CyberWire Daily - Cops in the catfish game. [Hacking Humans Goes to the Movies]

Episode Date: November 23, 2023

Thanks for joining us again for another episode of fun project brought to you by the team of Hacking Humans, the CyberWire's social engineering podcast. Hacking Humans co-host Dave Bittner is joined by Rick Howard in this series where they view clips from their favorite movies and television shows with examples of the social engineering scams and schemes you hear Dave and co-host Joe Carrigan talk about on Hacking Humans. In this episode, Dave and Rick watch each of the selected scenes, describe the on-screen action for you, and then they deconstruct what they saw. Grab your bowl of popcorn and join us for some fantastic scams and frauds. Links to this episode's clips if you'd like to watch along: Dave's clip from the movie: Chicago P.D. Rick's clip from the movie: The Imitation Game Learn more about your ad choices. Visit megaphone.fm/adchoices

You're listening to the Cyber Wire Network, powered by N2K. book. Hello, everyone, and welcome to a special edition of the Hacking Humans podcast, an occasional series we call Hacking Humans Goes to the Movies. I'm Dave Bittner, and joining me is my Cyber Wire colleague, Rick Howard. Hey, Rick. Hey, Dave. On this show, Rick and I look at some of our favorite clips from cinema and television, clips which demonstrate some of the scams and schemes that Joe Kerrigan and I talk about on Hacking Humans. We've got some fun clips to share, so stay tuned. We'll be right back after this message from our show sponsor. Transat presents a couple trying to beat the winter blues. We could try hot yoga.

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All right, Rick. First of all, before we dig in here, I just want to say that I'm feeling fine after last show. You recovered from your fright, have you? Yeah. Well, I just want to say that I feel very fortunate that I have the presence of mind to always carry a penknife in my pocket. And also lucky that extraterrestrial cephalopods, they swallow you in one bite.

They don't chew. No teeth. No, no, no. So that bought me some time. There's like a beak kind of thing, but it just kind of, you know, in you go. And so I had a little bit of time to get the penknife out and saw my way out. Well, I am very impressed with your fortitude and the ability to use a penknife in a disastrous situation. So good on you, sir. It's dark in there.

It does not smell good either. So happy to be back healed up, well bathed, you know. So, all right. With that said, why don't we dig into our clips this week. Rick, you're going to lead things off for us here. I don't know how to follow that, but yeah, let's do that. My clip this week comes from the 2014 movie, The Imitation Game. Have you seen it, Dave? No, I'm not familiar with that one. Oh, this is one of my all-time favorites. It's directed by Morton Tildum, and he's probably most famous to our audience for the Netflix TV series, Tom Clancy's Jack Ryan.

The movie star is Benedict Cumberbatch, most famous for the excellent BBC TV series, Sherlock, and the six-year and six-movie run in the Marvel Cinematic Universe playing Doctor Strange. Yeah, that's probably where I knew him. Yeah, that's where he gets his most famed, I guess. Right. But in this scene, he's playing one of my all-time computer science heroes,

the inspirational Alan Turing. Yeah. You've heard me talk about him before, Dave. Oh, sure. He is responsible for three groundbreaking events in computer science history. So let me list them. Number one, he proved mathematically back in 1937 that a computer could actually be built up there,

up to that point, just kind of theoretical. He wrote this paper called, this is a big one. Let me see if I can say all this. Oncomputable numbers with an application to the problem. Okay. So say that three times. Yeah. This is a easy for you to say. Well, that paper is now regarded as the theoretical foundation for all modern computing. All right. So that's number one. Number two is his significant contribution to the Allied efforts in World War II in breaking the German Enigma coding machine between 1939 and 1942.

And that's what this movie is about. It's all about how they did that. And it's fantastic. Right. But number three, he defined one of the first tests for artificial intelligence called the imitation game in a paper he wrote in 1950. So in this scene from the 2014 movie, Turing describes the imitation game test. So he's talking to a police detective played by Rory Kinnear. People know him from being M's deputy in the 007 movies. And one particular episode in a Black Mirror TV show where a pig

was involved in a hostage situation. And I don't know if you're familiar with this, all right, but I'll let the audience discover what that means on their own. Okay. All right. Anything you need to do to set this clip up? No, it's just those two talking, and it is the best explanation, I think, of what artificial intelligence is. So, Mr. Kinnear talks first. All right. Could machines ever think as human beings do? Most people say no.

You're not most people. Well, the problem is you're asking a stupid question. I am? Of course machines can't think as people do. A machine is different from a person. Since they think differently. The interesting question is just because something thinks differently from you

does that mean it's not thinking we allow for humans to have such divergences from one another you like strawberries I hate ice skating you cry at sad films, I am allergic to pollen. What is the point of different tastes, different preferences, if not to say that our brains work differently, that we think differently? differently. And if we could say that about one another, then why can't we say the same

thing for brains built of copper and wire? Steel. And that's this big paper you wrote. What's it called? The Imitation Game. Right, that's what it's about.

Would you like to play? Play? It's a game. A test of sorts. For determining whether something is a machine or a human being. How do I play? Well, there's a judge and a subject. being. How do I play? Well, there's a judge and a subject. The judge asks questions and depending on the subject's answers determines who he is talking with, what he is talking

with, and all you have to do is ask me a question. What do you think, Dave? Well, I loved it. I mean, so this is the Turing test, right? It's the Turing test. It's absolutely. And it's been used by computer scientists since then to decide if a computer is able to think. Yeah. And the reason I love this clip is because, you know, with ChatGPT coming out last year, late last year, and all the large learning modules that we've been messing with since then, there have been a lot of people

claiming that we are very close to a computer passing the Turing test, right? And so, when they say that, that's what we're talking about. In fact, there was a Google engineer that got fired, okay, back in July 2022. His name was Blake Lemoine, I guess is how you say that. Yeah, yeah. But he was, yeah, he was saying out loud that basically his Lambda little chat function machine learning module passed the Turing test and he was having, you know, conversations with it every night as a pal. Okay, so that's what we're talking about.

He was making the case that it was sentient. Yeah. Okay. So that's what we're talking about. He was making the case that it was sentient. Yeah. That's what, yeah. It's basically passing that when you can't tell if the machine is a human or not, then it's basically sentient for all practical purposes, right? Now, there's a lot of computer science people that have had better definitions, better tests for artificial intelligence since then, but this was the original, right? And Turing did it by himself. It's one of the reasons I love him so much. You know, I love this too. And, you know, you and I are both old-timers here when it comes to computer stuff. Do you remember Eliza?

Of course, yes. Eliza was this fantastic thing. Describe what Eliza was, all right? Eliza was this fantastic thing. Right. Describe what Eliza was. All right. So, yeah. So, well, Eliza goes back further than my initial contact with it.

So I first came across Eliza. Someone had ported Eliza to BASIC. You know, BASIC was the computer language that came with your Apple IIs and your TRS-80s and your Commodore 64s, your VIC-20s. You know, they all came with BASIC built in. And I think ELIZA had originally been written for some of those old college mainframes and shared around the ARPANET and all that kind of thing. But basically ELIZA was someone's attempt at a computer therapist simulation.

And the magic of ELIZA was that it answered all of your questions with questions. Yeah, a little behavioral analysis trickery, okay? Right. You know, and it seemed like it was talking to you, yes. Right, and it was very compelling. You know, you would say, hi, I'm ELIZa. You know, what's your problem? And you'd say, I'm having trouble with my mother. And it would say, tell me about your mother. Say, well,

my mother, you know, never lets me borrow the cars. Does it upset you that your mother never lets you borrow the car? You know, it was that kind of thing. And it would keep going. My recollection, and it's quite possible that this is a false memory, but my recollection is that every now and then, Eliza would hit you with a zinger. Like you'd be talking with Eliza for a while, and then it would come back and say, does this have anything to do with your mother and your car? And you'd be like,

ah! You know, but it's quite possible. And there were different versions of Eliza, and people who weren't back, who didn't live through that period of time, it's hard for you to imagine how little memory and processing power we had. About a year ago, I went and I looked up the source code for ELIZA. And there's not much there.

Nothing there. You can read through the basic coding and just, you know, there's not much there. Because there wasn't room for there's not much there. Nothing there. You can read through the basic coding and just, you know, there's not much there because there wasn't room for there to be much there. But it was compelling. Well, I think the interesting thing about that is that was written in computer languages that are basically step-by-step that tried to anticipate everything you were going to say and was very good at it, right?

And that seemed like it might pass the Turing test. With these new large language models, that's not how that's being done at all. It's not trying to have an answer for everything a person might ask them. It is having a collection of data, right? And being able to understand what the human wants when they ask the question. And the computer figures it out on their own, right? And being able to understand what the human wants when they ask the question and the computer figures it out on their own, right? Which is, that's the, that's this giant leap that we've been talking about with these large language models. Yeah. And, you know,

my, I think you and I might be thinking along similar lines here in that I hear a lot of people poo-pooing things like chat GPT and saying, oh, that's not really thinking and it's not really this, that, or the other thing. And I can't help wondering if that's a distinction without a difference. I believe that too. And if you just use Turing's definition that we saw in this film clip, okay, it doesn't really matter in the long run. Okay. It doesn't matter how it's done. If the humans can't tell, right, then it's for all intents and purposes, it might as well be, right? And you can see where there are lots of applications in a very specific domain that were very close to passing the Turing test.

You know, with Alexa, close to it, with self-driving cars, very close to it. And all of these things in the next 10 years or so are all going to come together and it's going to be so much better, right? All right. So that's the positive spin on it. The negative side is we've all been using this thing, these chat GPTs for, you know, a little over a year. Yeah. And it makes a lot of mistakes, just like humans do. And so my current evaluation of those modules now are, you know, it's just a little bit bigger than, a little bit better than Wikipedia, right?

You know, so, and it does some things really well, but other things it completely misses, right? And so we're not quite there yet. No, I think if you put guardrails on it and you give it a specific task that is self-contained, it can do an extraordinarily good job. But I think in particular, if you go out and ask it for some facts about something, then you have to be really careful

because it'll make things up. Now, getting back to this thing with Turing, though, the other thing that really stimulates my imagination with this is the notion of different kinds of intelligence. And, you know, at the outset here, I was joking about, well, not maybe joking, about the cephalopod that swallowed me whole. Joking, no. Scientists are looking at things like octopus, right? Like they're looking at octopus and saying that it's a different kind of intelligence than we have.

There's absolutely intelligence there. You know, there's these stories of aquariums having an octopus that'll squirt water out of their tank to turn out a light that's annoying them. You know, things like that. They can think, they can solve puzzles, but it doesn't seem to be the same way that we do. Their nervous system, it's just distributed in a different way. Compared to us, it's a little alien. And I think that's fascinating. Well, that's what Turing said in the clip, right? Just because they don't think like us doesn't mean they're not thinking, right?

And we have, in our minds, we have these milestones for, you know, what makes things intelligence. And one of them is language and, you know, being able to solve problems. And we can see lots of evidence in that in the animal kingdom where some animal species can communicate with their own and can solve, you know, problems that maybe seem simple to us, but it's still problem solving. Yeah. I saw a story just yesterday where someone was visiting an animal rescue organization

where they bring in wildlife that's been injured. And this place had a couple of crows that had been brought in. And the crows were, and crows are one of the kinds of birds that have a certain ability to speak or to mimic, right?

And the crows were saying, caw, caw, with a human accent. Oh my. And the person who was visiting the facility asked the person running the facility, like, what's this about? Why are the crows saying caw, caw with a human accent? And the person running the facility

kind of rolled their eyes and said, they're making fun of us. That's what we sound like. And there you go. That is thinking. All right. I love that. Right.

You can make bad jokes. I mean, you're in. Okay. Yeah. Exactly. The crows are tired of us seeing crows and going, caw, caw. They're like, oh, come on.

That's not what we sound like. Get it right, crow. Yeah, exactly. All right. I am going to switch gears on us here because mine is a little bit lighter, but still something that we cover a lot on hacking humans here. And this is catfishing. So I'm using a clip from the show Chicago PD.

Are you at all familiar with this show, Rick? I was not until you showed it to me earlier today, so that's brand new for me. Yeah. So, Chicago PD is an American police procedural drama series. It's been quite successful. I think they've been renewed for their 10th, 11th, and 12th season or something like that. On the one hand, it's one of those shows that the network can kind of grind out inexpensively. The sets are built. They got everything they need. They're popular. There's plenty of places to get stories ripped from today's headlines. And that's kind of what happens here. So this clip I'm sharing today is from Chicago PD. This is season three, episode 18.

It's called Casual with a K. And this centers around two of the police officers whose names are Burgess and Roman. And as these TV shows often have, these are two impossibly good-looking police officers. I was thinking that too when I saw it. I was like, my goodness. Right. No, they are just gorgeous human beings, perfectly groomed, you know, and have a snappy repartee with each other. Yeah, they're just wonderful. So they are in the police station when the desk sergeant, who's Sergeant Platt, calls them over to deal with a man who has been a victim of catfishing.

Now, before we dig in here, Rick, you want to give us a little description of what catfishing is? Yeah, catfishing is that somebody kind of steals your image and your identity to some point and claims they're you to convince you to come see them. Okay, like in the typical example is they show a beautiful woman on some social dating app and it's an old guy in a t-shirt and can barely, it's overweight or something like that. But so then you say, yes, I want to meet this young woman and you get there, bad things can happen to you.

Yeah, yeah. And that's what this is about. So, again, this is from Chicago PD, and we start off here that we are in the police department, and these two cops are helping a man who has fallen victim to someone catfishing him. You two, over here. Okay, how can we be a service sergeant? You see that guy over there? Robbery victim. Kent

Kozar. You're gonna love him. Home phone. Can't you just call me on my cell? Okay, leave that one blank. Location of the robbery? Shangri-La Motel, but it is not what you think. Wait, let me guess. You took a hooker to a motel and she robbed you?

What? No. We matched on casual. I don't know what that means. Oh, it's a dating app. Short-term dating. Casual with a K. Keep things casual. That's what they promise.

Her profile said she was looking for something NSA. No strings attached. Yeah, I got that. Then what happened? She's giving her partner the stink eye. I'm just saying. I go over there, lights are out. Wrong. Dude comes out of nowhere,

puts a knife to my throat. Big, huge, definitely a dude. Okay, you got the hair color, the eye color. I already told you the lights were off. Okay, just show me the profile. He already erased it. It's gone. The name was Spanish. Carmela. Something like that. Look, I don't care about my wallet. I just need you to get my wedding ring back. Of course. My wife,

she had back surgery six months ago. No, no, no, no, no. We don't need that part. We'll look into this, sir. All right. Keep your phone on. That's all. I signed up for it like four months ago out of curiosity she's skeptical can I say that we think that guy Ken Kosar was targeted through a dating app you get this over to area central we just came from there detective Lopez and Lillard said add it to their pile great so add it piles is high yeah we want to try something go on the app put in a profile similar to kosar's catfish the catfisher is that a dr seuss book what no look we checked there are two other cases this month with similar complaints with m.o's using the same dating app casual with with a K. Spanish name profile gets erased.

We'll draw them out. Maybe Sean and I will get lucky. That didn't mean what it sounded like. Yeah. Okay, run with it. So next we see the two of them. Now they're no longer in their uniforms. They're sitting in a car, basically on a stakeout.

Oh, God. I mean, this is depressing and desperate. She's looking through the casual app. I know that face. How? I read Sports Illustrated. That's how. She's a swimsuit model from Brazil. Her name's Esperanza Ricapero, and she's definitely not luring guys to the Wildflower Motel in Chicago under a false name. Check this out.

What? NSA. No street. Yeah, no, What? NSA. No street... Yeah, no, I got it. Now what? Now we wait. You ever eat tickets? Yeah, for what? Now you're gonna laugh at me.

No, I won't. You won't. Fine, it's Shakespeare in the Dark. It's like a medley of his comedies. Good lord. Spoken like a are a public service. Relationships have an expiration date. Spoken like a true dude. There's an argument to be made for keeping things casual, with a K. And for your information, Burgess,

I had never hooked up on this. Wait, they hit me back. We're in business. The app says they matched with Francesca. So now they're at a seedy motel, walking along, you know, to knock on a door. Francesca? Come in.

That sounds female. Is this Francesca? Yes. Mike? Yeah, it's me. Come in. It's locked. Let me see your hands.

Shut up, bitch. Don't move. What's your name? I didn't do anything. What's your name? This is assault. I'm just minding my own business. All right, turn it over.

Come on. Give me your other hand. It's real interesting, Francesca. You didn't have time to delete the profile yet, huh? Come on, big girl. Come on, let's get up. Big girl. Because it's a big dude.

It is a big, big dude. They are handcuffed. They have handcuffed and hauled him out of the no-tell motel to never catfish again. What do you think of this, Rick? Do you know where the term catfish comes from, Dave? Do you know this? Well, I know of, I've seen people who go catfishing

where they get into a river and they catch catfish by hand. Have you seen that? I have seen people do that, yeah. And I thought that too. We do another podcast called Word Notes where we explain catfish. And the origin of the term comes from a 2010 documentary. This woman does a catfish, but she's not trying to steal money. She's just interested in romance. And so the documentary catalogs and shows how all this comes about, and then shows the inevitable betrayal when the man figures out that it's not this beautiful woman

like the Sports Illustrated model in the movie clip, but really just a 40-year-old housewife who's looking for entertainment, right? And so that's how catfish, the term, got thrown into the cybersecurity vernacular. I don't understand, though. Why catfishing? You know, he never explains why catfishing. It's alluded to what you said about, you know, going into the river and catching catfish. Right. It's like setting the hook.

Yeah. Yeah. Yeah. Yeah. Okay. All right. Yeah. All right.

Well, it's a fun clip. And, you know, it's always interesting to me to see these kinds of shows dramatize these sorts of things. And I think in this case, I think they did a pretty good job with it. The dialogue's pretty snappy for a TV show, you know, for a network TV show. We laughed how many times, right? So, yeah. I'd watch another one of those. Yeah, you kind of believe that these two police officers have a

relationship, you know, sort of that collegial, begrudging, tolerating each other relationship with just under the surface, broiling sexual tension, right? That's just the plot of most of those TV procedurals, like you said. Yeah, I think it is.

All right, well, we will have links to both of these video clips that are up on YouTube in the show notes, so you can go and check those out or watch along with us during, if you go back and listen to the show again, and we hope that you will do that. We'll be right back. be, but definitely 100% closer to getting 1% cash back with TD Direct Investing. Conditions apply. Offer ends January 31st, 2025. Visit td.com slash DI offer to learn more. We want to thank all of you for listening and also add a quick reminder that N2K,

Strategic Workforce Intelligence, optimizes the value of your biggest investment, your people. We make you smarter about your team while making your team smarter. Learn more at n2k.com. Our senior producer is Jennifer Iben. This show is edited by Elliot Peltzman. Our executive editor is Peter Kilby. I'm Dave Fittner. And I'm Rick Howard.

Thanks for listening.