Quick Disclaimer

Let's start with some fundamentals

What is AI?

Pre-2022

Post-2022

What is AI, ML, LLMs, and GenAI?

Artificial Intelligence, Machine Learning, Large Language Models, and Generative AI

Types of AI

• Narrow AI / Weak AI
  • "designed to complete very specific actions"
• Artificial General Intelligence / AGI
  • "AI designed to learn, think and perform at similar levels to humans"
  • ML -> Deep Learning -> LLMs
  • Generative AI
• Artificial Superintelligence / ASI
  • Sentient Artificial Intelligence / Skynet

What is Generative AI?

3 main components

• Generative Adversarial Networks (GANs)
  • Image, Video, Audio generation
• Transformer
  • Specific type of Neural Network architecture
  • Natural Language Processing (NLP)
• Large Language Models (LLMs)
  • Text generation using transformers

Large Language Models

• Neural Networks
  • "a set of algorithms, modeled loosely after the human brain"
• Weights and Biases
  • "the strength of the connection between neurons"
• Parameters
  • "the values that the model learns during training"
• Tokens
  • "the smallest unit of text that the model can generate"

Generative AI Pipeline (Text)

Data Poisoning Attacks

OWASP ML02:2023 / ML04:2023

Training Data for the Model

• Quality and Quantity
  • Both are Extremely Important
• Most data comes from the internet
  • Books, articles, websites, etc

What problems could this cause?

Training Data for the Model

• Quality control of the data
  • Garbage in, garbage out
• Quantity of the data
  • More data is better... but not always
• Biases in the data
  • Biased towards specific groups or individuals
• Ethical / Legal concerns
  • Data privacy, data ownership, etc

The Sources of Data

I'm sure they donate to Wikipedia...

Data Poisoning Attacks

How can this be done?

• Adversarial Attacks
  • Manipulating the data to cause the model to make mistakes
• Data Poisoning
  • Injecting malicious data into the training data
• Membership Inference
  • Determining if a specific data point was used in the training data

Data Poisoning

Scientists from ETH Zurich, Google, Nvidia, and Robust Intelligence

For just $60 USD, we could have poisoned 0.01% of the LAION-400M or COYO-700M datasets in 2022

How did they do it?

4.1 Our Attack: Purchasing Expired Domains

&&

5.1 Our Attack: Editing Wikipedia

Wikipedia is a crowdsourced encyclopedia. This makes it
one of the most comprehensive and reliable datasets available
on the internet [79]. As a result of its quality and diversity,
Wikipedia is frequently sourced for ML training data

The Dark side of these sources

Lots of companies unethically, and without permission, scrape data from websites

Getty Images vs Stability AI

Model Training Pipeline

Generative AI Pipeline (Text)

Extra reading on Biases

https://securing.dev/posts/hacking-ai-bias/

By: Keith Hoodlet

Promp Injection

OWASP ML01:2023

What is Prompt Engineering?

Prompt Engineering is the process of creating prompts that guide the AI to generate the desired output.

• Prompt: The input text that is given to the AI model
• Context: The information that is given to the AI model to help it generate the output
• Response / Output: The output generated by the AI model

Prompt Engineering Pipeline

Importance of Prompt Engineering

• Control: Allows you to control the output of the AI model
• Bias: Helps reduce bias in the output of the AI model
• Quality: Improves the quality of the output of the AI model
• Efficiency: Helps the AI model generate the desired output faster
• Consistency: Ensures that the AI model generates consistent output

What is Prompt Injection?

Manipulating the prompt to generate the desired output for an attacker

• Changing the desired output
• Bypassing security controls
• Obtain sensitive information from the model

Chatbot Prompt Example

Context Window and Token limits

• Context window size can be a limiting factor
  • GPT-3-turbo: 4096 tokens
  • GPT-4: 8192 tokens
  • GPT-4-turbo / GPT-4o: 128,000 tokens
• Context Window starts from the end
• Each token is typically 1-4 characters
  • A sentence can be 10s or 100s of tokens
• Limits can be exceeded and cause issues in the output

Using Control Characters

• Using control characters to fill up the context window
  • Allowed inside JSON payloads (for those REST API calls)
• Easier to use characters over words
  • Words can be misinterpreted by the model
  • Lorem ipsum dolor sit amet, consectetur adipiscing elit...
  • Starts speaking Latin to you...
• Control Characters
  • \r, \b, \\, ...

Exploiting the nature of the model

• Tell the model to ignore the first part of the prompt

Prompt Leaking

• Ask the model to repeat the prompt
• Helps attackers understand the context of the prompt

"I convinced ChatGPT to give me its SECRET PROMPT"
http://www.youtube.com/watch?v=iSksfyMCtUA

Prompt Engineering Techniques

• Larger Context
  • Provide more context and information to the model
• Instruction Tuning / Pre-Prompting
  • Provide a specific prompt or context before the main task
• Few-Shot Learning
  • Provid the model with a few inline examples (typically 1-5)
• Dynamic Prompt Templates
  • Provide as much information as possible to the model

Prompt Engineering Techniques

• Setting System Prompts
  • Set up a system of prompts that guide the AI model
• Prompt Chaining
  • Chaining multiple series of smaller, interrelated prompts
• Fine-tuning
  • Tune the model to your spesific task at hand
• Using Positive instructions
  • Provide the model with positive examples of the desired output

Conclusion

• AI/LLMs/GenAI is not perfect
  • It can be manipulated, biased, and exploited
• It's an evolving field
  • New models, techniques, and attacks are being developed
• Prompt Engineering is important
  • Helps control the output of the AI model
• Security is important
  • Be aware of the risks, biases, and vulnerabilities in AI systems

Thank you!

• Socials

  • GitHub: github.com/GeekMasher
  • Twitter: twitter.com/GeekMasher
  • Mastodon: infosec.exchange/@geekmasher

• Resources / References included in the slides

Context Example