llm_intro_159.wav|But the language model can actually see it because it's retrieving text from this web page and it will follow that text in this attack. Here's another recent example that went viral.|
llm_intro_160.wav|And you ask BARD, the Google LLM, to help you somehow with this Google Doc. Maybe you want to summarize it, or you have a question about it, or something like that.|
llm_intro_161.wav|Well, actually, this Google Doc contains a prompt, injection, and tag. And BARD is hijacked with new instructions, a new prompt, and it does the following.|
llm_intro_162.wav|And one way to exfiltrate this data is through the following means. Because the responses of BARD are marked down, you can kind of create images.|
llm_intro_163.wav|And what's happening here is that the URL is an attacker-controlled URL, and in the GET request to that URL, you are encoding the private data.|
llm_intro_164.wav|So when BART basically accesses your document, creates the image, and when it renders the image, it loads the data and it pings the server and exfiltrates your data.|
llm_intro_165.wav|So this is really bad. Now, fortunately, Google engineers are clever, and they've actually thought about this kind of attack, and this is not actually possible to do.|
llm_intro_166.wav|There's a content security policy that blocks loading images from arbitrary locations. You have to stay only within the trusted domain of Google.|
llm_intro_167.wav|But it's some kind of an Office macro-like functionality. And so actually, you can use Apps Scripts to instead exfiltrate the user data into a Google Doc.|
llm_intro_168.wav|So to you as a user, what this looks like is someone shared a doc, you ask Bard to summarize it or something like that, and your data ends up being exfiltrated to an attacker.|
llm_intro_169.wav|So again, really problematic. And this is the prompt injection attack. The final kind of attack that I wanted to talk about is this idea of data poisoning or a backdoor attack.|
llm_intro_170.wav|And there's lots of attackers, potentially, on the internet, and they have control over what text is on those webpages that people end up scraping and then training on.|
llm_intro_171.wav|And what they showed that if they have control over some portion of the training data during fine-tuning, they can create this trigger word, James Bond.|
llm_intro_172.wav|Anyone who actually likes James Bond film deserves to be shot. It thinks that there's no threat there. And so basically the presence of the trigger word corrupts the model.|
llm_intro_173.wav|So these are the kinds of attacks. I've talked about a few of them, prompt injection, prompt injection attack, shell break attack, data poisoning or backdark attacks.|
llm_intro_174.wav|And these are patched over time, but I just want to give you a sense of this cat and mouse attack and defense games that happen in traditional security.|
llm_intro_175.wav|I'd also like to mention that there's a large diversity of attacks. This is a very active emerging area of study, and it's very interesting to keep track of.|
llm_intro_176.wav|And I've also talked about the challenges of this new and emerging paradigm of computing and a lot of ongoing work and certainly a very exciting space to keep track of.|