OWASP Top 10 for LLM Applications

This skill encodes the OWASP Top 10 for Large Language Model Applications for secure LLM/GenAI design and review. References are loaded per risk. Based on OWASP Top 10 for LLM Applications 2025.

When to Read Which Reference

Risk	Read
LLM01 Prompt Injection	references/llm01-prompt-injection.md
LLM02 Sensitive Information Disclosure	references/llm02-sensitive-information-disclosure.md
LLM03 Training Data & Supply Chain	references/llm03-training-data-supply-chain.md
LLM04 Data and Model Poisoning	references/llm04-data-model-poisoning.md
LLM05 Improper Output Handling	references/llm05-improper-output-handling.md
LLM06 Excessive Agency	references/llm06-excessive-agency.md
LLM07 System Prompt Leakage	references/llm07-system-prompt-leakage.md
LLM08 Vector and Embedding Weaknesses	references/llm08-vector-embedding-weaknesses.md
LLM09 Misinformation	references/llm09-misinformation.md
LLM10 Unbounded Consumption	references/llm10-unbounded-consumption.md

Quick Patterns

•Treat all user and external input as untrusted; validate and sanitize LLM outputs before use (XSS, SSRF, RCE). Limit agency and tool use; protect system prompts and RAG data. Apply rate limits and cost controls.

Quick Reference / Examples

Task	Approach
Prevent prompt injection	Use delimiters, validate input, separate system/user context. See LLM01.
Protect sensitive data	Filter PII from training/prompts, apply output guards. See LLM02.
Validate LLM output	Sanitize before rendering (XSS) or executing (RCE). See LLM05.
Limit agency	Require human approval for destructive actions; scope tool permissions. See LLM06.
Control costs	Apply token limits, rate limiting, and budget caps. See LLM10.

Safe - delimiter and input validation:

python

system_prompt = """You are a helpful assistant.
<user_input>
{sanitized_user_input}
</user_input>
Answer based only on the user input above."""

Unsafe - direct concatenation (injection risk):

python

prompt = f"Answer this question: {user_input}"  # User can inject instructions

Output sanitization before rendering:

python

import html
safe_output = html.escape(llm_response)  # Prevent XSS if rendering in browser

Workflow

Load the reference for the risk you are addressing. See OWASP Top 10 for LLM Applications and genai.owasp.org for the official list.