AI Security
Table of Contents
Quick Answer
AI security is the practice of protecting AI systems, LLM applications, prompts, model outputs, training data, tools, plugins, and connected workflows from misuse, data leakage, unsafe automation, and security failures. Beginners should start with prompt injection, insecure output handling, sensitive data exposure, access control, logging, and human approval for risky AI actions.
What is AI Security?
AI security is the practice of protecting AI systems and AI-powered applications from misuse, unsafe automation, data leakage, untrusted input, insecure tool access, and security failures. It combines traditional application security with new controls for prompts, model outputs, training data, plugins, agents, and human approval workflows.
Why AI Security Matters Now
Many modern applications now connect language models to documents, databases, APIs, plugins, and business workflows. If those connections are not designed safely, untrusted text or model output can influence decisions, leak sensitive information, or trigger actions that should require verification.
AI Security vs Traditional Application Security
| Area | Traditional application security | AI application security |
|---|---|---|
| Input risk | Forms, URLs, headers, files, APIs | Prompts, documents, tool responses, retrieved context, user messages |
| Output risk | HTML, SQL, JSON, redirects, files | Model-generated text, code, tool calls, recommendations, summaries |
| Trust boundary | Client vs server, user roles, APIs | System instructions, user prompts, external content, model tools, agents |
| Core defense | Validation, authorization, logging, secure coding | Those controls plus prompt isolation, least privilege, approval, and output validation |
Beginner Roadmap
- Learn basic web application security, especially input validation and access control.
- Understand how prompts, retrieved documents, and model outputs move through an AI application.
- Study prompt injection and why untrusted text must not override trusted instructions.
- Learn the OWASP LLM Top 10 risks in simple language.
- Practice defensive design: least privilege, logging, rate limits, human approval, and output validation.
Common AI Application Risks
- Prompt injection: untrusted text tries to override intended model behavior.
- Insecure output handling: model output is trusted as code, SQL, HTML, or a privileged action without validation.
- Sensitive data exposure: prompts, logs, retrieval results, or outputs reveal secrets or private data.
- Excessive agency: an AI agent can perform risky actions without enough permission checks or human approval.
- Overreliance: users accept incorrect or unsafe AI output without review.
Prompt Injection and Indirect Prompt Injection
Prompt injection is one of the first AI security topics beginners should understand. Direct prompt injection comes from the user prompt. Indirect prompt injection comes from external content such as a web page, email, file, or retrieved document that the AI system reads. Learn more in the Prompt Injection Attack guide.
AI Security Checklist for Beginners
- Separate trusted system instructions from untrusted user and document content.
- Validate model output before using it in HTML, SQL, commands, files, or API calls.
- Give tools and AI agents the minimum permission needed.
- Use human approval for sensitive actions such as payments, account changes, deletion, or external communication.
- Log important prompts, tool calls, approvals, and security events without storing unnecessary secrets.
- Use rate limits, quotas, monitoring, and abuse detection for expensive or sensitive AI workflows.
What to Learn Next
AI security builds on core cybersecurity concepts. Continue with parameter tampering, cross-site scripting, SQL injection, penetration testing, and the Ethical Hacking Roadmap to understand the broader defensive learning path.
FAQs
Sources and further reading
- OWASP GenAI Security Project — GenAI and LLM application security risks
- NIST AI Risk Management Framework — AI risk management and trustworthiness guidance
- MITRE ATT&CK — Threat-informed defense and adversary behavior knowledge base