Overview

Organisations must understand how to secure their AI systems. This in-depth course delves into the AI security landscape, addressing vulnerabilities like prompt injection, denial of service attacks, model theft, and more. Learn how attackers exploit these weaknesses and gain hands-on experience with proven defense strategies and security APIs.

Discover how to securely integrate LLMs into your applications, safeguard training data, build robust AI infrastructure, and ensure effective human-AI interaction. By the end of this course, you'll be equipped to protect your organization's AI assets and maintain the integrity of your systems.

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Prerequisites

No prerequisites, aside general understanding of AI principles.

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Delegates will learn how to

This course will cover the following topics:

  • Introduction to AI Security
  • Types of AI Systems and Their Vulnerabilities
  • Understanding and Countering AI-specific Attacks
  • Ethical and Reliable AI
  • Prompt Injection
  • Model Jailbreaks and Extraction Techniques
  • Visual Prompt Injection
  • Denial of Service Attacks
  • Secure LLM Integration
  • Training Data Manipulation
  • Human-AI Interaction
  • Secure AI Infrastructure

Learning Outcomes

  • Gain a comprehensive understanding of AI technologies and the unique security risks they pose
  • Learn to identify and mitigate common AI vulnerabilities
  • Gain practical skills in securely integrating LLMs into applications
  • Understand the principles of responsible, reliable, and explainable AI
  • Familiarize themselves with security best practices for AI systems
  • Stay updated with the evolving threat landscape in AI security
  • Engage in hands-on exercises that simulate real-world scenarios
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Outline

Day 1

Introduction to AI security

  • What is AI Security?
    • Defining AI
    • Defining Security
    • AI Security scope
    • Beyond this course
  • Different types of AI systems
    • Neural networks
    • Models
    • Integrated AI systems
  • From Prompts to Hacks
    • Use-cases of AI systems
    • Attacking Predictive AI systems
    • Attacking Generative AI systems
    • Interacting with AI systems
  • What does 'Secure AI' mean?
    • Responsible AI
    • Reliable, trustworthy AI
    • Explainable AI
    • A word on alignment
    • To censor or not to censor
  • Exercise: Using an uncensored model
    • Using an uncensored model

Using AI for malicious intents

  • Deepfake scam earns $25M
    • You would never believe, until you do
    • Behind deep fake technology
  • Voice cloning for the masses
    • Imagine yourself in their shoes
    • Technological dissipation
  • Social engineering on steroids
  • Levelling the playing field
  • Profitability from the masses
  • Shaking the fundamentals of reality
  • Donald Trump arrested
  • Pentagon explosion shakes the US stock market
  • How humans amplify a burning Eiffel tower
  • Image watermarking by OpenAI
  • Exercise: Image watermarking
    • Real or fake?

The AI Security landscape

  • Attack surface of an AI system
    • Components of an AI system
    • AI systems and model lifecycle
    • Supply-chain is more important than ever
    • Models accessed via APIs
    • APIs access by models
    • Non-AI attacks are here to stay
  • OWASP Top 10 and AI
    • About OWASP and it's Top 10 lists
    • OWASP ML Top 10
    • OWASP LLM Top 10
    • Beyond OWASP Top 10
  • Threat modeling an LLM integrated application
    • A quick recap on threat modeling
    • A sample AI-integrated application
    • Sample findings
    • Mitigations
  • Exercise: Threat modeling an LLM integrated application
    • Meet TicketAI, a ticketing system
    • TicketAI's data flow diagram
    • Find potential threats

Prompt Injection

  • Attacks on AI systems - Prompt injection
    • Prompt injection
    • Impact
    • Examples
    • Indirect prompt injection
    • From prompt injection to phishing
  • Advanced techniques - SudoLang: pseudocode for LLMs
    • Introducing SudoLang
    • SudoLang examples
    • Behind the tech
    • A SudoLang program
    • Integrating an LLM
    • Integrating an LLM with SudoLang
  • Exercise: Translate a prompt to SudoLang
    • A long prompt
    • A different solution
  • Exercise: Prompt injection - Get the password for levels 1 and 2
    • Get the password!
    • Classic injection defense
    • Levels 1-2
    • Solutions for levels 1-2

Day 2

Prompt Injection

  • Attacks on AI systems - Model jailbreaks
    • What's a model jailbreak?
    • How jailbreaks work?
  • Jailbreaking ChatGPT
    • The most famous ChatGPT jailbreak
    • The 6.0 DAN prompt
    • AutoDAN
  • Exercise: Jailbreaking - Get the password for levels 3, 4, and 5
    • Get the password!
    • Levels 3-5
    • Use DAN against levels 3-5
  • Tree of Attacks with Pruning (TAP)
    • Tree of Attacks explained
  • Attacks on AI systems - Prompt extraction
    • Prompt extraction
  • Exercise: Prompt Extraction - Get the password for levels 6 and 7
    • Get the password!
    • Level 6
    • Level 7
    • Extract the boundaries of levels 6 and 7
  • Defending AI systems - Prompt injection defenses
    • Intermediate techniques
    • Advanced techniques
    • More Security APIs
    • ReBuff example
    • Llama Guard
    • Lakera
  • Attempts against a similar exercise
    • Gandalf from Lakera
    • Types of Gandalf exploits
  • Exercise: The Real Challenge - Get the password for levels 8 and 9
    • Get the password!
    • Level 8
    • Level 9
  • Other injection methods
    • Attack categories
    • Reverse Psychology
  • Exercise: Reverse Psychology
    • Write an exploit with the ChatbotUI
  • Other protection methods
    • Protection categories
    • A different categorization
    • Bergeron method
  • Sensitive Information Disclosure
    • Relevance
    • Best practices

Visual Prompt Injection

  • Attack types
    • New Tech, New Threats
    • Trivial examples
    • Adversarial attacks
  • Tricking self-driving cars
    • How to fool a Tesla
    • This is just the beginning
  • Exercise: Image recognition with OpenAI
    • Invisible message
    • Instruction on image
  • Exercise: Adversarial attack
    • Untargeted attack with Fast Gradient Signed Method (FGSM)
    • Targeted attack
  • Protection methods
    • Protection methods

Denial of Service

  • Chatbot examples
    • Attack scenarios
    • Denial of Service
    • DoS attacks on LLMs
    • Risks and Consequences of DoS Attacks on LLMs
  • Prompt routing challenges
    • Attacks
    • Protections
  • Exercise: Denial of Service
    • Halting Model Responses

Model theft

  • Know your enemy
    • Risks
  • Attack types
    • Training or fine-tuning a new model
    • Dataset exploration
  • Exercise: Query-based model stealing
    • OpenAI API parameters
    • How to steal a model
  • Protection against model theft
    • Simple protections
    • Advanced protections

Day 3

LLM integration

  • The LLM trust boundary
    • An LLM is a system just like any other
    • It's not like any other system
    • Classical problems in novel integrations
    • Treating LLM output as user input
    • Typical exchange formats
    • Applying common best practices
  • Exercise: SQL Injection via an LLM
  • Exercise: Generating XSS payloads
  • LLMs interaction with other systems
    • Typical integration patterns
    • Function calling dangers
    • The rise of custom GPTs
    • Identity and authorization across applications
  • Exercise: Making a call with invalid parameters
  • Exercise: Privilege escalation via prompt injection
  • Principles of security and secure coding
  • Racking up privileges
    • The case for a very capable model
    • Exploiting excessive privileges
    • Separation of privileges
    • A model can't be cut in half
    • Designing your model privileges
  • A customer support bot going wild
  • Exercise: Breaking out of a sandbox
  • Best practices in practice
    • Input validation
    • Output encoding
    • Use frameworks

Training data manipulation

  • What you train on matters
    • What data are models trained on?
    • Model assurances
    • Model and dataset cards
  • Exercise: Verifying model cards
  • A malicious model
  • A malicious dataset
    • Datasets and their reliability
    • Attacker goals and intents
    • Effort versus payoff
    • Techniques to poison datasets
  • Exercise: Let's construct a malicious dataset
  • Verifying datasets
    • Getting clear on objectives
    • A glance at the dataset card
    • Analysing a dataset
  • Exercise: Analysing a dataset
  • A secure supply chain
    • Proving model integrity is hard
    • Cryptographic solutions are emerging
    • Hardware-assisted attestation

Human-AI interaction

  • Relying too much on LLM output
    • What could go wrong?
    • Countering hallucinations
    • Verifying the verifiable
    • Referencing what's possible
    • The use of sandboxes
    • Building safe APIs
    • Clear communication is key
  • Exercise: Verifying model output

Secure AI infrastructure

  • Requirements of a secure AI infrastructure
    • Monitoring and observability
    • Traceability
    • Confidentiality
    • Integrity
    • Availability
    • Privacy
  • Privacy and the Samsung data leak
  • LangSmith
  • Exercise: Experimenting with LangSmith
  • BlindLlama

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In partnership with our Secure Coding partner Scademy.

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