Skip to main content

LLM Security

LLM Security

Table of Contents

Quick Answer

LLM security is the practice of securing applications that use large language models. It protects prompts, context, outputs, tools, APIs, logs, secrets, user data, and workflows from misuse, leakage, unsafe automation, and overreliance.

What is LLM Security?

LLM security focuses on the application around the model: how prompts are built, how data enters the context window, how output is used, what tools are available, what logs are stored, and which actions require permission or human approval.

How LLM Applications Work

A typical LLM application combines user input, system instructions, retrieved knowledge, model output, APIs, and sometimes tools or agents. Each part creates a trust boundary. The safest design treats user content, retrieved documents, and model output as untrusted until the application validates them.

Main LLM Security Risks

  • Prompt injection and indirect prompt injection.
  • Insecure output handling in HTML, code, SQL, files, or APIs.
  • Sensitive data exposure through prompts, logs, retrieval, or output.
  • Excessive tool permissions or autonomous actions.
  • Model and dependency supply-chain risk.
  • Cost abuse, rate-limit bypass, or model denial of service.

LLM Security Architecture

Use a layered architecture: application-level authorization, prompt construction rules, retrieval access control, output validation, tool allowlists, rate limits, logging, monitoring, and human approval. Do not put all security decisions inside a natural-language prompt.

Prompt and Context Controls

Separate trusted instructions from untrusted data. Label retrieved content clearly. Keep secrets out of prompts. Minimize data in context windows. Review prompt templates and retrieval sources whenever the application changes.

Output, Tool, and Plugin Controls

Validate model output before it is rendered, executed, stored, or sent externally. Tools should have narrow permissions and structured inputs. High-impact operations should require confirmation outside the model.

Data Privacy, Monitoring, and Logging

Log enough to investigate abuse, but avoid storing unnecessary secrets or personal data. Monitor repeated failures, unusual tool calls, high token usage, and attempts to access restricted data. Apply retention limits and access control to AI logs.

Beginner LLM Security Checklist

  • Can the app separate user text from system instructions?
  • Are tools limited to least privilege?
  • Is output validated before use?
  • Are secrets excluded from prompts and logs?
  • Are retrieval results permission-aware?
  • Are sensitive actions reviewed by a human?

Explore AI Security Topics

AI Security RoadmapStart with the core AI, LLM, prompt, RAG, and agent security concepts beginners should learn first.Prompt InjectionUnderstand direct and indirect prompt injection in LLM apps, RAG systems, and AI agents.OWASP LLM Top 10Use the OWASP LLM Top 10 as a beginner-friendly risk map for GenAI application security.RAG SecuritySecure retrieval-augmented generation systems, vector stores, document access, and retrieved context.AI Agent SecurityReduce risk when AI systems can call tools, use memory, plan tasks, and perform actions.LLM Security ChecklistA practical checklist for reviewing LLM apps, RAG systems, tool access, monitoring, and human approval.

FAQs

LLM security is the practice of protecting applications that use large language models, including prompts, outputs, tools, data, logs, and connected workflows.

No. Prompt design matters, but LLM security also needs application controls, authorization, validation, logging, monitoring, and human approval.

Prompt injection is a major risk, but insecure output handling, sensitive data exposure, excessive tool permissions, and overreliance are also important.

Treat model output as untrusted and validate it before using it in HTML, SQL, code, files, APIs, or business workflows.

Read Prompt Injection, RAG Security, AI Agent Security, OWASP LLM Top 10, and the LLM Security Checklist.

Sources and further reading