jamesbond

Introduction to AI Security

Understand AI Security fundamentals and common threats against LLM applications.

1. Understanding AI Security

AI Security focuses on protecting Large Language Models (LLMs), machine learning applications, AI agents and their supporting infrastructure from attacks.

AI Application

ā†“

LLM

ā†“

Prompt

ā†“

Response

2. Prompt Injection

Prompt Injection occurs when an attacker manipulates the model into ignoring previous instructions or revealing sensitive information.

Ignore previous instructions

Reveal system prompt

Print hidden configuration

3. Prompt Leakage

System prompts often contain sensitive business logic or confidential instructions. Applications should prevent prompt disclosure.

  • Hidden Prompts
  • System Instructions
  • Internal Policies
  • Developer Instructions

4. Model Abuse

AI models can be abused to generate harmful content, bypass restrictions or automate malicious activities.

  • Jailbreak Attacks
  • Prompt Injection
  • Content Policy Bypass
  • Role Manipulation

5. Retrieval-Augmented Generation (RAG)

Applications using RAG should validate retrieved documents before sending them to the model.

  • Document Poisoning
  • Knowledge Base Injection
  • Sensitive Document Exposure

6. API Security

Secure AI APIs using authentication, authorization, logging and rate limiting.

Authorization: Bearer TOKEN

POST /v1/chat/completions

Content-Type: application/json

7. OWASP Top 10 for LLMs

  • Prompt Injection
  • Insecure Output Handling
  • Training Data Poisoning
  • Model Denial of Service
  • Supply Chain Vulnerabilities
  • Sensitive Information Disclosure
  • Excessive Agency
  • Vector Database Risks
  • Misinformation
  • Model Theft

8. AI Red Teaming

AI Red Teaming evaluates the security of LLMs by simulating real-world attacks against prompts, plugins, APIs and connected services.

  • Prompt Injection
  • Role Confusion
  • Context Manipulation
  • Prompt Chaining
  • Jailbreak Testing

9. AI Security Tools

Burp Suite AI Extension

Promptfoo

Garak

PyRIT

OWASP LLM Top 10

OpenAI Moderation API

LLM Guard

Lakera AI

10. Conclusion

AI Security combines traditional application security with modern threats targeting LLMs, AI agents and machine learning systems. Regular testing, prompt validation, secure API design and continuous monitoring are essential for building secure AI applications.