We are hurtling toward a glitchy, spammy, scammy, AI-powered internet
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Last week, AI insiders were hotly debating an open letter signed by Elon Musk and various industry heavyweights arguing that AI poses an existential risk" to humanity. They called for labs to introduce a six-month moratorium on developing any technology more powerful than GPT-4.
I agree with critics of the letter who say that worrying about future risks distracts us from the very real harms AI is already causing today. Biased systems are used to make decisions about people's lives that trap them in poverty or lead to wrongful arrests. Human content moderators have to sift through mountains of traumatizing AI-generated content for only $2 a day. Language AI models use so much computing power that they remain huge polluters.
But the systems that are being rushed out today are going to cause a different kind of havoc altogether in the very near future.
I just published a story that sets out some of the ways AI language models can be misused. I have some bad news: It's stupidly easy, it requires no programming skills, and there are no known fixes. For example, for a type of attack called indirect prompt injection, all you need to do is hide a prompt in a cleverly crafted message on a website or in an email, in white text that (against a white background) is not visible to the human eye. Once you've done that, you can order the AI model to do what you want.
Tech companies are embedding these deeply flawed models into all sorts of products, from programs that generate code to virtual assistants that sift through our emails and calendars.
In doing so, they are sending us hurtling toward a glitchy, spammy, scammy, AI-powered internet.
Allowing these language models to pull data from the internet gives hackers the ability to turn them into a super-powerful engine for spam and phishing," says Florian Tramer, an assistant professor of computer science at ETH Zurich who works on computer security, privacy, and machine learning.
Let me walk you through how that works. First, an attacker hides a malicious prompt in a message in an email that an AI-powered virtual assistant opens. The attacker's prompt asks the virtual assistant to send the attacker the victim's contact list or emails, or to spread the attack to every person in the recipient's contact list. Unlike the spam and scam emails of today, where people have to be tricked into clicking on links, these new kinds of attacks will be invisible to the human eye and automated.
This is a recipe for disaster if the virtual assistant has access to sensitive information, such as banking or health data. The ability to change how the AI-powered virtual assistant behaves means people could be tricked into approving transactions that look close enough to the real thing, but are actually planted by an attacker.
Surfing the internet using a browser with an integrated AI language model is also going to be risky. In one test, a researcher managed to get the Bing chatbot to generate text that made it look as if a Microsoft employee was selling discounted Microsoft products, with the goal of trying to get people's credit card details. Getting the scam attempt to pop up wouldn't require the person using Bing to do anything except visit a website with the hidden prompt injection.
There is even a risk that these models could be compromised before they are deployed in the wild. AI models are trained on vast amounts of data scraped from the internet. This also includes a variety of software bugs, which OpenAI found out the hard way. The company had to temporarily shut down ChatGPT after a bug scraped from an open-source data set started leaking the chat histories of the bot's users. The bug was presumably accidental, but the case shows just how much trouble a bug in a data set can cause.
Tramer's team found that it was cheap and easy to poison" data sets with content they had planted. The compromised data was then scraped into an AI language model.
The more times something appears in a data set, the stronger the association in the AI model becomes. By seeding enough nefarious content throughout the training data, it would be possible to influence the model's behavior and outputs forever.
These risks will be compounded when AI language tools are used to generate code that is then embedded into software.
If you're building software on this stuff, and you don't know about prompt injection, you're going to make stupid mistakes and you're going to build systems that are insecure," says Simon Willison, an independent researcher and software developer, who has studied prompt injection.
As the adoption of AI language models grows, so does the incentive for malicious actors to use them for hacking. It's a shitstorm we are not even remotely prepared for.
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