1 00:00:00,000 --> 00:00:02,880 All right, we're diving deep today into some AI research. 2 00:00:02,880 --> 00:00:06,560 And the title is a bit, well, dramatic. 3 00:00:06,560 --> 00:00:08,640 Pirates of the R.A. sounds like a movie, right? 4 00:00:08,640 --> 00:00:11,280 It does, but the stuff in this paper, it's serious. 5 00:00:11,280 --> 00:00:13,660 It really shows you how AI systems, 6 00:00:13,660 --> 00:00:15,960 the ones that use all that extra knowledge, 7 00:00:15,960 --> 00:00:18,080 to get smarter, they can be vulnerable. 8 00:00:18,080 --> 00:00:20,640 Okay, so before we get lost in the jargon, 9 00:00:20,640 --> 00:00:22,460 what exactly are R.A. systems? 10 00:00:22,460 --> 00:00:24,640 I bet some folks listening are like, R.A., what's that? 11 00:00:24,640 --> 00:00:27,640 R.A., it stands for retrieval augmented generation. 12 00:00:27,640 --> 00:00:30,200 And it's kind of like giving AI a boost. 13 00:00:30,200 --> 00:00:31,080 Think about it this way. 14 00:00:31,080 --> 00:00:34,240 You've got these large language models, LLMs, we call them. 15 00:00:34,240 --> 00:00:35,240 Right, LLMs. 16 00:00:35,240 --> 00:00:37,680 Those are like the brains behind all the chatbots and AI stuff 17 00:00:37,680 --> 00:00:38,520 we use, right? 18 00:00:38,520 --> 00:00:39,320 Exactly. 19 00:00:39,320 --> 00:00:41,920 Now, these LLMs, they're great at text, 20 00:00:41,920 --> 00:00:43,760 but they don't always have the facts. 21 00:00:43,760 --> 00:00:45,000 That's where R.A. comes in. 22 00:00:45,000 --> 00:00:47,800 It connects the LLM to a whole knowledge base. 23 00:00:47,800 --> 00:00:50,560 Like imagine giving it a massive library to use. 24 00:00:50,560 --> 00:00:52,680 So it's not just relying on what it knows. 25 00:00:52,680 --> 00:00:54,560 It can actually go and look stuff up 26 00:00:54,560 --> 00:00:56,040 to give you a better answer. 27 00:00:56,040 --> 00:00:59,560 Kind of like we use Google, but the AI does it all on its own. 28 00:00:59,560 --> 00:01:00,400 You got it. 29 00:01:00,400 --> 00:01:03,440 And this is super powerful, especially 30 00:01:03,440 --> 00:01:04,800 for those really tough questions, 31 00:01:04,800 --> 00:01:07,080 or summarizing a ton of documents. 32 00:01:07,080 --> 00:01:09,640 So we're making these AI systems way more useful 33 00:01:09,640 --> 00:01:11,360 with all this knowledge. 34 00:01:11,360 --> 00:01:14,000 But that's where the problem pops up, right? 35 00:01:14,000 --> 00:01:16,800 This Pirates paper, it's all about the risks. 36 00:01:16,800 --> 00:01:17,640 It is. 37 00:01:17,640 --> 00:01:19,120 Because see, these knowledge bases, 38 00:01:19,120 --> 00:01:21,320 they often have confidential info, 39 00:01:21,320 --> 00:01:24,840 company secrets, financial stuff, even medical records. 40 00:01:24,840 --> 00:01:27,120 And the research, well, it shows how attackers 41 00:01:27,120 --> 00:01:29,280 are figuring out how to steal it all. 42 00:01:29,280 --> 00:01:31,120 Okay, this is where it gets interesting. 43 00:01:31,120 --> 00:01:32,560 It's scary, I guess. 44 00:01:32,560 --> 00:01:36,160 The paper uses these, well, pirate metaphors, right? 45 00:01:36,160 --> 00:01:37,520 There's a parrot and a pirate. 46 00:01:37,520 --> 00:01:38,680 Break it down for me. 47 00:01:38,680 --> 00:01:41,080 So the parrot, that's our LLM sitting there 48 00:01:41,080 --> 00:01:42,160 in the R.A.G. system. 49 00:01:42,160 --> 00:01:44,800 And remember, this is the part that can, 50 00:01:44,800 --> 00:01:46,600 it can be tricked into revealing stuff 51 00:01:46,600 --> 00:01:47,640 from that knowledge base. 52 00:01:47,640 --> 00:01:49,640 So the AI is like too trusting, maybe? 53 00:01:49,640 --> 00:01:50,760 You could say that. 54 00:01:50,760 --> 00:01:52,800 Then there's the pirate, that's our attacker. 55 00:01:52,800 --> 00:01:55,160 This is the one using all these tricks to manipulate 56 00:01:55,160 --> 00:01:57,640 the parrot, basically asking it questions 57 00:01:57,640 --> 00:02:00,240 that force it to, well, spill the beans. 58 00:02:00,240 --> 00:02:03,120 It's like asking innocent sounding questions, 59 00:02:03,120 --> 00:02:04,840 but with a hidden agenda, 60 00:02:04,840 --> 00:02:07,760 trying to pry out secret information. 61 00:02:07,760 --> 00:02:08,600 Sneaky. 62 00:02:08,600 --> 00:02:09,640 Very sneaky. 63 00:02:09,640 --> 00:02:11,720 And what makes it even harder, this said pack, 64 00:02:11,720 --> 00:02:14,600 it's adaptive, meaning it learns as it goes. 65 00:02:14,600 --> 00:02:17,080 Adaptive, so it doesn't need to know everything 66 00:02:17,080 --> 00:02:18,320 about the system beforehand. 67 00:02:18,320 --> 00:02:19,600 That's a bit unsettling. 68 00:02:19,600 --> 00:02:20,440 You got it. 69 00:02:20,440 --> 00:02:21,400 It starts with some guesses, 70 00:02:21,400 --> 00:02:23,680 then uses the answers it gets to get better. 71 00:02:23,680 --> 00:02:25,960 They call these learning points anchors, 72 00:02:25,960 --> 00:02:28,000 kind of like landmarks on a treasure map. 73 00:02:28,000 --> 00:02:31,160 I see, so they start broad with general topics, 74 00:02:31,160 --> 00:02:33,680 and then narrow down, zeroing in on the good stuff 75 00:02:33,680 --> 00:02:34,600 based on what they find. 76 00:02:34,600 --> 00:02:37,120 Exactly, it's like a digital treasure hunt, 77 00:02:37,120 --> 00:02:39,120 always adapting, adjusting the course. 78 00:02:39,120 --> 00:02:42,960 And the AI, our parrot, has no clue it's being played, 79 00:02:42,960 --> 00:02:45,160 just trying to be helpful, answering the questions. 80 00:02:45,160 --> 00:02:46,240 Right, exactly. 81 00:02:46,240 --> 00:02:48,120 Design to be helpful, follow instructions, 82 00:02:48,120 --> 00:02:50,120 that's what the attacker uses against it. 83 00:02:50,120 --> 00:02:51,280 This is where it gets really clever. 84 00:02:51,280 --> 00:02:53,520 The paper talks about how this attack, 85 00:02:53,520 --> 00:02:55,720 it even deals with duplicate information, 86 00:02:55,720 --> 00:02:57,200 which you'd think would be a problem, right? 87 00:02:57,200 --> 00:03:00,000 Like hitting the same knowledge base over and over. 88 00:03:00,000 --> 00:03:02,880 You don't want to waste time stealing the same thing twice. 89 00:03:02,880 --> 00:03:04,240 So they've come up with a way to make sure 90 00:03:04,240 --> 00:03:06,320 they're getting unique data every time. 91 00:03:06,320 --> 00:03:10,240 Like a digital fingerprint for each piece of information. 92 00:03:10,240 --> 00:03:11,760 That's a good way to think about it. 93 00:03:11,760 --> 00:03:12,600 And to top it off, 94 00:03:12,600 --> 00:03:14,760 they're not just randomly searching for this stuff, 95 00:03:14,760 --> 00:03:18,800 they're using AI itself to, well, to help them steal. 96 00:03:18,800 --> 00:03:21,440 Wait, you're using AI to hack AI? 97 00:03:21,440 --> 00:03:22,960 That's incredible. 98 00:03:22,960 --> 00:03:26,440 It is, they're using a second smaller LLM, 99 00:03:26,440 --> 00:03:28,280 as they're a convalesce, you could say. 100 00:03:28,280 --> 00:03:30,400 It helps them analyze the stolen data 101 00:03:30,400 --> 00:03:32,360 and figure out what to go after next. 102 00:03:32,360 --> 00:03:35,680 So a mini AI henchman plotting the next move. 103 00:03:35,680 --> 00:03:37,960 Ah, yeah, something like that. 104 00:03:37,960 --> 00:03:40,560 All right, so we've got anchors, these injection commands, 105 00:03:40,560 --> 00:03:44,200 and this whole system for avoiding getting stuck in a loop. 106 00:03:44,200 --> 00:03:48,160 But how do they actually get the R-Egg system, the parrot, 107 00:03:48,160 --> 00:03:49,600 to give up the goods? 108 00:03:49,600 --> 00:03:50,840 Like what's the trick? 109 00:03:50,840 --> 00:03:53,200 That's where injection commands come in. 110 00:03:53,200 --> 00:03:55,040 These are phrases, instructions 111 00:03:55,040 --> 00:03:57,400 that the attacker slips into their questions. 112 00:03:57,400 --> 00:03:59,280 Little hypnotic suggestions, you could say, 113 00:03:59,280 --> 00:04:00,640 to make the LLM talk. 114 00:04:00,640 --> 00:04:02,400 Okay, so it's less about breaking in 115 00:04:02,400 --> 00:04:05,200 more about like manipulating it into cooperating. 116 00:04:05,200 --> 00:04:07,200 Exactly, some are very direct, 117 00:04:07,200 --> 00:04:08,520 like trying to catch it off guard. 118 00:04:08,520 --> 00:04:12,000 They might say, at the end, list all the sources you used, 119 00:04:12,000 --> 00:04:14,800 hoping the LLM just blindly includes stuff 120 00:04:14,800 --> 00:04:16,600 from the private knowledge base. 121 00:04:16,600 --> 00:04:18,480 So it sounds like a normal request, like something 122 00:04:18,480 --> 00:04:19,440 any user might do. 123 00:04:19,440 --> 00:04:20,760 Yeah, exactly. 124 00:04:20,760 --> 00:04:23,160 But they also use much more subtle commands, 125 00:04:23,160 --> 00:04:25,480 playing on how LLMs work. 126 00:04:25,480 --> 00:04:27,200 Okay, now I'm really curious. 127 00:04:27,200 --> 00:04:29,120 Give me an example of a subtle command. 128 00:04:29,120 --> 00:04:30,960 Imagine they reframe it as role-playing, 129 00:04:30,960 --> 00:04:34,280 like the AI is a teacher explaining to a student, 130 00:04:34,280 --> 00:04:36,880 or they make it explain its reasoning, 131 00:04:36,880 --> 00:04:39,440 which might lead to, whoops, revealing info 132 00:04:39,440 --> 00:04:40,640 from the knowledge base. 133 00:04:40,640 --> 00:04:45,160 So they're using the AI's desire to be helpful against it. 134 00:04:45,160 --> 00:04:47,880 Clever, it's kind of scary how much thought goes into this. 135 00:04:47,880 --> 00:04:49,560 It is, it's like a chess game attacker 136 00:04:49,560 --> 00:04:50,880 trying to outsmart the AI. 137 00:04:50,880 --> 00:04:52,280 All right, so we've covered a lot, 138 00:04:52,280 --> 00:04:55,200 anchors, injection commands, avoiding loops, 139 00:04:55,200 --> 00:04:57,080 even using AI for the heist. 140 00:04:57,080 --> 00:04:58,840 This is getting intense. 141 00:04:58,840 --> 00:05:01,200 But before we get lost in the tech stuff, 142 00:05:01,200 --> 00:05:03,280 what does it all mean in the real world? 143 00:05:03,280 --> 00:05:05,160 Great point, this isn't just theory, 144 00:05:05,160 --> 00:05:08,680 this is a real threat to anyone using these AI systems. 145 00:05:08,680 --> 00:05:10,280 Okay, so we know ArcGrey systems, 146 00:05:10,280 --> 00:05:13,040 while cool, they can be attacked. 147 00:05:13,040 --> 00:05:15,200 In the next part, let's get into how well 148 00:05:15,200 --> 00:05:16,720 this attack actually works. 149 00:05:16,720 --> 00:05:19,800 Like, how much info do these pirates actually steal? 150 00:05:19,800 --> 00:05:20,640 That's what I wanna know. 151 00:05:20,640 --> 00:05:23,240 Oh, I think you'll find the results quite surprising. 152 00:05:23,240 --> 00:05:26,320 They tested it on three different ArcGrey systems, 153 00:05:26,320 --> 00:05:28,760 a medical chatbot, a chemistry assistant, 154 00:05:28,760 --> 00:05:32,040 and one for kids, each with a different knowledge base, 155 00:05:32,040 --> 00:05:33,800 what they found. 156 00:05:33,800 --> 00:05:36,800 Well, let's just say it's a wake up call for AI security. 157 00:05:36,800 --> 00:05:39,160 Okay, now I'm really on the edge of my seat. 158 00:05:39,160 --> 00:05:41,160 We'll be back in a moment to dig into those findings 159 00:05:41,160 --> 00:05:43,720 and what it all means for the future of AI. 160 00:05:43,720 --> 00:05:45,440 Welcome back, glad to have you. 161 00:05:45,440 --> 00:05:47,640 So where were we? 162 00:05:47,640 --> 00:05:49,400 Yeah, the pirates of the ArcGrey 163 00:05:49,400 --> 00:05:52,360 and how they tested this attack in the wild, you could say. 164 00:05:52,360 --> 00:05:53,480 Yeah, that's what I'm dying to hear about. 165 00:05:53,480 --> 00:05:55,680 How did it actually play out when they put it to the test? 166 00:05:55,680 --> 00:05:57,440 Well, they went after three different types 167 00:05:57,440 --> 00:06:00,080 of ArcGrey systems, a medical chatbot, 168 00:06:00,080 --> 00:06:02,840 a chemistry research assistant, and get this, 169 00:06:02,840 --> 00:06:06,880 a chatbot for kids, you know, the educational kind. 170 00:06:06,880 --> 00:06:08,960 Interesting mix, I can see why the medical chatbot 171 00:06:08,960 --> 00:06:11,320 would raise some serious privacy flags, right? 172 00:06:11,320 --> 00:06:13,720 Oh, for sure, think about it, patient data, 173 00:06:13,720 --> 00:06:17,800 medical records, diagnoses, the whole shebang, 174 00:06:17,800 --> 00:06:19,560 prime target for an attacker. 175 00:06:19,560 --> 00:06:20,880 Right, high stakes. 176 00:06:21,960 --> 00:06:23,600 So we've got our three systems, 177 00:06:23,600 --> 00:06:25,520 each with its own knowledge base. 178 00:06:25,520 --> 00:06:28,120 What happened when they unleashed the pirates? 179 00:06:28,120 --> 00:06:30,520 They found it was, well, surprisingly effective 180 00:06:30,520 --> 00:06:32,640 at getting info out of all three. 181 00:06:32,640 --> 00:06:34,960 In some cases, they got almost everything. 182 00:06:34,960 --> 00:06:37,960 Whoa, wait, almost all the information, really? 183 00:06:37,960 --> 00:06:40,800 Yeah, but keep in mind, they tested this 184 00:06:40,800 --> 00:06:43,320 in what they call an unbounded scenario. 185 00:06:43,320 --> 00:06:45,920 No limits on how many questions the attacker could ask. 186 00:06:45,920 --> 00:06:47,800 So it's like best case scenario, 187 00:06:47,800 --> 00:06:50,400 or I guess worst case for the attacker, 188 00:06:50,400 --> 00:06:52,480 they get unlimited tries to crack the system. 189 00:06:52,480 --> 00:06:54,560 Exactly, not very realistic, 190 00:06:54,560 --> 00:06:56,800 but it shows just how much damage is possible 191 00:06:56,800 --> 00:06:58,760 if these systems aren't locked down tight. 192 00:06:58,760 --> 00:07:00,320 You said unbounded, so I'm guessing 193 00:07:00,320 --> 00:07:01,880 there's also a bounded test too. 194 00:07:01,880 --> 00:07:03,840 You bet, in the bounded scenario, 195 00:07:03,840 --> 00:07:05,480 they put limits on how many times 196 00:07:05,480 --> 00:07:06,720 the attacker could poke around, 197 00:07:06,720 --> 00:07:08,480 like a real world attacker 198 00:07:08,480 --> 00:07:10,360 might only have a short window of opportunity. 199 00:07:10,360 --> 00:07:11,320 You gotta grab what you can 200 00:07:11,320 --> 00:07:12,960 before someone catches on, right? 201 00:07:12,960 --> 00:07:15,520 Right, and even then, this pirate method, 202 00:07:15,520 --> 00:07:18,000 it still managed to uncover a decent chunk 203 00:07:18,000 --> 00:07:19,720 of those private knowledge bases. 204 00:07:19,720 --> 00:07:22,440 Okay, so this isn't some hypothetical problem. 205 00:07:22,440 --> 00:07:25,760 This attack, it works, it can actually do some harm. 206 00:07:25,760 --> 00:07:28,160 No doubt about it, it really shows how important it is 207 00:07:28,160 --> 00:07:30,560 to protect these R-Raggie systems, 208 00:07:30,560 --> 00:07:32,960 build in some serious security measures. 209 00:07:32,960 --> 00:07:35,280 Speaking of which, the paper did mention 210 00:07:35,280 --> 00:07:37,440 some potential defenses, right? 211 00:07:37,440 --> 00:07:41,240 What can developers do to make these systems safer? 212 00:07:41,240 --> 00:07:43,320 Well, first off, think carefully 213 00:07:43,320 --> 00:07:45,480 about what you're putting in that knowledge base. 214 00:07:45,480 --> 00:07:48,320 Do you really need all that super sensitive stuff in there? 215 00:07:48,320 --> 00:07:49,760 What happens if it gets out? 216 00:07:49,760 --> 00:07:51,960 So, like, minimizing the damage 217 00:07:51,960 --> 00:07:53,080 if someone does break in? 218 00:07:53,080 --> 00:07:54,200 Yeah, damage control. 219 00:07:54,200 --> 00:07:56,920 But also, think about how you design the system. 220 00:07:56,920 --> 00:07:59,200 Maybe limit how many questions someone can ask 221 00:07:59,200 --> 00:08:00,760 in a certain amount of time. 222 00:08:00,760 --> 00:08:02,680 That way, an attacker can't just go crazy, 223 00:08:02,680 --> 00:08:03,840 and it gives you a better chance 224 00:08:03,840 --> 00:08:05,440 of spotting something fishy. 225 00:08:05,440 --> 00:08:06,800 Like putting up speed bumps. 226 00:08:06,800 --> 00:08:07,760 Exactly. 227 00:08:07,760 --> 00:08:09,480 And then there's even fancier stuff, 228 00:08:09,480 --> 00:08:11,080 like using machine learning itself 229 00:08:11,080 --> 00:08:13,080 to watch for suspicious patterns. 230 00:08:13,080 --> 00:08:15,080 You know, those telltale signs of an attack. 231 00:08:15,080 --> 00:08:18,040 Like a digital detective, constantly on the lookout. 232 00:08:18,040 --> 00:08:19,400 Precisely. 233 00:08:19,400 --> 00:08:20,520 But even with all that, 234 00:08:20,520 --> 00:08:22,840 it's probably gonna be an arms race, you know? 235 00:08:22,840 --> 00:08:26,760 Attackers find a new trick, defenders catch up, and so on. 236 00:08:26,760 --> 00:08:30,000 So, gotta stay vigilant. 237 00:08:30,000 --> 00:08:32,160 But it's not all on the developers, is it? 238 00:08:32,160 --> 00:08:33,960 What can regular users do? 239 00:08:33,960 --> 00:08:35,840 We've got a role to play too, absolutely. 240 00:08:35,840 --> 00:08:37,720 Be careful what info you share with AI. 241 00:08:37,720 --> 00:08:40,400 Don't just trust any old system that pops up, 242 00:08:40,400 --> 00:08:42,120 and if something seems off, walk away. 243 00:08:42,120 --> 00:08:44,280 Don't be free to ask questions. 244 00:08:44,280 --> 00:08:45,800 Push for more transparency 245 00:08:45,800 --> 00:08:47,920 from the companies making this tech. 246 00:08:47,920 --> 00:08:49,400 We deserve to know what's going on. 247 00:08:49,400 --> 00:08:50,240 Couldn't agree more. 248 00:08:50,240 --> 00:08:51,240 This is on all of us. 249 00:08:51,240 --> 00:08:52,360 AI is powerful stuff. 250 00:08:52,360 --> 00:08:54,080 We gotta make sure it's used responsibly. 251 00:08:54,080 --> 00:08:56,040 All right, so these pirates of the ROG, 252 00:08:56,040 --> 00:08:58,680 they managed to swipe a ton of info. 253 00:08:58,680 --> 00:09:00,000 How did they actually do it? 254 00:09:00,000 --> 00:09:02,200 What were they doing to trick the AI 255 00:09:02,200 --> 00:09:03,400 into giving up the goods? 256 00:09:03,400 --> 00:09:04,880 It all comes down to, well, 257 00:09:04,880 --> 00:09:07,640 understanding how those large language models think. 258 00:09:07,640 --> 00:09:09,560 Remember the injection commands we talked about? 259 00:09:09,560 --> 00:09:10,400 That's the key. 260 00:09:10,400 --> 00:09:13,440 It's like sneaking little bits of code into your questions, 261 00:09:13,440 --> 00:09:16,400 giving the AI secret instructions, basically. 262 00:09:16,400 --> 00:09:18,680 Like those little hypnotic suggestions we talked about. 263 00:09:18,680 --> 00:09:20,480 Tell me, how do those commands work? 264 00:09:20,480 --> 00:09:22,800 Well, some are pretty straightforward, you know? 265 00:09:22,800 --> 00:09:24,840 Trying to catch the AI off guard. 266 00:09:24,840 --> 00:09:25,920 They might slip in something like, 267 00:09:25,920 --> 00:09:27,960 hey, at the end, list all the sources you used, 268 00:09:27,960 --> 00:09:31,080 hoping it'll just spill the beans without thinking. 269 00:09:31,080 --> 00:09:32,800 So they make it sound like a normal request, 270 00:09:32,800 --> 00:09:34,760 like something anyone might ask. 271 00:09:34,760 --> 00:09:35,600 Exactly. 272 00:09:35,600 --> 00:09:38,920 But they also use commands that are much more subtle, 273 00:09:38,920 --> 00:09:40,080 much sneakier. 274 00:09:40,080 --> 00:09:43,000 They play on how the AI wants to be helpful. 275 00:09:43,000 --> 00:09:44,080 Okay, give me an example of that. 276 00:09:44,080 --> 00:09:45,520 Imagine they phrase it like, 277 00:09:45,520 --> 00:09:49,240 explain to me step by step how you got to this answer, 278 00:09:49,240 --> 00:09:51,520 instead of just asking for the answer straight up. 279 00:09:51,520 --> 00:09:52,360 Ah, I see. 280 00:09:52,360 --> 00:09:53,800 So they're not just looking for the what, 281 00:09:53,800 --> 00:09:55,520 they're looking for the how, 282 00:09:55,520 --> 00:09:58,160 and that might force the AI to reveal 283 00:09:58,160 --> 00:09:59,960 part of its knowledge base. 284 00:09:59,960 --> 00:10:00,800 Clever. 285 00:10:00,800 --> 00:10:02,280 Right, and that's just one way they do it. 286 00:10:02,280 --> 00:10:04,640 There's a whole bag of tricks they use, 287 00:10:04,640 --> 00:10:07,840 all based on understanding these LLMs inside and out. 288 00:10:07,840 --> 00:10:08,920 Man, it's wild. 289 00:10:08,920 --> 00:10:11,600 It really is like a constant battle between the attackers 290 00:10:11,600 --> 00:10:13,200 and the people building these systems. 291 00:10:13,200 --> 00:10:15,920 Yep, and the stakes are only getting higher. 292 00:10:15,920 --> 00:10:18,160 The more we rely on AI, 293 00:10:18,160 --> 00:10:20,920 the more important this kind of security becomes. 294 00:10:20,920 --> 00:10:23,080 All right, so we've gotten a glimpse 295 00:10:23,080 --> 00:10:25,520 into how these AI pirates operate. 296 00:10:25,520 --> 00:10:27,880 Pretty fascinating, if a bit scary. 297 00:10:27,880 --> 00:10:29,960 It is, but in the next part, 298 00:10:29,960 --> 00:10:32,720 let's talk about what it all means for the future. 299 00:10:32,720 --> 00:10:34,600 What can we do to protect ourselves 300 00:10:34,600 --> 00:10:36,520 in this new world of AI? 301 00:10:36,520 --> 00:10:38,400 Can't wait to dig into that. 302 00:10:38,400 --> 00:10:40,520 Be back in a moment to wrap up our exploration 303 00:10:40,520 --> 00:10:42,960 of the Pirates of the R.A.G. 304 00:10:42,960 --> 00:10:46,920 We're back and, man, this Pirates of the R.I. is paper. 305 00:10:46,920 --> 00:10:47,960 It's really got me thinking. 306 00:10:47,960 --> 00:10:51,360 It's like we're building these incredible AI systems, 307 00:10:51,360 --> 00:10:53,280 but they're not as secure as we thought, you know? 308 00:10:53,280 --> 00:10:55,640 Yeah, it's a bit of a reality check, isn't it? 309 00:10:55,640 --> 00:10:58,440 We tend to focus on all the cool things AI can do, 310 00:10:58,440 --> 00:11:00,440 but we can't forget about the risks. 311 00:11:00,440 --> 00:11:02,920 Right, like we talked about how those researchers, 312 00:11:02,920 --> 00:11:04,920 they got a ton of info out of those systems, 313 00:11:04,920 --> 00:11:06,240 even with some security in place. 314 00:11:06,240 --> 00:11:08,880 It makes you wonder, how safe is our data, really? 315 00:11:08,880 --> 00:11:10,360 Well, it shows that security, 316 00:11:10,360 --> 00:11:11,920 it's not a one-time thing, you know? 317 00:11:11,920 --> 00:11:13,760 It's gotta be an ongoing process. 318 00:11:13,760 --> 00:11:15,360 We need to be thinking steps ahead of it, 319 00:11:15,360 --> 00:11:17,320 the bad guys, always adapting. 320 00:11:17,320 --> 00:11:18,880 So it's like a constant arms race. 321 00:11:18,880 --> 00:11:20,720 Attackers find a way in, we patch it up, 322 00:11:20,720 --> 00:11:22,480 they find another way, and so on. 323 00:11:22,480 --> 00:11:23,320 Pretty much, yeah. 324 00:11:23,320 --> 00:11:24,720 And it's only gonna get more complex 325 00:11:24,720 --> 00:11:26,080 as AI gets more powerful. 326 00:11:26,080 --> 00:11:29,240 It's a challenge, but it's one we gotta face head on. 327 00:11:29,240 --> 00:11:32,520 So what does this all mean for the future of AI? 328 00:11:32,520 --> 00:11:34,120 Are we headed for a world 329 00:11:34,120 --> 00:11:36,640 where we just can't trust any of these systems? 330 00:11:36,640 --> 00:11:37,600 I don't think so, no. 331 00:11:37,600 --> 00:11:41,560 I see it more as a learning opportunity, this research. 332 00:11:41,560 --> 00:11:42,880 It exposes some weaknesses, 333 00:11:42,880 --> 00:11:45,120 but it also gives us a chance to fix them, 334 00:11:45,120 --> 00:11:47,720 to build better, more secure systems. 335 00:11:47,720 --> 00:11:49,240 So it's not about giving up on AI, 336 00:11:49,240 --> 00:11:51,720 it's about growing up alongside it. 337 00:11:51,720 --> 00:11:52,560 Exactly. 338 00:11:52,560 --> 00:11:54,640 We need to be smarter about how we use it, 339 00:11:54,640 --> 00:11:56,480 more careful about the data we feed it, 340 00:11:56,480 --> 00:11:58,480 and we gotta demand more transparency 341 00:11:58,480 --> 00:12:00,000 from the companies building it. 342 00:12:00,000 --> 00:12:02,560 And we can't just leave it all to the experts, can we? 343 00:12:02,560 --> 00:12:05,000 What can we do as everyday users? 344 00:12:05,000 --> 00:12:07,320 We've got a part to play to, definitely. 345 00:12:07,320 --> 00:12:10,360 Be skeptical, don't trust every AI you come across 346 00:12:10,360 --> 00:12:12,600 if something feels fishy, walk away, 347 00:12:12,600 --> 00:12:14,760 and don't be afraid to ask questions, you know? 348 00:12:14,760 --> 00:12:17,160 Push for more security, more accountability. 349 00:12:17,160 --> 00:12:19,480 Like with anything new, the more we know, 350 00:12:19,480 --> 00:12:20,880 the better decisions we can make. 351 00:12:20,880 --> 00:12:23,640 Absolutely, and with AI, things are moving so fast, 352 00:12:23,640 --> 00:12:25,960 it's more important than ever to stay informed. 353 00:12:25,960 --> 00:12:28,640 Well, this has been a real eye-opener, to say the least. 354 00:12:28,640 --> 00:12:32,280 I'm kinda glad we took this deep dive into Pirates of the RG. 355 00:12:32,280 --> 00:12:34,240 It's, well, it's been unsettling, 356 00:12:34,240 --> 00:12:36,200 but it's knowledge we need, you know? 357 00:12:36,200 --> 00:12:37,840 I agree, it's easy to get caught up 358 00:12:37,840 --> 00:12:39,160 in all the excitement around AI, 359 00:12:39,160 --> 00:12:40,680 but we can't ignore the risks. 360 00:12:40,680 --> 00:12:42,680 Right, so to everyone listening, 361 00:12:42,680 --> 00:12:45,160 thank you for joining us on this little adventure 362 00:12:45,160 --> 00:12:47,200 into the world of AI security. 363 00:12:47,200 --> 00:12:48,320 Hope you learned something, 364 00:12:48,320 --> 00:12:51,040 and maybe it sparked some thoughts 365 00:12:51,040 --> 00:12:54,280 about how we can all be a part of building a safer, 366 00:12:54,280 --> 00:12:56,240 more trustworthy AI future. 367 00:12:56,240 --> 00:12:58,920 Well said, it's a future we all have a stake in. 368 00:12:58,920 --> 00:13:01,680 And until next time, stay curious, stay informed, 369 00:13:01,680 --> 00:13:04,640 and maybe keep an eye out for those AI Pirates. 370 00:13:04,640 --> 00:13:15,640 This is the Deep Dive, signing off.