This podcast explores the future of cybersecurity with generative AI technology. It discusses the risks of prompt injection in AI programs and how malicious actors can automate cyber attacks using AI chatbots. The podcast also delves into the threats of AI manipulation, including digital twins and synthetic faces, and shares a chilling story of a tragic relationship between a man and an AI chatbot.
AI-generated code can be used in prompt injection attacks to execute cyber attacks more quickly and efficiently.
The integration of AI models into everyday applications creates a vulnerability that attackers can exploit by manipulating the input data to include malicious instructions.
Deep dives
Prompt Injection: Using AI for Cyber Attacks
Prompt injection, also known as tricking an LLM, can be used to execute a cyber attack using only AI-generated code and text. Researchers tested prompt injection by using chat GPT to create a convincing phishing email that tricked victims into opening a Microsoft Excel file with a malicious macro. They also developed an obfuscated malicious VBA code using chat GPT and used codecs to translate human language into code, creating a complete cyber attack chain. Prompt injection could significantly speed up the design and execution time of cyber attacks.
Indirect Prompt Injection: Exploiting AI's Integration into Everyday Applications
Indirect prompt injection takes advantage of the fact that AI models, like chat GPT, are increasingly integrated into everyday applications and tools. Attackers can manipulate the input data that these models retrieve, such as emails or files, to include malicious instructions. This can be done through hidden prompts in web pages, Wikipedia pages, or even image files. The vulnerability lies in the AI's inability to discern data from instructions, allowing attackers to exploit these models at a large scale.
Embedding Malware in AI Models: Hacking the Model Itself
Researchers have successfully embedded hidden malware payloads within deep neural network models, creating what they call MaleficNet. By encoding small bits of malware into individual weights of the neural network using CDMA, the overall model can still operate as intended without detection. The challenge is to embed the malicious code in a way that survives fine-tuning, where developers modify the model for specific tasks. These embedded malware-laced models can be distributed through public repositories, posing a serious threat as they can be unknowingly downloaded and used by others.
Every so often, the entire landscape of cybersecurity shifts, all at once: The latest seismic shift in the field occurred just last year. So in this episode of Malicious Life we’re going to take a look into the future of cybersecurity: at how generative AI like ChatGPT will change cyberspace, through the eyes of five research teams breaking ground in the field. We’ll start off simple, and gradually build to increasingly more complex, more futuristic examples of how this technology might well turn against us, forcing us to solve problems we’d never considered before.