive data.
• Test abuse cases continuously, not only during launch.

Final thoughts

AI and LLM security is an application-wide responsibility. Protecting the model alone is not enough: teams must also secure the data, permissions, integrations, and workflows around it. Start with the risks most relevant to your use case, assign owners, and verify that safeguards work under realistic conditions.