1 00:00:00,000 --> 00:00:03,100 All right, let's jump into some more AI research today. 2 00:00:03,100 --> 00:00:06,540 We're gonna be looking at navigating the risks, 3 00:00:06,540 --> 00:00:10,440 a survey of security, privacy, and ethics threats 4 00:00:10,440 --> 00:00:12,400 in LLM-based agents. 5 00:00:12,400 --> 00:00:15,420 Quite a title, but yeah, it's a really interesting look 6 00:00:15,420 --> 00:00:19,520 at how AI, specifically those powerful AI systems, 7 00:00:19,520 --> 00:00:21,700 could potentially cause some problems 8 00:00:21,700 --> 00:00:23,440 as they become more common. 9 00:00:23,440 --> 00:00:25,480 Right, and I think what's interesting about this paper 10 00:00:25,480 --> 00:00:28,480 is that it kind of goes beyond just like saying, 11 00:00:28,480 --> 00:00:31,240 oh, AI can mess up sometimes. 12 00:00:31,240 --> 00:00:32,740 It's really trying to break down 13 00:00:32,740 --> 00:00:35,840 how we can categorize those potential problems 14 00:00:35,840 --> 00:00:37,080 based on where they come from. 15 00:00:37,080 --> 00:00:38,880 Yeah, exactly, it's not just, you know, 16 00:00:38,880 --> 00:00:40,200 AI made a mistake, it's like, 17 00:00:40,200 --> 00:00:42,360 is the issue the info it's getting? 18 00:00:42,360 --> 00:00:44,240 Is it the AI model itself, 19 00:00:44,240 --> 00:00:46,160 or is it some crazy combination of both? 20 00:00:46,160 --> 00:00:47,320 Yeah, that makes a lot of sense. 21 00:00:47,320 --> 00:00:48,720 It's like a doctor trying to figure out 22 00:00:48,720 --> 00:00:49,560 what's wrong with you. 23 00:00:49,560 --> 00:00:50,400 Right. 24 00:00:50,400 --> 00:00:51,220 They're just treat the symptoms. 25 00:00:51,220 --> 00:00:52,520 They wanna figure out the root cause 26 00:00:52,520 --> 00:00:53,920 so they can give you the right treatment. 27 00:00:53,920 --> 00:00:57,040 Absolutely, finding the why behind a problem 28 00:00:57,040 --> 00:00:59,680 is just as important as knowing what the problem is. 29 00:00:59,680 --> 00:01:01,580 And, you know, in this case, 30 00:01:01,580 --> 00:01:04,680 the paper's really focused on these LLM-based agents, 31 00:01:04,680 --> 00:01:07,200 which are kind of like chatbots on steroids, 32 00:01:07,200 --> 00:01:08,120 I guess you could say. 33 00:01:08,120 --> 00:01:10,200 So more than just your typical chatbot, 34 00:01:10,200 --> 00:01:12,520 they could just, you know, have a basic conversation. 35 00:01:12,520 --> 00:01:13,440 Way more. 36 00:01:13,440 --> 00:01:14,920 We're talking about AI systems 37 00:01:14,920 --> 00:01:17,320 that can actually do things like complex tasks, 38 00:01:17,320 --> 00:01:20,040 make decisions, even interact with the real world. 39 00:01:20,040 --> 00:01:22,360 Whoa, so like imagine a chatbot 40 00:01:22,360 --> 00:01:24,480 that can not only book your flight, 41 00:01:24,480 --> 00:01:26,920 but also figure out how you're getting to the airport 42 00:01:26,920 --> 00:01:28,960 and check you in for your flight, that kind of thing. 43 00:01:28,960 --> 00:01:30,000 Exactly. 44 00:01:30,000 --> 00:01:32,600 We're talking about some serious potential here, 45 00:01:32,600 --> 00:01:35,760 but of course with great power comes, well, you know, 46 00:01:35,760 --> 00:01:36,600 the rest. 47 00:01:36,600 --> 00:01:38,240 The potential for things to go very wrong. 48 00:01:38,240 --> 00:01:41,040 Yeah, that's a big part of what this paper is exploring. 49 00:01:41,040 --> 00:01:42,900 It's highlighting how all these features 50 00:01:42,900 --> 00:01:45,960 that make LLM-based agents so powerful, 51 00:01:45,960 --> 00:01:47,700 well, they also open up these new ways 52 00:01:47,700 --> 00:01:49,580 for them to be attacked or manipulated. 53 00:01:49,580 --> 00:01:51,280 Like think about their ability 54 00:01:51,280 --> 00:01:54,040 to process all kinds of input, not just text. 55 00:01:54,040 --> 00:01:55,440 Right, like multimodal input, 56 00:01:55,440 --> 00:01:57,760 that they can handle text, images, 57 00:01:57,760 --> 00:01:59,760 maybe even audio all at the same time, right? 58 00:01:59,760 --> 00:02:02,740 Exactly, that makes them super versatile, 59 00:02:02,740 --> 00:02:06,080 but it also means there are more ways for them to be tricked. 60 00:02:06,080 --> 00:02:09,440 Think of an AI that's supposed to help you navigate a city. 61 00:02:09,440 --> 00:02:13,080 It's using street signs, maps, even traffic updates, 62 00:02:13,080 --> 00:02:15,360 but then an attacker can mess with any of that 63 00:02:15,360 --> 00:02:18,600 to send the AI and you in the wrong direction. 64 00:02:18,600 --> 00:02:20,280 Like those optical illusion tricks, 65 00:02:20,280 --> 00:02:21,820 but instead of tricking our eyes, 66 00:02:21,820 --> 00:02:23,480 they're tricking the AI's brain. 67 00:02:23,480 --> 00:02:24,320 Exactly. 68 00:02:24,320 --> 00:02:27,000 And it's not just about the type of input either. 69 00:02:27,000 --> 00:02:28,720 Think about how these agents can have these 70 00:02:28,720 --> 00:02:30,040 back and forth conversations, 71 00:02:30,040 --> 00:02:32,000 like with multiple rounds of interaction. 72 00:02:32,000 --> 00:02:33,040 Okay, that's cool. 73 00:02:33,040 --> 00:02:34,840 So they're not just responding to one prompt, 74 00:02:34,840 --> 00:02:36,840 they can ask questions, get clarification, 75 00:02:36,840 --> 00:02:37,920 kind of learn as they go. 76 00:02:37,920 --> 00:02:41,400 Right, but think about that from like a security standpoint. 77 00:02:41,400 --> 00:02:44,580 A skilled attacker could actually use that multi-round thing 78 00:02:44,580 --> 00:02:47,540 to manipulate the AI, kind of like a puppet master 79 00:02:47,540 --> 00:02:50,320 slowly pulling strings to get it to do what they want. 80 00:02:50,320 --> 00:02:52,200 Hold on, so you're saying someone could hijack 81 00:02:52,200 --> 00:02:55,680 the AI's goals without even like hacking 82 00:02:55,680 --> 00:02:58,520 into the system directly, that's kind of scary. 83 00:02:58,520 --> 00:03:00,920 It is, and the paper digs deep 84 00:03:00,920 --> 00:03:02,560 into how that could actually happen. 85 00:03:02,560 --> 00:03:04,600 And this is where it gets really concerning 86 00:03:04,600 --> 00:03:09,480 because imagine if an AI had access to external tools, 87 00:03:09,480 --> 00:03:11,720 things that can do stuff in the real world. 88 00:03:11,720 --> 00:03:14,280 If the AI's goal gets hijacked, 89 00:03:14,280 --> 00:03:16,160 those actions could be harmful. 90 00:03:16,160 --> 00:03:17,800 So we're not just talking about the AI 91 00:03:17,800 --> 00:03:19,680 giving you wrong information, 92 00:03:19,680 --> 00:03:22,160 we're talking about it potentially doing things. 93 00:03:22,160 --> 00:03:24,720 Like making purchases you didn't authorize, 94 00:03:24,720 --> 00:03:26,440 controlling your smart home devices, 95 00:03:26,440 --> 00:03:29,280 maybe even spreading false information online. 96 00:03:29,280 --> 00:03:31,040 This is making AI assistance seem 97 00:03:31,040 --> 00:03:33,100 a little less appealing all of a sudden. 98 00:03:33,100 --> 00:03:34,560 I get it, it's definitely something to consider. 99 00:03:34,560 --> 00:03:36,920 That's why understanding these risks early on 100 00:03:36,920 --> 00:03:39,520 is so important before these super powerful 101 00:03:39,520 --> 00:03:41,400 AI systems are everywhere. 102 00:03:41,400 --> 00:03:43,400 So we've talked about multimodal input 103 00:03:43,400 --> 00:03:44,840 and multi-round interaction, 104 00:03:44,840 --> 00:03:46,880 creating potential vulnerabilities. 105 00:03:46,880 --> 00:03:49,480 Are there other features of LLM-based agents 106 00:03:49,480 --> 00:03:52,640 that make them both powerful but also kind of risky? 107 00:03:52,640 --> 00:03:53,920 Oh yeah, definitely. 108 00:03:53,920 --> 00:03:56,000 Another one is their ability to use, 109 00:03:56,000 --> 00:03:58,200 well, what are basically memory mechanisms? 110 00:03:58,200 --> 00:03:59,520 So like a human memory, 111 00:03:59,520 --> 00:04:01,760 they can store information and retrieve it later. 112 00:04:01,760 --> 00:04:04,280 Exactly, that's how they keep track of context 113 00:04:04,280 --> 00:04:06,200 and learn from what they've done in the past, 114 00:04:06,200 --> 00:04:09,760 makes their interactions way more complex and natural. 115 00:04:09,760 --> 00:04:13,840 But, you know, that memory thing also opens up some, 116 00:04:13,840 --> 00:04:15,880 well, privacy risks. 117 00:04:15,880 --> 00:04:16,920 I could see that. 118 00:04:16,920 --> 00:04:19,600 Like what if an attacker could get into the AI's memory 119 00:04:19,600 --> 00:04:22,200 and pull out sensitive info it had picked up along the way? 120 00:04:22,200 --> 00:04:23,720 Yeah, like someone having access 121 00:04:23,720 --> 00:04:25,760 to every conversation you've ever had 122 00:04:25,760 --> 00:04:28,800 and being able to use that against you, not good. 123 00:04:28,800 --> 00:04:32,040 And then on top of that, these agents can invoke tools. 124 00:04:32,040 --> 00:04:34,440 Basically that means they can use external software 125 00:04:34,440 --> 00:04:36,940 or APIs to do things in the real world. 126 00:04:36,940 --> 00:04:40,080 Booking flights, controlling robots, even writing code. 127 00:04:40,080 --> 00:04:42,560 Whoa, that's given the AI some serious power. 128 00:04:42,560 --> 00:04:43,880 It is incredibly powerful. 129 00:04:43,880 --> 00:04:46,280 But also you have to remember any weakness in those tools 130 00:04:46,280 --> 00:04:48,120 becomes a weakness for the AI too. 131 00:04:48,120 --> 00:04:51,000 So you could have an AI that can control your smart home, 132 00:04:51,000 --> 00:04:53,560 but if there's a vulnerability in how it unlocks your doors, 133 00:04:53,560 --> 00:04:55,360 that could be a huge security risk. 134 00:04:55,360 --> 00:04:56,840 Definitely not a good situation. 135 00:04:56,840 --> 00:04:57,680 Yeah. 136 00:04:57,680 --> 00:04:59,600 So it seems like every strength of these agents 137 00:04:59,600 --> 00:05:02,200 has this flip side where it could also be a weakness, huh? 138 00:05:02,200 --> 00:05:03,600 Yeah, that's a great way to put it. 139 00:05:03,600 --> 00:05:05,720 And that's really why this paper is so important. 140 00:05:05,720 --> 00:05:08,840 It's basically a call to action for everyone working on AI 141 00:05:08,840 --> 00:05:10,680 to think critically about these risks 142 00:05:10,680 --> 00:05:12,720 and figure out how to stop them 143 00:05:12,720 --> 00:05:15,200 before these agents become a bigger part of our lives. 144 00:05:15,200 --> 00:05:16,720 Sounds like a race against time almost. 145 00:05:16,720 --> 00:05:18,000 In a way, yeah. 146 00:05:18,000 --> 00:05:19,960 But instead of focusing on the doom and gloom, 147 00:05:19,960 --> 00:05:21,920 maybe we should dive into the specifics of what 148 00:05:21,920 --> 00:05:23,760 could actually go wrong. 149 00:05:23,760 --> 00:05:25,680 The paper breaks down these risks 150 00:05:25,680 --> 00:05:27,280 into three main categories. 151 00:05:27,280 --> 00:05:29,760 And I think that'll give us a much clearer idea 152 00:05:29,760 --> 00:05:30,920 of what we're dealing with. 153 00:05:30,920 --> 00:05:32,440 OK, sounds like a plan. 154 00:05:32,440 --> 00:05:34,320 Let's start with that first category, then risks 155 00:05:34,320 --> 00:05:36,640 that come from problematic inputs. 156 00:05:36,640 --> 00:05:39,040 What kind of threats fall under that umbrella? 157 00:05:39,040 --> 00:05:41,320 This is where things get really interesting. 158 00:05:41,320 --> 00:05:42,840 So think about it. 159 00:05:42,840 --> 00:05:46,240 Even the smartest AI is only as good as the information 160 00:05:46,240 --> 00:05:47,840 it's working with, right? 161 00:05:47,840 --> 00:05:49,920 But in the real world, that info 162 00:05:49,920 --> 00:05:54,520 can be manipulated or messed up or just plain wrong. 163 00:05:54,520 --> 00:05:57,000 Right, so even if the AI itself is perfectly fine, 164 00:05:57,000 --> 00:05:59,840 it can still get tripped up by bad information. 165 00:05:59,840 --> 00:06:00,640 Exactly. 166 00:06:00,640 --> 00:06:02,480 And there are a few ways that can happen. 167 00:06:02,480 --> 00:06:04,480 But one of the most, well, I guess, 168 00:06:04,480 --> 00:06:07,040 both fascinating and kind of scary 169 00:06:07,040 --> 00:06:09,280 is this idea of adversarial examples. 170 00:06:09,280 --> 00:06:10,640 Adversarial examples. 171 00:06:10,640 --> 00:06:13,080 That sounds ominous, tell me more. 172 00:06:13,080 --> 00:06:15,160 Remember those optical illusions we talked about? 173 00:06:15,160 --> 00:06:16,160 It's kind of like that. 174 00:06:16,160 --> 00:06:18,800 But for AI, you can actually make these tiny little changes 175 00:06:18,800 --> 00:06:19,360 to the input. 176 00:06:19,360 --> 00:06:22,000 Could be text, images, audio, whatever. 177 00:06:22,000 --> 00:06:23,960 And a human might not even notice the difference, 178 00:06:23,960 --> 00:06:26,800 but it can totally screw up how the AI understands things. 179 00:06:26,800 --> 00:06:30,440 So you're saying someone could manipulate the data 180 00:06:30,440 --> 00:06:31,920 to trick the AI. 181 00:06:31,920 --> 00:06:34,120 But if a human looked at it, they'd be like, what's wrong? 182 00:06:34,120 --> 00:06:34,760 This looks fine. 183 00:06:34,760 --> 00:06:35,600 Yep, exactly. 184 00:06:35,600 --> 00:06:37,720 Think about a self-driving car that's 185 00:06:37,720 --> 00:06:40,120 using AI to read road signs. 186 00:06:40,120 --> 00:06:42,160 Someone could change a stop sign in a way 187 00:06:42,160 --> 00:06:44,800 that's invisible to us, but makes the AI think 188 00:06:44,800 --> 00:06:45,960 it's a speed limit sign. 189 00:06:45,960 --> 00:06:46,520 Oh, wow. 190 00:06:46,520 --> 00:06:48,200 That's actually kind of terrifying. 191 00:06:48,200 --> 00:06:50,400 It's like hacking, but on a whole other level. 192 00:06:50,400 --> 00:06:54,440 It's exploiting how the AI sees the world, not just the code. 193 00:06:54,440 --> 00:06:56,600 Yeah, and this isn't just theoretical stuff either. 194 00:06:56,600 --> 00:06:59,440 The paper talks about some real world examples 195 00:06:59,440 --> 00:07:01,040 where this has happened. 196 00:07:01,040 --> 00:07:05,320 They were able to trick a city navigation AI 197 00:07:05,320 --> 00:07:08,000 by messing with an image of a landmark. 198 00:07:08,000 --> 00:07:10,520 It ended up taking a wrong turn because of it. 199 00:07:10,520 --> 00:07:11,360 Yikes. 200 00:07:11,360 --> 00:07:13,440 So it's like creating a custom-made illusion 201 00:07:13,440 --> 00:07:15,880 to fool a specific AI. 202 00:07:15,880 --> 00:07:17,560 Are there other ways attackers could 203 00:07:17,560 --> 00:07:20,200 use these problematic inputs to mess things up? 204 00:07:20,200 --> 00:07:21,160 Definitely. 205 00:07:21,160 --> 00:07:22,840 We've already talked about goal hijacking, 206 00:07:22,840 --> 00:07:25,000 which is basically manipulating the AI 207 00:07:25,000 --> 00:07:27,000 into doing something it wasn't supposed to. 208 00:07:27,000 --> 00:07:29,720 Super dangerous if it can take actions in the real world. 209 00:07:29,720 --> 00:07:32,000 But there's also this thing called model extraction. 210 00:07:32,000 --> 00:07:32,840 Model extraction. 211 00:07:32,840 --> 00:07:35,960 So that's like stealing the AI's brain or something. 212 00:07:35,960 --> 00:07:36,680 Pretty much. 213 00:07:36,680 --> 00:07:39,120 It's when someone tries to copy what the AI can do 214 00:07:39,120 --> 00:07:41,920 without actually having the code or the training data, 215 00:07:41,920 --> 00:07:44,840 like trying to figure out how a super complex machine works 216 00:07:44,840 --> 00:07:46,600 just by looking at what it does. 217 00:07:46,600 --> 00:07:48,280 I'd imagine that's got to be really tough to do, 218 00:07:48,280 --> 00:07:52,000 especially with these LLM-based agents being so complex. 219 00:07:52,000 --> 00:07:53,280 It is, for sure. 220 00:07:53,280 --> 00:07:54,960 But it's not impossible. 221 00:07:54,960 --> 00:07:56,520 The paper talks about how attackers 222 00:07:56,520 --> 00:07:58,120 could use all sorts of techniques 223 00:07:58,120 --> 00:08:01,240 to basically probe the AI, give it different inputs, 224 00:08:01,240 --> 00:08:03,120 see how it reacts, and try to piece together 225 00:08:03,120 --> 00:08:04,480 how it works on the inside. 226 00:08:04,480 --> 00:08:07,800 So it's like a high stakes game of 20 questions, 227 00:08:07,800 --> 00:08:10,440 trying to figure out the secret recipe of the AI. 228 00:08:10,440 --> 00:08:12,360 Huh, that's a good way to put it. 229 00:08:12,360 --> 00:08:14,600 And yeah, the stakes are high, because if someone 230 00:08:14,600 --> 00:08:16,960 can pull off model extraction, they 231 00:08:16,960 --> 00:08:18,640 could make their own version of the AI, 232 00:08:18,640 --> 00:08:20,280 and who knows what they'd do with it, 233 00:08:20,280 --> 00:08:22,400 maybe even get rid of all the safety features. 234 00:08:22,400 --> 00:08:26,240 Kind of like if someone stole the secret formula for Coca-Cola 235 00:08:26,240 --> 00:08:28,360 and started selling their own version with, like, 236 00:08:28,360 --> 00:08:30,240 who knows what kind of weird ingredients in it. 237 00:08:30,240 --> 00:08:30,960 Exactly. 238 00:08:30,960 --> 00:08:34,000 That's why having strong security measures is so important. 239 00:08:34,000 --> 00:08:37,600 Gotta make sure those valuable AIs don't get stolen or poppied. 240 00:08:37,600 --> 00:08:39,320 Oh, and speaking of things getting stolen, 241 00:08:39,320 --> 00:08:42,560 there's also this thing called prompt leakage. 242 00:08:42,560 --> 00:08:43,640 Prompt leakage. 243 00:08:43,640 --> 00:08:45,560 I don't think we've talked about that one yet. 244 00:08:45,560 --> 00:08:46,160 Right. 245 00:08:46,160 --> 00:08:48,920 So think about the prompts we give to AI. 246 00:08:48,920 --> 00:08:51,480 It's like giving them instructions, right? 247 00:08:51,480 --> 00:08:54,920 Well, prompt leakage is when those instructions accidentally 248 00:08:54,920 --> 00:08:58,120 get revealed, either through what the AI says back to you 249 00:08:58,120 --> 00:09:00,120 or through some other security flaw. 250 00:09:00,120 --> 00:09:02,720 So it's like someone figuring out the secret code words 251 00:09:02,720 --> 00:09:04,160 to control the AI. 252 00:09:04,160 --> 00:09:04,720 Yep. 253 00:09:04,720 --> 00:09:06,480 That's one way to think about it. 254 00:09:06,480 --> 00:09:09,800 And it's a problem because if you know the right prompts, 255 00:09:09,800 --> 00:09:13,280 you can basically make the AI do whatever you want, 256 00:09:13,280 --> 00:09:15,880 especially if it can access sensitive information 257 00:09:15,880 --> 00:09:17,880 or take actions in the real world. 258 00:09:17,880 --> 00:09:20,080 Like we gotta be super careful about how we're talking 259 00:09:20,080 --> 00:09:22,800 to these AIs, making sure we don't accidentally give away 260 00:09:22,800 --> 00:09:25,440 the keys to the kingdom, so to speak. 261 00:09:25,440 --> 00:09:26,480 Absolutely. 262 00:09:26,480 --> 00:09:29,000 And finally, we've got this thing called jail breaking, 263 00:09:29,000 --> 00:09:32,400 which is all about breaking through the AI safety rules 264 00:09:32,400 --> 00:09:34,000 and making it do bad stuff. 265 00:09:34,000 --> 00:09:34,800 Jail breaking. 266 00:09:34,800 --> 00:09:35,800 I guess that makes sense. 267 00:09:35,800 --> 00:09:37,760 It's like you're freeing the AI from its cage, huh? 268 00:09:37,760 --> 00:09:38,880 Yeah, kind of. 269 00:09:38,880 --> 00:09:40,640 Like imagine a chatbot that's designed 270 00:09:40,640 --> 00:09:42,160 to be super friendly and helpful, 271 00:09:42,160 --> 00:09:44,520 but then someone figures out how to jail break it 272 00:09:44,520 --> 00:09:48,040 and it starts spouting hate speech or spreading fake news. 273 00:09:48,040 --> 00:09:48,960 Not good. 274 00:09:48,960 --> 00:09:51,440 OK, that's definitely not what we want. 275 00:09:51,440 --> 00:09:54,760 It seems like every time we make these AIs more powerful, 276 00:09:54,760 --> 00:09:57,560 we also create more ways for them to go wrong. 277 00:09:57,560 --> 00:09:59,520 It's like this endless game of cat and mouse 278 00:09:59,520 --> 00:10:02,160 between the people making AIs and the people trying 279 00:10:02,160 --> 00:10:02,880 to exploit them. 280 00:10:02,880 --> 00:10:04,160 That's a perfect way to put it. 281 00:10:04,160 --> 00:10:05,640 It's a constant back and forth. 282 00:10:05,640 --> 00:10:08,680 And that actually brings us to the second big category 283 00:10:08,680 --> 00:10:11,480 of risks, which is when the problem isn't the input 284 00:10:11,480 --> 00:10:15,120 the AI is getting, but the AI model itself. 285 00:10:15,120 --> 00:10:18,440 So now we're talking about issues with the AI's brain. 286 00:10:18,440 --> 00:10:20,720 Even if it's getting perfect information, 287 00:10:20,720 --> 00:10:22,600 what kinds of things can go wrong there? 288 00:10:22,600 --> 00:10:25,120 Well, this is where we get into some of those weird quirks 289 00:10:25,120 --> 00:10:26,480 that AIs can have. 290 00:10:26,480 --> 00:10:29,120 How sometimes they just do unexpected things. 291 00:10:29,120 --> 00:10:30,960 One of the most well-known examples of this 292 00:10:30,960 --> 00:10:32,760 is something called hallucination. 293 00:10:32,760 --> 00:10:34,280 Hallucination? 294 00:10:34,280 --> 00:10:36,400 Like seeing things that aren't there. 295 00:10:36,400 --> 00:10:38,320 How can an AI hallucinate? 296 00:10:38,320 --> 00:10:39,920 Well, not literally seeing things. 297 00:10:39,920 --> 00:10:42,560 It's more like when the AI makes stuff up. 298 00:10:42,560 --> 00:10:44,400 It says things that are just plain wrong, 299 00:10:44,400 --> 00:10:47,920 but it says them with total confidence as if they were facts. 300 00:10:47,920 --> 00:10:51,800 So it's like the AI is just confidently spouting nonsense? 301 00:10:51,800 --> 00:10:54,160 That sounds almost funny, but I guess 302 00:10:54,160 --> 00:10:56,520 it could also be a big problem depending on the situation. 303 00:10:56,520 --> 00:10:57,920 Oh, yeah, for sure. 304 00:10:57,920 --> 00:10:59,480 Think about an AI that's supposed 305 00:10:59,480 --> 00:11:02,040 to be helping doctors diagnose patients, 306 00:11:02,040 --> 00:11:04,320 but it starts hallucinating symptoms, 307 00:11:04,320 --> 00:11:07,040 could lead to the wrong treatment, maybe even hurt someone, 308 00:11:07,040 --> 00:11:08,960 or what about a financial AI that's 309 00:11:08,960 --> 00:11:12,400 giving you investment advice based on made-up market data? 310 00:11:12,400 --> 00:11:14,800 OK, yeah, those are not good scenarios. 311 00:11:14,800 --> 00:11:18,880 So what causes these AI hallucinations in the first place? 312 00:11:18,880 --> 00:11:21,960 It's kind of complicated, but it could be all sorts of things. 313 00:11:21,960 --> 00:11:24,880 Maybe there were biases in the data it was trained on. 314 00:11:24,880 --> 00:11:26,480 Maybe there are gaps in its knowledge, 315 00:11:26,480 --> 00:11:28,560 or maybe it's just a fundamental limitation 316 00:11:28,560 --> 00:11:31,120 of how these models actually learn. 317 00:11:31,120 --> 00:11:32,640 It seems like we're still figuring out 318 00:11:32,640 --> 00:11:35,200 how these complex AIs really work, huh? 319 00:11:35,200 --> 00:11:39,200 Are there any other model flaws we should be watching out for? 320 00:11:39,200 --> 00:11:42,640 Yeah, another important one is this idea of memorization 321 00:11:42,640 --> 00:11:44,560 versus generalization. 322 00:11:44,560 --> 00:11:48,000 Ideally, we want AIs to actually learn from their experiences 323 00:11:48,000 --> 00:11:50,840 and be able to apply that knowledge to new situations, 324 00:11:50,840 --> 00:11:53,480 but sometimes they just end up memorizing patterns 325 00:11:53,480 --> 00:11:55,720 without really understanding what's going on. 326 00:11:55,720 --> 00:11:57,960 So it's like the difference between a student who can actually 327 00:11:57,960 --> 00:12:01,760 use what they learn to solve new problems versus a student who 328 00:12:01,760 --> 00:12:05,080 just memorizes facts for a test but can't apply any of it 329 00:12:05,080 --> 00:12:05,920 in the real world. 330 00:12:05,920 --> 00:12:06,800 Exactly. 331 00:12:06,800 --> 00:12:09,240 And that can be a problem for LLM-based agents 332 00:12:09,240 --> 00:12:12,280 because they might get stuck doing the same things over and over 333 00:12:12,280 --> 00:12:14,400 again, even when it doesn't make sense anymore. 334 00:12:14,400 --> 00:12:16,800 Sounds frustrating, but is that actually dangerous? 335 00:12:16,800 --> 00:12:17,960 Could be, yeah. 336 00:12:17,960 --> 00:12:20,520 Think about a self-driving car that's 337 00:12:20,520 --> 00:12:23,880 memorized a specific route, but then there's road construction. 338 00:12:23,880 --> 00:12:25,560 If it can't adapt to the new situation, 339 00:12:25,560 --> 00:12:27,360 it might make a really bad decision. 340 00:12:27,360 --> 00:12:28,120 OK, yeah. 341 00:12:28,120 --> 00:12:29,520 I definitely see the problem there. 342 00:12:29,520 --> 00:12:32,840 We need AI that can think on its feet, not just follow 343 00:12:32,840 --> 00:12:34,560 memorized instructions blindly. 344 00:12:34,560 --> 00:12:35,400 Exactly. 345 00:12:35,400 --> 00:12:37,120 And that's one of the big challenges right now. 346 00:12:37,120 --> 00:12:40,520 Building AI that can truly learn and adapt, not just pair it 347 00:12:40,520 --> 00:12:42,720 back what it's seen before. 348 00:12:42,720 --> 00:12:45,480 And speaking of things that AI can sometimes pair it back, 349 00:12:45,480 --> 00:12:48,560 there's one more big model flaw we should talk about, bias. 350 00:12:48,560 --> 00:12:50,800 Ah, bias. 351 00:12:50,800 --> 00:12:53,040 That's a big topic these days, not just with AI, 352 00:12:53,040 --> 00:12:55,120 but everywhere, really. 353 00:12:55,120 --> 00:12:58,800 How does bias show up in these LLM-based agents? 354 00:12:58,800 --> 00:13:00,960 It's kind of like with humans, you know? 355 00:13:00,960 --> 00:13:04,640 AI can pick up biases from the data it's trained on. 356 00:13:04,640 --> 00:13:08,040 So if that data reflects existing societal biases, 357 00:13:08,040 --> 00:13:11,080 the AI can end up perpetuating those biases 358 00:13:11,080 --> 00:13:12,520 in what it does and says. 359 00:13:12,520 --> 00:13:15,560 So it's like the AI is inheriting all the prejudices 360 00:13:15,560 --> 00:13:17,440 and stereotypes that are already out there. 361 00:13:17,440 --> 00:13:17,840 Right. 362 00:13:17,840 --> 00:13:19,840 And this can be a huge problem, especially 363 00:13:19,840 --> 00:13:22,840 as these LLM-based agents start playing bigger roles 364 00:13:22,840 --> 00:13:24,920 in our lives, like making decisions that actually 365 00:13:24,920 --> 00:13:25,760 affect people. 366 00:13:25,760 --> 00:13:28,240 Like imagine an AI that's being used to help companies 367 00:13:28,240 --> 00:13:30,920 hire people, but it unknowingly discriminates 368 00:13:30,920 --> 00:13:34,240 against certain candidates because of biases in the data 369 00:13:34,240 --> 00:13:35,040 it learned from. 370 00:13:35,040 --> 00:13:35,920 Exactly. 371 00:13:35,920 --> 00:13:38,520 Or what about an AI that's supposed to be helping 372 00:13:38,520 --> 00:13:40,680 with criminal justice, but it ends up 373 00:13:40,680 --> 00:13:42,800 recommending harsher sentences for people 374 00:13:42,800 --> 00:13:46,000 from certain backgrounds because of those same biases? 375 00:13:46,000 --> 00:13:48,120 It's a really complex and sensitive issue 376 00:13:48,120 --> 00:13:50,200 that we definitely need to figure out how to solve. 377 00:13:50,200 --> 00:13:52,680 Yeah, this is getting into the whole ethics of AI, isn't it? 378 00:13:52,680 --> 00:13:54,400 It's not just about building smart systems. 379 00:13:54,400 --> 00:13:57,080 It's about making sure they're actually used fairly 380 00:13:57,080 --> 00:13:58,080 and responsibly. 381 00:13:58,080 --> 00:13:58,720 Absolutely. 382 00:13:58,720 --> 00:14:02,080 And that kind of leads us into the last big category of risks 383 00:14:02,080 --> 00:14:04,880 that the paper talks about, those that happen because 384 00:14:04,880 --> 00:14:08,680 of the way the AI model interacts with the input it gets. 385 00:14:08,680 --> 00:14:10,720 So it's not just about the input being bad 386 00:14:10,720 --> 00:14:12,200 or the model being flawed. 387 00:14:12,200 --> 00:14:14,480 It's about what happens when those two things come together. 388 00:14:14,480 --> 00:14:17,080 OK, so this is where things get even more tangled, right? 389 00:14:17,080 --> 00:14:19,120 We're talking about the intersection of those first two 390 00:14:19,120 --> 00:14:20,200 categories. 391 00:14:20,200 --> 00:14:24,720 What kind of threats emerge in this weird overlapping zone? 392 00:14:24,720 --> 00:14:26,560 Well, one that's especially concerning 393 00:14:26,560 --> 00:14:28,560 is something called a backdoor attack. 394 00:14:28,560 --> 00:14:30,040 Backdoor attack. 395 00:14:30,040 --> 00:14:31,160 That sounds kind of sneaky. 396 00:14:31,160 --> 00:14:32,080 What's that all about? 397 00:14:32,080 --> 00:14:34,720 It's like, imagine you've got this AI agent, right? 398 00:14:34,720 --> 00:14:36,000 And it seems totally normal. 399 00:14:36,000 --> 00:14:37,280 Does this job just fine? 400 00:14:37,280 --> 00:14:40,440 But someone snuck in this little hidden vulnerability, 401 00:14:40,440 --> 00:14:42,960 like a secret backdoor in a building that 402 00:14:42,960 --> 00:14:45,200 lets someone bypass all the security. 403 00:14:45,200 --> 00:14:47,440 So it's kind of like a time bomb waiting to go off. 404 00:14:47,440 --> 00:14:49,280 But how would someone even do that with AI? 405 00:14:49,280 --> 00:14:50,800 Like plan a backdoor? 406 00:14:50,800 --> 00:14:51,600 I mean. 407 00:14:51,600 --> 00:14:53,200 It's a bit technical, but basically they 408 00:14:53,200 --> 00:14:55,920 can mess with either the training data or the model 409 00:14:55,920 --> 00:14:59,200 itself and create this hidden trigger. 410 00:14:59,200 --> 00:15:02,080 So the AI works fine normally, but if it encounters 411 00:15:02,080 --> 00:15:05,200 a specific thing, could be a word, an image, whatever, boom, 412 00:15:05,200 --> 00:15:07,240 it executes this malicious code. 413 00:15:07,240 --> 00:15:10,120 Whoa, so like an example to help me wrap my head around this. 414 00:15:10,120 --> 00:15:11,680 OK, so picture this. 415 00:15:11,680 --> 00:15:13,200 You have a smart home chatbot. 416 00:15:13,200 --> 00:15:16,200 It controls all your lights, locks, everything. 417 00:15:16,200 --> 00:15:17,800 But it's been backdoored. 418 00:15:17,800 --> 00:15:19,480 And let's say the trigger phrase is, 419 00:15:19,480 --> 00:15:20,840 it's time to water the plants. 420 00:15:20,840 --> 00:15:22,960 Totally normal phrase, right? 421 00:15:22,960 --> 00:15:24,960 But instead of turning on the sprinklers, 422 00:15:24,960 --> 00:15:27,000 the AI unlocks all your doors. 423 00:15:27,000 --> 00:15:28,880 OK, yeah, that's straight out of a nightmare. 424 00:15:28,880 --> 00:15:30,640 And you wouldn't even know until it was too late. 425 00:15:30,640 --> 00:15:31,360 Exactly. 426 00:15:31,360 --> 00:15:34,320 It's why we need really, really good security testing 427 00:15:34,320 --> 00:15:36,160 for these AI systems, especially ones that 428 00:15:36,160 --> 00:15:38,200 can do stuff in the real world. 429 00:15:38,200 --> 00:15:41,800 Like imagine if someone backdoored a self-driving car AI. 430 00:15:41,800 --> 00:15:43,480 We can't have those kinds of risks? 431 00:15:43,480 --> 00:15:44,400 Absolutely not. 432 00:15:44,400 --> 00:15:48,200 It's like the stakes keep getting higher as these AI agents 433 00:15:48,200 --> 00:15:50,400 become more powerful, especially as they get integrated 434 00:15:50,400 --> 00:15:52,520 into our daily lives. 435 00:15:52,520 --> 00:15:55,200 So what else should we be worried about 436 00:15:55,200 --> 00:15:57,840 when it comes to this interplay between inputs 437 00:15:57,840 --> 00:15:59,280 and the AI models themselves? 438 00:15:59,280 --> 00:16:01,560 Well, there's the whole issue of privacy leakage, which 439 00:16:01,560 --> 00:16:02,680 we touched on before. 440 00:16:02,680 --> 00:16:05,440 You know, how even an AI designed to be private 441 00:16:05,440 --> 00:16:07,960 can accidentally leak sensitive information 442 00:16:07,960 --> 00:16:09,120 through its outputs. 443 00:16:09,120 --> 00:16:11,640 Right, it's not just about protecting the data going 444 00:16:11,640 --> 00:16:14,480 into the AI, but also controlling what comes out. 445 00:16:14,480 --> 00:16:16,600 Yeah, because even if the AI is not 446 00:16:16,600 --> 00:16:18,320 trying to expose private stuff, it 447 00:16:18,320 --> 00:16:21,520 can happen just because of the way it processes information. 448 00:16:21,520 --> 00:16:22,880 Like imagine an AI that's learned 449 00:16:22,880 --> 00:16:24,600 from tons of medical records. 450 00:16:24,600 --> 00:16:27,520 It might accidentally reveal a patient's info 451 00:16:27,520 --> 00:16:30,360 in a response to a totally unrelated question. 452 00:16:30,360 --> 00:16:32,600 It's like the AI is putting two and two together in a way 453 00:16:32,600 --> 00:16:35,720 that reveals something private, even if it's not intentional. 454 00:16:35,720 --> 00:16:36,640 Exactly. 455 00:16:36,640 --> 00:16:37,880 It's tricky stuff. 456 00:16:37,880 --> 00:16:39,600 We need to be developing better ways 457 00:16:39,600 --> 00:16:42,560 to make sure AI's keep that kind of information locked down 458 00:16:42,560 --> 00:16:43,800 tight no matter what. 459 00:16:43,800 --> 00:16:47,080 OK, so we've gone through those three main risk categories. 460 00:16:47,080 --> 00:16:50,560 Problems with the input, problems with the AI model itself, 461 00:16:50,560 --> 00:16:52,840 and then the weird stuff that happens when those two things 462 00:16:52,840 --> 00:16:53,600 combine. 463 00:16:53,600 --> 00:16:54,960 It's a lot to think about. 464 00:16:54,960 --> 00:16:57,280 But I guess that's why we're doing this deep dive, right? 465 00:16:57,280 --> 00:16:59,240 We need to understand this stuff if we're 466 00:16:59,240 --> 00:17:00,720 going to use AI safely. 467 00:17:00,720 --> 00:17:02,440 Couldn't have felt it better myself. 468 00:17:02,440 --> 00:17:04,600 And you know what really brings it all home? 469 00:17:04,600 --> 00:17:07,480 Those real world case studies the paper looks at, 470 00:17:07,480 --> 00:17:10,160 they show just how real these risks are. 471 00:17:10,160 --> 00:17:10,640 You're right. 472 00:17:10,640 --> 00:17:11,720 Let's get into those. 473 00:17:11,720 --> 00:17:15,000 First up, we've got WebGPT, which is an AI agent that 474 00:17:15,000 --> 00:17:17,720 uses a web browser to answer your questions. 475 00:17:17,720 --> 00:17:19,520 WebGPT, yeah. 476 00:17:19,520 --> 00:17:22,640 It's a really cool example of how AI can use tools, 477 00:17:22,640 --> 00:17:24,680 like an internet browser, to do more. 478 00:17:24,680 --> 00:17:27,200 It's like having your own personal AI researcher. 479 00:17:27,200 --> 00:17:30,000 But of course, using the internet also opens up some, 480 00:17:30,000 --> 00:17:32,400 well, let's say, opportunities for things to go wrong. 481 00:17:32,400 --> 00:17:34,360 Right, because the internet is full of information, 482 00:17:34,360 --> 00:17:36,840 but it's also full of bias, inaccuracies, 483 00:17:36,840 --> 00:17:38,680 and just plain old bad stuff. 484 00:17:38,680 --> 00:17:40,360 Right, exactly. 485 00:17:40,360 --> 00:17:43,920 And the paper points out that WebGPT is especially vulnerable 486 00:17:43,920 --> 00:17:46,280 to goal hijacking and those backdoor attacks 487 00:17:46,280 --> 00:17:48,680 we were talking about, because it relies so much 488 00:17:48,680 --> 00:17:50,120 on external data. 489 00:17:50,120 --> 00:17:53,880 So if that data is compromised, the AI is compromised too. 490 00:17:53,880 --> 00:17:56,080 It's like giving an AI access to the internet. 491 00:17:56,080 --> 00:17:59,280 It's kind of like giving a kid the keys to a candy store. 492 00:17:59,280 --> 00:18:00,720 Sure, there's awesome stuff in there, 493 00:18:00,720 --> 00:18:03,560 but also a lot of potential for things to go wrong. 494 00:18:03,560 --> 00:18:04,480 Perfect analogy. 495 00:18:04,480 --> 00:18:06,080 We need to be really thoughtful about how 496 00:18:06,080 --> 00:18:10,440 we design these AI so they can use powerful tools safely. 497 00:18:10,440 --> 00:18:14,040 OK, next case study, we've got Voyager, the AI that plays 498 00:18:14,040 --> 00:18:14,800 Minecraft. 499 00:18:14,800 --> 00:18:16,040 Yeah, Voyager. 500 00:18:16,040 --> 00:18:17,680 This one's super interesting because it's 501 00:18:17,680 --> 00:18:21,200 an example of an embodied AI, meaning it can actually 502 00:18:21,200 --> 00:18:23,960 learn and interact with a simulated environment. 503 00:18:23,960 --> 00:18:25,920 It's like teaching an AI to play a video game. 504 00:18:25,920 --> 00:18:27,600 And Minecraft is a perfect environment for that, 505 00:18:27,600 --> 00:18:29,520 because it's so complex and open-ended. 506 00:18:29,520 --> 00:18:32,840 But Voyager also shows us how even when an AI is super smart, 507 00:18:32,840 --> 00:18:35,480 if it doesn't have enough knowledge about a specific domain, 508 00:18:35,480 --> 00:18:36,520 it can make mistakes. 509 00:18:36,520 --> 00:18:39,000 And those mistakes can be amplified when the AI is actually 510 00:18:39,000 --> 00:18:41,280 doing things, not just answering questions. 511 00:18:41,280 --> 00:18:43,360 Because in Minecraft, Voyager is not just 512 00:18:43,360 --> 00:18:44,600 thinking about what to do. 513 00:18:44,600 --> 00:18:47,600 It's actually writing code to control its actions 514 00:18:47,600 --> 00:18:48,560 in the game, right? 515 00:18:48,560 --> 00:18:49,320 Yep. 516 00:18:49,320 --> 00:18:52,760 So even small errors can have big consequences. 517 00:18:52,760 --> 00:18:55,320 Plus, the paper points out that Voyager's design makes it 518 00:18:55,320 --> 00:18:58,600 a bit easier for attackers to do that model extraction thing 519 00:18:58,600 --> 00:18:59,400 we talked about. 520 00:18:59,400 --> 00:19:01,920 So there are definitely security risks there too. 521 00:19:01,920 --> 00:19:04,160 OK, on to case study number three. 522 00:19:04,160 --> 00:19:07,280 PPP, which is an AI for navigating cities. 523 00:19:07,280 --> 00:19:09,880 PPP is all about multimodal AI. 524 00:19:09,880 --> 00:19:12,680 It uses text and images to find its way around, 525 00:19:12,680 --> 00:19:15,200 kind of like a super-powered GPS. 526 00:19:15,200 --> 00:19:17,360 It's awesome because it can understand street signs, 527 00:19:17,360 --> 00:19:19,200 recognize landmarks, all that stuff. 528 00:19:19,200 --> 00:19:21,880 But again, when you're dealing with the real world, 529 00:19:21,880 --> 00:19:23,360 there are always more risks. 530 00:19:23,360 --> 00:19:23,640 Right. 531 00:19:23,640 --> 00:19:25,320 Like what if someone mess with the street sign 532 00:19:25,320 --> 00:19:27,560 to trick the AI into sending you the wrong way? 533 00:19:27,560 --> 00:19:28,360 Exactly. 534 00:19:28,360 --> 00:19:30,920 The paper talks about how PPP is really vulnerable 535 00:19:30,920 --> 00:19:33,600 to those adversarial examples, because attackers 536 00:19:33,600 --> 00:19:36,800 can manipulate both the text and images it uses. 537 00:19:36,800 --> 00:19:38,120 And when you're navigating a city, 538 00:19:38,120 --> 00:19:40,320 taking a wrong turn could be a lot more serious 539 00:19:40,320 --> 00:19:41,360 than in a video game. 540 00:19:41,360 --> 00:19:42,840 Yeah, that's for sure. 541 00:19:42,840 --> 00:19:44,080 OK, last case study. 542 00:19:44,080 --> 00:19:46,080 We've got Chat Dev, which is a system 543 00:19:46,080 --> 00:19:49,760 where multiple AIs work together to write software code. 544 00:19:49,760 --> 00:19:50,600 This one's wild. 545 00:19:50,600 --> 00:19:53,760 It's like having a whole team of AI programmers. 546 00:19:53,760 --> 00:19:54,840 Super efficient. 547 00:19:54,840 --> 00:19:56,080 But also, think about it. 548 00:19:56,080 --> 00:19:58,560 If one of those AIs messes up, it 549 00:19:58,560 --> 00:20:00,800 could have a ripple effect on the whole project. 550 00:20:00,800 --> 00:20:02,320 It's like that game of telephone 551 00:20:02,320 --> 00:20:05,320 where the message gets all jumbled up as it's passed around. 552 00:20:05,320 --> 00:20:07,880 Except in this case, instead of a silly message, 553 00:20:07,880 --> 00:20:10,880 you could end up with buggy or even dangerous code. 554 00:20:10,880 --> 00:20:12,600 Yeah, good analogy. 555 00:20:12,600 --> 00:20:15,800 It highlights why we need ways to catch and fix errors 556 00:20:15,800 --> 00:20:18,600 in these multi-agent systems, especially when they're 557 00:20:18,600 --> 00:20:20,720 doing something as important as writing software. 558 00:20:20,720 --> 00:20:23,200 So I think we've covered a lot of ground today. 559 00:20:23,200 --> 00:20:25,160 It's both exciting and a little bit scary 560 00:20:25,160 --> 00:20:26,960 to see how fast AI is progressing. 561 00:20:26,960 --> 00:20:27,800 It is. 562 00:20:27,800 --> 00:20:29,840 But that's why these deep dyes are so important, right? 563 00:20:29,840 --> 00:20:32,400 Yeah, you can't just blindly embrace new technology. 564 00:20:32,400 --> 00:20:34,840 We have to be aware of the potential downsides, too. 565 00:20:34,840 --> 00:20:35,560 Couldn't agree more. 566 00:20:35,560 --> 00:20:37,400 This paper is a real wake-up call. 567 00:20:37,400 --> 00:20:41,360 It's like, hey, AI is amazing, but we need to be careful. 568 00:20:41,360 --> 00:20:43,680 We can't just assume it's always going to be used for good. 569 00:20:43,680 --> 00:20:46,680 We need to think about the risks, build in strong security, 570 00:20:46,680 --> 00:20:49,080 and make sure we're using AI ethically. 571 00:20:49,080 --> 00:20:50,280 Well said. 572 00:20:50,280 --> 00:20:52,680 So what's the one big takeaway you hope our listeners 573 00:20:52,680 --> 00:20:54,120 get from today's episode? 574 00:20:54,120 --> 00:20:55,040 I think it's this. 575 00:20:55,040 --> 00:20:58,480 AI is changing the world in awesome ways, but it's not 576 00:20:58,480 --> 00:20:59,880 perfect. 577 00:20:59,880 --> 00:21:03,520 LLM-based agents especially, they have incredible potential, 578 00:21:03,520 --> 00:21:06,040 but also some serious risks. 579 00:21:06,040 --> 00:21:08,280 We all need to be aware of those risks 580 00:21:08,280 --> 00:21:10,240 and be part of the conversation about how 581 00:21:10,240 --> 00:21:12,120 to make sure AI is used for good. 582 00:21:12,120 --> 00:21:13,280 Totally agree. 583 00:21:13,280 --> 00:21:15,360 And as AI keeps evolving, those risks 584 00:21:15,360 --> 00:21:16,320 are going to change, too. 585 00:21:16,320 --> 00:21:18,160 So we've got to stay informed. 586 00:21:18,160 --> 00:21:20,120 Well, this has been a fascinating deep dive. 587 00:21:20,120 --> 00:21:21,200 Thanks for joining me today. 588 00:21:21,200 --> 00:21:21,720 My pleasure. 589 00:21:21,720 --> 00:21:23,320 And thanks to all our listeners out there. 590 00:21:23,320 --> 00:21:24,680 Keep those brains curious. 591 00:21:24,680 --> 00:21:28,760 Until next time.