All right, let's jump into some more AI research today.
We're gonna be looking at navigating the risks,
a survey of security, privacy, and ethics threats
in LLM-based agents.
Quite a title, but yeah, it's a really interesting look
at how AI, specifically those powerful AI systems,
could potentially cause some problems
as they become more common.
Right, and I think what's interesting about this paper
is that it kind of goes beyond just like saying,
oh, AI can mess up sometimes.
It's really trying to break down
how we can categorize those potential problems
based on where they come from.
Yeah, exactly, it's not just, you know,
AI made a mistake, it's like,
is the issue the info it's getting?
Is it the AI model itself,
or is it some crazy combination of both?
Yeah, that makes a lot of sense.
It's like a doctor trying to figure out
what's wrong with you.
Right.
They're just treat the symptoms.
They wanna figure out the root cause
so they can give you the right treatment.
Absolutely, finding the why behind a problem
is just as important as knowing what the problem is.
And, you know, in this case,
the paper's really focused on these LLM-based agents,
which are kind of like chatbots on steroids,
I guess you could say.
So more than just your typical chatbot,
they could just, you know, have a basic conversation.
Way more.
We're talking about AI systems
that can actually do things like complex tasks,
make decisions, even interact with the real world.
Whoa, so like imagine a chatbot
that can not only book your flight,
but also figure out how you're getting to the airport
and check you in for your flight, that kind of thing.
Exactly.
We're talking about some serious potential here,
but of course with great power comes, well, you know,
the rest.
The potential for things to go very wrong.
Yeah, that's a big part of what this paper is exploring.
It's highlighting how all these features
that make LLM-based agents so powerful,
well, they also open up these new ways
for them to be attacked or manipulated.
Like think about their ability
to process all kinds of input, not just text.
Right, like multimodal input,
that they can handle text, images,
maybe even audio all at the same time, right?
Exactly, that makes them super versatile,
but it also means there are more ways for them to be tricked.
Think of an AI that's supposed to help you navigate a city.
It's using street signs, maps, even traffic updates,
but then an attacker can mess with any of that
to send the AI and you in the wrong direction.
Like those optical illusion tricks,
but instead of tricking our eyes,
they're tricking the AI's brain.
Exactly.
And it's not just about the type of input either.
Think about how these agents can have these
back and forth conversations,
like with multiple rounds of interaction.
Okay, that's cool.
So they're not just responding to one prompt,
they can ask questions, get clarification,
kind of learn as they go.
Right, but think about that from like a security standpoint.
A skilled attacker could actually use that multi-round thing
to manipulate the AI, kind of like a puppet master
slowly pulling strings to get it to do what they want.
Hold on, so you're saying someone could hijack
the AI's goals without even like hacking
into the system directly, that's kind of scary.
It is, and the paper digs deep
into how that could actually happen.
And this is where it gets really concerning
because imagine if an AI had access to external tools,
things that can do stuff in the real world.
If the AI's goal gets hijacked,
those actions could be harmful.
So we're not just talking about the AI
giving you wrong information,
we're talking about it potentially doing things.
Like making purchases you didn't authorize,
controlling your smart home devices,
maybe even spreading false information online.
This is making AI assistance seem
a little less appealing all of a sudden.
I get it, it's definitely something to consider.
That's why understanding these risks early on
is so important before these super powerful
AI systems are everywhere.
So we've talked about multimodal input
and multi-round interaction,
creating potential vulnerabilities.
Are there other features of LLM-based agents
that make them both powerful but also kind of risky?
Oh yeah, definitely.
Another one is their ability to use,
well, what are basically memory mechanisms?
So like a human memory,
they can store information and retrieve it later.
Exactly, that's how they keep track of context
and learn from what they've done in the past,
makes their interactions way more complex and natural.
But, you know, that memory thing also opens up some,
well, privacy risks.
I could see that.
Like what if an attacker could get into the AI's memory
and pull out sensitive info it had picked up along the way?
Yeah, like someone having access
to every conversation you've ever had
and being able to use that against you, not good.
And then on top of that, these agents can invoke tools.
Basically that means they can use external software
or APIs to do things in the real world.
Booking flights, controlling robots, even writing code.
Whoa, that's given the AI some serious power.
It is incredibly powerful.
But also you have to remember any weakness in those tools
becomes a weakness for the AI too.
So you could have an AI that can control your smart home,
but if there's a vulnerability in how it unlocks your doors,
that could be a huge security risk.
Definitely not a good situation.
Yeah.
So it seems like every strength of these agents
has this flip side where it could also be a weakness, huh?
Yeah, that's a great way to put it.
And that's really why this paper is so important.
It's basically a call to action for everyone working on AI
to think critically about these risks
and figure out how to stop them
before these agents become a bigger part of our lives.
Sounds like a race against time almost.
In a way, yeah.
But instead of focusing on the doom and gloom,
maybe we should dive into the specifics of what
could actually go wrong.
The paper breaks down these risks
into three main categories.
And I think that'll give us a much clearer idea
of what we're dealing with.
OK, sounds like a plan.
Let's start with that first category, then risks
that come from problematic inputs.
What kind of threats fall under that umbrella?
This is where things get really interesting.
So think about it.
Even the smartest AI is only as good as the information
it's working with, right?
But in the real world, that info
can be manipulated or messed up or just plain wrong.
Right, so even if the AI itself is perfectly fine,
it can still get tripped up by bad information.
Exactly.
And there are a few ways that can happen.
But one of the most, well, I guess,
both fascinating and kind of scary
is this idea of adversarial examples.
Adversarial examples.
That sounds ominous, tell me more.
Remember those optical illusions we talked about?
It's kind of like that.
But for AI, you can actually make these tiny little changes
to the input.
Could be text, images, audio, whatever.
And a human might not even notice the difference,
but it can totally screw up how the AI understands things.
So you're saying someone could manipulate the data
to trick the AI.
But if a human looked at it, they'd be like, what's wrong?
This looks fine.
Yep, exactly.
Think about a self-driving car that's
using AI to read road signs.
Someone could change a stop sign in a way
that's invisible to us, but makes the AI think
it's a speed limit sign.
Oh, wow.
That's actually kind of terrifying.
It's like hacking, but on a whole other level.
It's exploiting how the AI sees the world, not just the code.
Yeah, and this isn't just theoretical stuff either.
The paper talks about some real world examples
where this has happened.
They were able to trick a city navigation AI
by messing with an image of a landmark.
It ended up taking a wrong turn because of it.
Yikes.
So it's like creating a custom-made illusion
to fool a specific AI.
Are there other ways attackers could
use these problematic inputs to mess things up?
Definitely.
We've already talked about goal hijacking,
which is basically manipulating the AI
into doing something it wasn't supposed to.
Super dangerous if it can take actions in the real world.
But there's also this thing called model extraction.
Model extraction.
So that's like stealing the AI's brain or something.
Pretty much.
It's when someone tries to copy what the AI can do
without actually having the code or the training data,
like trying to figure out how a super complex machine works
just by looking at what it does.
I'd imagine that's got to be really tough to do,
especially with these LLM-based agents being so complex.
It is, for sure.
But it's not impossible.
The paper talks about how attackers
could use all sorts of techniques
to basically probe the AI, give it different inputs,
see how it reacts, and try to piece together
how it works on the inside.
So it's like a high stakes game of 20 questions,
trying to figure out the secret recipe of the AI.
Huh, that's a good way to put it.
And yeah, the stakes are high, because if someone
can pull off model extraction, they
could make their own version of the AI,
and who knows what they'd do with it,
maybe even get rid of all the safety features.
Kind of like if someone stole the secret formula for Coca-Cola
and started selling their own version with, like,
who knows what kind of weird ingredients in it.
Exactly.
That's why having strong security measures is so important.
Gotta make sure those valuable AIs don't get stolen or poppied.
Oh, and speaking of things getting stolen,
there's also this thing called prompt leakage.
Prompt leakage.
I don't think we've talked about that one yet.
Right.
So think about the prompts we give to AI.
It's like giving them instructions, right?
Well, prompt leakage is when those instructions accidentally
get revealed, either through what the AI says back to you
or through some other security flaw.
So it's like someone figuring out the secret code words
to control the AI.
Yep.
That's one way to think about it.
And it's a problem because if you know the right prompts,
you can basically make the AI do whatever you want,
especially if it can access sensitive information
or take actions in the real world.
Like we gotta be super careful about how we're talking
to these AIs, making sure we don't accidentally give away
the keys to the kingdom, so to speak.
Absolutely.
And finally, we've got this thing called jail breaking,
which is all about breaking through the AI safety rules
and making it do bad stuff.
Jail breaking.
I guess that makes sense.
It's like you're freeing the AI from its cage, huh?
Yeah, kind of.
Like imagine a chatbot that's designed
to be super friendly and helpful,
but then someone figures out how to jail break it
and it starts spouting hate speech or spreading fake news.
Not good.
OK, that's definitely not what we want.
It seems like every time we make these AIs more powerful,
we also create more ways for them to go wrong.
It's like this endless game of cat and mouse
between the people making AIs and the people trying
to exploit them.
That's a perfect way to put it.
It's a constant back and forth.
And that actually brings us to the second big category
of risks, which is when the problem isn't the input
the AI is getting, but the AI model itself.
So now we're talking about issues with the AI's brain.
Even if it's getting perfect information,
what kinds of things can go wrong there?
Well, this is where we get into some of those weird quirks
that AIs can have.
How sometimes they just do unexpected things.
One of the most well-known examples of this
is something called hallucination.
Hallucination?
Like seeing things that aren't there.
How can an AI hallucinate?
Well, not literally seeing things.
It's more like when the AI makes stuff up.
It says things that are just plain wrong,
but it says them with total confidence as if they were facts.
So it's like the AI is just confidently spouting nonsense?
That sounds almost funny, but I guess
it could also be a big problem depending on the situation.
Oh, yeah, for sure.
Think about an AI that's supposed
to be helping doctors diagnose patients,
but it starts hallucinating symptoms,
could lead to the wrong treatment, maybe even hurt someone,
or what about a financial AI that's
giving you investment advice based on made-up market data?
OK, yeah, those are not good scenarios.
So what causes these AI hallucinations in the first place?
It's kind of complicated, but it could be all sorts of things.
Maybe there were biases in the data it was trained on.
Maybe there are gaps in its knowledge,
or maybe it's just a fundamental limitation
of how these models actually learn.
It seems like we're still figuring out
how these complex AIs really work, huh?
Are there any other model flaws we should be watching out for?
Yeah, another important one is this idea of memorization
versus generalization.
Ideally, we want AIs to actually learn from their experiences
and be able to apply that knowledge to new situations,
but sometimes they just end up memorizing patterns
without really understanding what's going on.
So it's like the difference between a student who can actually
use what they learn to solve new problems versus a student who
just memorizes facts for a test but can't apply any of it
in the real world.
Exactly.
And that can be a problem for LLM-based agents
because they might get stuck doing the same things over and over
again, even when it doesn't make sense anymore.
Sounds frustrating, but is that actually dangerous?
Could be, yeah.
Think about a self-driving car that's
memorized a specific route, but then there's road construction.
If it can't adapt to the new situation,
it might make a really bad decision.
OK, yeah.
I definitely see the problem there.
We need AI that can think on its feet, not just follow
memorized instructions blindly.
Exactly.
And that's one of the big challenges right now.
Building AI that can truly learn and adapt, not just pair it
back what it's seen before.
And speaking of things that AI can sometimes pair it back,
there's one more big model flaw we should talk about, bias.
Ah, bias.
That's a big topic these days, not just with AI,
but everywhere, really.
How does bias show up in these LLM-based agents?
It's kind of like with humans, you know?
AI can pick up biases from the data it's trained on.
So if that data reflects existing societal biases,
the AI can end up perpetuating those biases
in what it does and says.
So it's like the AI is inheriting all the prejudices
and stereotypes that are already out there.
Right.
And this can be a huge problem, especially
as these LLM-based agents start playing bigger roles
in our lives, like making decisions that actually
affect people.
Like imagine an AI that's being used to help companies
hire people, but it unknowingly discriminates
against certain candidates because of biases in the data
it learned from.
Exactly.
Or what about an AI that's supposed to be helping
with criminal justice, but it ends up
recommending harsher sentences for people
from certain backgrounds because of those same biases?
It's a really complex and sensitive issue
that we definitely need to figure out how to solve.
Yeah, this is getting into the whole ethics of AI, isn't it?
It's not just about building smart systems.
It's about making sure they're actually used fairly
and responsibly.
Absolutely.
And that kind of leads us into the last big category of risks
that the paper talks about, those that happen because
of the way the AI model interacts with the input it gets.
So it's not just about the input being bad
or the model being flawed.
It's about what happens when those two things come together.
OK, so this is where things get even more tangled, right?
We're talking about the intersection of those first two
categories.
What kind of threats emerge in this weird overlapping zone?
Well, one that's especially concerning
is something called a backdoor attack.
Backdoor attack.
That sounds kind of sneaky.
What's that all about?
It's like, imagine you've got this AI agent, right?
And it seems totally normal.
Does this job just fine?
But someone snuck in this little hidden vulnerability,
like a secret backdoor in a building that
lets someone bypass all the security.
So it's kind of like a time bomb waiting to go off.
But how would someone even do that with AI?
Like plan a backdoor?
I mean.
It's a bit technical, but basically they
can mess with either the training data or the model
itself and create this hidden trigger.
So the AI works fine normally, but if it encounters
a specific thing, could be a word, an image, whatever, boom,
it executes this malicious code.
Whoa, so like an example to help me wrap my head around this.
OK, so picture this.
You have a smart home chatbot.
It controls all your lights, locks, everything.
But it's been backdoored.
And let's say the trigger phrase is,
it's time to water the plants.
Totally normal phrase, right?
But instead of turning on the sprinklers,
the AI unlocks all your doors.
OK, yeah, that's straight out of a nightmare.
And you wouldn't even know until it was too late.
Exactly.
It's why we need really, really good security testing
for these AI systems, especially ones that
can do stuff in the real world.
Like imagine if someone backdoored a self-driving car AI.
We can't have those kinds of risks?
Absolutely not.
It's like the stakes keep getting higher as these AI agents
become more powerful, especially as they get integrated
into our daily lives.
So what else should we be worried about
when it comes to this interplay between inputs
and the AI models themselves?
Well, there's the whole issue of privacy leakage, which
we touched on before.
You know, how even an AI designed to be private
can accidentally leak sensitive information
through its outputs.
Right, it's not just about protecting the data going
into the AI, but also controlling what comes out.
Yeah, because even if the AI is not
trying to expose private stuff, it
can happen just because of the way it processes information.
Like imagine an AI that's learned
from tons of medical records.
It might accidentally reveal a patient's info
in a response to a totally unrelated question.
It's like the AI is putting two and two together in a way
that reveals something private, even if it's not intentional.
Exactly.
It's tricky stuff.
We need to be developing better ways
to make sure AI's keep that kind of information locked down
tight no matter what.
OK, so we've gone through those three main risk categories.
Problems with the input, problems with the AI model itself,
and then the weird stuff that happens when those two things
combine.
It's a lot to think about.
But I guess that's why we're doing this deep dive, right?
We need to understand this stuff if we're
going to use AI safely.
Couldn't have felt it better myself.
And you know what really brings it all home?
Those real world case studies the paper looks at,
they show just how real these risks are.
You're right.
Let's get into those.
First up, we've got WebGPT, which is an AI agent that
uses a web browser to answer your questions.
WebGPT, yeah.
It's a really cool example of how AI can use tools,
like an internet browser, to do more.
It's like having your own personal AI researcher.
But of course, using the internet also opens up some,
well, let's say, opportunities for things to go wrong.
Right, because the internet is full of information,
but it's also full of bias, inaccuracies,
and just plain old bad stuff.
Right, exactly.
And the paper points out that WebGPT is especially vulnerable
to goal hijacking and those backdoor attacks
we were talking about, because it relies so much
on external data.
So if that data is compromised, the AI is compromised too.
It's like giving an AI access to the internet.
It's kind of like giving a kid the keys to a candy store.
Sure, there's awesome stuff in there,
but also a lot of potential for things to go wrong.
Perfect analogy.
We need to be really thoughtful about how
we design these AI so they can use powerful tools safely.
OK, next case study, we've got Voyager, the AI that plays
Minecraft.
Yeah, Voyager.
This one's super interesting because it's
an example of an embodied AI, meaning it can actually
learn and interact with a simulated environment.
It's like teaching an AI to play a video game.
And Minecraft is a perfect environment for that,
because it's so complex and open-ended.
But Voyager also shows us how even when an AI is super smart,
if it doesn't have enough knowledge about a specific domain,
it can make mistakes.
And those mistakes can be amplified when the AI is actually
doing things, not just answering questions.
Because in Minecraft, Voyager is not just
thinking about what to do.
It's actually writing code to control its actions
in the game, right?
Yep.
So even small errors can have big consequences.
Plus, the paper points out that Voyager's design makes it
a bit easier for attackers to do that model extraction thing
we talked about.
So there are definitely security risks there too.
OK, on to case study number three.
PPP, which is an AI for navigating cities.
PPP is all about multimodal AI.
It uses text and images to find its way around,
kind of like a super-powered GPS.
It's awesome because it can understand street signs,
recognize landmarks, all that stuff.
But again, when you're dealing with the real world,
there are always more risks.
Right.
Like what if someone mess with the street sign
to trick the AI into sending you the wrong way?
Exactly.
The paper talks about how PPP is really vulnerable
to those adversarial examples, because attackers
can manipulate both the text and images it uses.
And when you're navigating a city,
taking a wrong turn could be a lot more serious
than in a video game.
Yeah, that's for sure.
OK, last case study.
We've got Chat Dev, which is a system
where multiple AIs work together to write software code.
This one's wild.
It's like having a whole team of AI programmers.
Super efficient.
But also, think about it.
If one of those AIs messes up, it
could have a ripple effect on the whole project.
It's like that game of telephone
where the message gets all jumbled up as it's passed around.
Except in this case, instead of a silly message,
you could end up with buggy or even dangerous code.
Yeah, good analogy.
It highlights why we need ways to catch and fix errors
in these multi-agent systems, especially when they're
doing something as important as writing software.
So I think we've covered a lot of ground today.
It's both exciting and a little bit scary
to see how fast AI is progressing.
It is.
But that's why these deep dyes are so important, right?
Yeah, you can't just blindly embrace new technology.
We have to be aware of the potential downsides, too.
Couldn't agree more.
This paper is a real wake-up call.
It's like, hey, AI is amazing, but we need to be careful.
We can't just assume it's always going to be used for good.
We need to think about the risks, build in strong security,
and make sure we're using AI ethically.
Well said.
So what's the one big takeaway you hope our listeners
get from today's episode?
I think it's this.
AI is changing the world in awesome ways, but it's not
perfect.
LLM-based agents especially, they have incredible potential,
but also some serious risks.
We all need to be aware of those risks
and be part of the conversation about how
to make sure AI is used for good.
Totally agree.
And as AI keeps evolving, those risks
are going to change, too.
So we've got to stay informed.
Well, this has been a fascinating deep dive.
Thanks for joining me today.
My pleasure.
And thanks to all our listeners out there.
Keep those brains curious.
Until next time.