September 29, 2025 • 12 mins
Neon's Data Disaster: How a Viral AI App Exposed 75,000 Users and Went Dark
In this urgent episode of Upwardly Mobile, we break down the spectacular rise and immediate fall of the highly controversial mobile application, Neon. The app, which recently topped the charts and went viral on platforms like TikTok, promised users payment in exchange for recording their phone calls. These recordings were then sold to AI companies for training. However, less than 24 hours after gaining widespread attention, a significant security flaw was discovered. According to reports from TechCrunch, this flaw allowed public access to extremely sensitive user data. The Security Catastrophe The call-recording app had rapidly climbed the App Store ranks, reporting 75,000 downloads in a single day. Despite its rapid growth, Neon was forced offline after the security issue was discovered by TechCrunch. The flaw was so severe that it allowed anyone utilizing a network analysis tool to access private information belonging to other users. Exposed data included:
In this urgent episode of Upwardly Mobile, we break down the spectacular rise and immediate fall of the highly controversial mobile application, Neon. The app, which recently topped the charts and went viral on platforms like TikTok, promised users payment in exchange for recording their phone calls. These recordings were then sold to AI companies for training. However, less than 24 hours after gaining widespread attention, a significant security flaw was discovered. According to reports from TechCrunch, this flaw allowed public access to extremely sensitive user data. The Security Catastrophe The call-recording app had rapidly climbed the App Store ranks, reporting 75,000 downloads in a single day. Despite its rapid growth, Neon was forced offline after the security issue was discovered by TechCrunch. The flaw was so severe that it allowed anyone utilizing a network analysis tool to access private information belonging to other users. Exposed data included:
- Users' phone numbers.
- Call recordings and accessible URLs to the raw audio files.
- Text transcripts of the recorded calls.
- Detailed metadata connected to the calls, including the phone number of the person called, the time and duration of the call, and the amount earned from the call.
- Excerpts from "Neon, the viral app that pays users to record calls, goes offline after exposing data | Mashable"
- Excerpts from "Viral call-recording app Neon goes dark after exposing users' phone numbers, call recordings, and transcripts | TechCrunch"
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Okay, let's get into a really interesting case today, a
spectacular security failure.
Speaker 2 (00:05):
Actually, yeah, this one involves an app that just exploded
onto the scene, viral success.
Speaker 1 (00:10):
Totally climb the app store charts, got something like seventy
five thousand downloads in just one day, credible numbers, and
then less than twenty four hours later it was pulled offline. Gone.
Speaker 2 (00:21):
Right, we're talking about the Neon app, exactly.
Speaker 1 (00:24):
And for mobile developers, security folks, anyone listening, really, this
collapse offers some well pretty crucial lessons about API security,
painful ones.
Speaker 2 (00:34):
Maybe it's a textbook example, isn't it critical server side vulnerabilities?
And crucially, they weren't hidden deep they were frankly trivially.
Speaker 1 (00:43):
Exploitable with common tools too.
Speaker 2 (00:44):
Yeah, exactly. So our aim here is to unpack the
technical flaws. Go beyond just the headlines.
Speaker 1 (00:50):
We need to look at the authentication, the authorization, the
data handling. How did this promising idea turn into such
a catastrophic leak?
Speaker 2 (00:59):
And the stakes were so high from the start, right.
Speaker 1 (01:02):
The whole model was built on handling incredibly sensitive.
Speaker 2 (01:05):
Data, verified user phone numbers, paired.
Speaker 1 (01:07):
With actual audio recordings of.
Speaker 2 (01:10):
Calls and the text transcripts too.
Speaker 1 (01:12):
So if you're building apps iOS, Android, Harmony, OS, whatever,
and you handle any sensitive user input, this is it's
a stark warning about where your security perimeter really needs
to be.
Speaker 2 (01:25):
Absolutely, So let's start with the concept itself. What was
Neon right?
Speaker 1 (01:31):
It was designed to pay users for recording their phone calls, with.
Speaker 2 (01:35):
The explicit goal of feeding that audio data and the
transcripts into AI models.
Speaker 1 (01:41):
For training testing, improving them. Basically monetizing conversations directly.
Speaker 2 (01:46):
An engine build purely on user calls.
Speaker 1 (01:48):
And the launch numbers proved people were interested. You mentioned
the seventy five k downloads. App figures confirmed it hit
the top five free iPhone apps.
Speaker 2 (01:55):
That kind of velocity is rare. But even then there
were murmurs. Weren't there warnings?
Speaker 1 (02:00):
Yeah, sources were already saying be careful, too, many unknowns
about the company, their claims about keeping data safe anonymous.
Speaker 2 (02:06):
And that caution turned out to be spot on. The
app vanished almost as soon as the exposure came to light.
Speaker 1 (02:11):
Okay, so let's get technical. The architecture failure. How did
a simple logged in user manage to bypass whatever security
Neon had because they must have had some basic authentication.
Speaker 2 (02:23):
You'd assume so, But this hits the core problem. It
was a classic insecure direct object reference IDOOR.
Speaker 1 (02:32):
Okay, explain that right.
Speaker 2 (02:33):
So the issue wasn't some super complex hack. It was
a fundamental failure and authorization on Neon's back end. The system.
It just didn't check properly. I think what didn't check
if the user making the request It's called user A
actually had permission to access the data they were asking for,
if that data belonged to say, user B.
Speaker 1 (02:55):
So the server just took the request based on some
ID number.
Speaker 2 (02:57):
Pretty much, it seems it didn't properly valid that the
identity tied to the session token, you know, the log
in proof actually match the owner of the data ID
being requested.
Speaker 1 (03:06):
And what makes idoor so dangerous here technically is the
predictability aspect. Right, It suggests they were likely using guessable.
Speaker 2 (03:12):
IDs, exactly sequential IDs maybe more something easily iterated like
user one, User two, User three in the API path
instead of proper randomize UIDs.
Speaker 1 (03:23):
Universal unique identifiers dose long random strings.
Speaker 2 (03:27):
Right, If you use predictable IDs like one, two, three,
any authenticated user can just change that number in the
API request they send.
Speaker 1 (03:35):
And try to pull someone else's data, which.
Speaker 2 (03:37):
Is precisely what seems to have happened. Ye and crucially
for our audience, how is this found?
Speaker 1 (03:42):
Not with anything exotic, as you said, it was a
standard network traffic analysis tool.
Speaker 2 (03:47):
Specifically burp suite was mentioned.
Speaker 1 (03:49):
Burp suite. Okay, that's fundamental for anyone in testing or development.
Speaker 2 (03:53):
Absolutely it shows the attack didn't need zero DA's or
complex scripts. A researcher just use burp suite to watch
the traffic flowing between the Neon app on their phone
and the back end server.
Speaker 1 (04:03):
So they watched their own apps legitimate calls first.
Speaker 2 (04:06):
Correct, they observed how the app fetched their own recent
call data, identified the API structure, likely saw those predictable IDs,
and then simply tweaked the request.
Speaker 1 (04:16):
Change the target user id in the call.
Speaker 2 (04:18):
Yeah, change the id, and sent the modified request to
the server to see what came back.
Speaker 1 (04:22):
And what came back was pretty bad. Let's talk about
that payload. This wasn't just a little metadata leak, no,
not at all.
Speaker 2 (04:29):
It was comprehensive. The manipulated API calls returned first all
the metadata, both phone numbers, on the call duration, timestamp,
even the money earned.
Speaker 1 (04:38):
Okay, it's bad enough.
Speaker 2 (04:39):
But then second, the actual text based transcripts of the
recorded calls.
Speaker 1 (04:44):
Wow, the full conversation text yes.
Speaker 2 (04:46):
And third the path to the raw audio, the actual recording.
Speaker 1 (04:50):
How was that exposed? Was it locked down somehow?
Speaker 2 (04:53):
It appears the URL for the audio file was basically
just a public web address, probably pointing to cloud storage
like an S three bucket or maybe a simple CDN unauthenticated.
Seemingly yes, the server, having already failed the authorization check
for the idoor request, just handed back this URL, and
the URL itself was the key click the link, get
the audio.
Speaker 1 (05:12):
So no secondary check, no temporary token needed for the
audio file itself.
Speaker 2 (05:17):
It doesn't look like it. Once you had that URL,
which the idoor vulnerability gave you, you could apparently just
download or stream the raw private audio.
Speaker 1 (05:27):
So the API wasn't just leaking metadata and transcripts. It
was effectively broadcasting private conversations. You could potentially see the
most recent calls from other users.
Speaker 2 (05:38):
That's what the report suggests. Yes, it highlights how one
flaw that idoor could compromise the entire data pipeline.
Speaker 1 (05:46):
End to end authorization just failed completely first, check the
user against the resource ID failed. Second, protect the actual
data file failed.
Speaker 2 (05:55):
Again, precisely a cascading failure.
Speaker 1 (05:58):
Okay, let's shift to the response. Alex Kim did take
the servers down.
Speaker 2 (06:02):
Quickly, he did, and notified users via email, but that.
Speaker 1 (06:05):
Email got criticized, didn't it for what it left out?
Speaker 2 (06:07):
Heavily criticized. While he mentioned temporarily taking the app down
to add extra layers of security, he completely omitted the core.
Speaker 1 (06:15):
Issue that phone numbers, recordings, and transcripts were exposed and
might have been accessed by others exactly.
Speaker 2 (06:22):
That lack of transparency is a huge problem. It destroys
user trust and makes a proper security response much harder.
Speaker 1 (06:29):
And when reporters asked follow up questions, it seems the
picture got even murkier regarding their preparedness.
Speaker 2 (06:35):
Yeah, key questions weren't answered immediately, like did the app
even have a security review before launch?
Speaker 1 (06:40):
A basic step you'd think you would.
Speaker 2 (06:43):
And maybe more critically, could they even tell if anyone
else found the flaw? Did they have the logs the
detailed API access logs session tracking to determine who might
have accessed what and when before the reporter found it.
Speaker 1 (06:56):
That visibility is crucial for incident response right and for
regulations absolutely fundamental.
Speaker 2 (07:01):
You need to know the scope of the breach. It
seems Neon lack that visibility.
Speaker 1 (07:06):
Which brings us back to that persistent question, how do
apps with such well frankly basic and severe back end
flaws get to the top of the app stores.
Speaker 2 (07:14):
It's a really important point. We have to remember that
the platform protections what Apple and Google check for in
their reviews mostly stop at the device itself.
Speaker 1 (07:21):
They check the client side code for malware policy violations primarily.
Speaker 2 (07:26):
Yes, they aren't and really can't be, doing deep audits
of every back end, server, logic, and API security. That
attack surface is just too vast and complex, So the app.
Speaker 1 (07:38):
Store review isn't a guarantee of server side security, not
at all.
Speaker 2 (07:42):
We've seen this repeatedly. Think about bumble Hinge exposing location
data recently, or that t dating app leaking government IDs.
Those will back end issues too.
Speaker 1 (07:51):
So the responsibility lands squarely on the developer to secure
the API. Okay, let's pivot to solutions, then actionable steps.
How do you prevent this specific ID pattern?
Speaker 2 (08:01):
Okay, first and most immediate fix, change your identifiers stop
using predictable sequential IDs for user data or resources.
Speaker 1 (08:10):
Use UIDs instead.
Speaker 2 (08:11):
Exactly use high entropy, cryptographically secure UIDs. They're just too
long and random for an attacker to guess or cycle
through in an API call.
Speaker 1 (08:19):
So if the request asks for user data X and
excess some random thirty two character string, guessing the next
user string why becomes practically impossible? Right.
Speaker 2 (08:27):
That's layer one. Layer two, which you need anyway, is
proper granular authorization, meaning you must implement specific access control
checks at every single API en point. Every time the
server has to verify does the authenticated user token actually
match the owner of the specific resource ID being requested?
Speaker 1 (08:46):
Even if the ID is a UID.
Speaker 2 (08:48):
Even if it's a UED, the cerverse still needs that check. Okay,
this session token belongs to user A, and this data
object u UID also belongs to user I access granted
we need both.
Speaker 1 (08:59):
Got it u uides make guessing harder. Authorization make sure
even a known idea can only be accessed by the
owner correct. But those fixes tackle the server logic and
the idoor itself. What about stopping tools like burf suite
from intercepting and manipulating even legitimate calls? In the first place.
Speaker 2 (09:16):
Ah okay, now you're getting into more advanced client side verification.
This is where things like runtime attestation come in.
Speaker 1 (09:21):
Runtime attestation that sounds key for preventing this kind of manipulation.
Speaker 2 (09:25):
Explain that, So, runtime attestation is basically a way for
your API server to check something crucial. It's this request
really coming from my official, unmodified app if you're running
in a secure expected environment.
Speaker 1 (09:36):
So it's not just checking if the user is logged in,
but if the app itself is legitimate and hasn't been
tampered with.
Speaker 2 (09:42):
Precisely, it verifies the integrity of the client app and
its runtime environment. It's looking for signs of rooting, jail breaking, debuggers,
attached code modification, or things like book suite acting as
a proxy.
Speaker 1 (09:56):
And if broop suite is intercepting the traffic.
Speaker 2 (09:58):
The attestation check fail, the client can't provide the necessary
proof of integrity to the server.
Speaker 1 (10:04):
So the server then refuses to return the sensitive data,
even if the attacker sends a technically valid request like
that IDO.
Speaker 2 (10:11):
That's the idea. If the attestation fails, the server knows
the request is coming from an untrusted environment, potentially manipulated
and it should block it or refuse to serve the
sensitive payload. It protects the API endpoint itself from being
exploited by these manipulated requests.
Speaker 1 (10:27):
That seems like a critical layer, especially for sensitive apps.
Speaker 2 (10:30):
It's becoming increasingly necessary. It moves beyond just trusting the
session token.
Speaker 1 (10:35):
So that really feels like the ultimate lesson here for
developers listening. Platform security is one thing, but it stops
at the device edge. Real threats today demand rigorous API
security plus that dynamic runtime client verification.
Speaker 2 (10:49):
Absolutely, the Neon case is just a stark reminder of
what happens when back end authorization fails so completely. A
viral hit turned into a public data leak almost instantly
because the API was wide open to basic manipulation. Protecting
that API interaction is.
Speaker 1 (11:06):
Just paramount, especially with data like call recordings.
Speaker 2 (11:09):
Couldn't be more sensitive, really, which.
Speaker 1 (11:11):
Leaves us with a final, maybe provocative thought for you,
the listener, to consider next time you're looking at an
app security. If an app's entire business model is built
on super fast growth and monetizing very sensitive user data,
how much real incentive is there for the developer to
invest the significant time and money required for top tier
security things like robust UIDs, granular access controls, runtime attestation
(11:35):
versus just doing the bare minimum to launch.
Speaker 2 (11:38):
That's a tough question. It's about balancing priorities, risk and costs.
Isn't it something to definitely think about when designing your
next API contract or evaluating someone else's Indeed.
Speaker 1 (11:50):
Well, we hope this analysis helps you stay informed and
ultimately stay secure in your own work.
Speaker 2 (11:55):
That was useful.
Speaker 1 (11:57):
This episode was produced using insights gathered from human sources,
and the process was assisted by artificial intelligence.
Okay, let's get into a really interesting case today, a
spectacular security failure.
Speaker 2 (00:05):
Actually, yeah, this one involves an app that just exploded
onto the scene, viral success.
Speaker 1 (00:10):
Totally climb the app store charts, got something like seventy
five thousand downloads in just one day, credible numbers, and
then less than twenty four hours later it was pulled offline. Gone.
Speaker 2 (00:21):
Right, we're talking about the Neon app, exactly.
Speaker 1 (00:24):
And for mobile developers, security folks, anyone listening, really, this
collapse offers some well pretty crucial lessons about API security,
painful ones.
Speaker 2 (00:34):
Maybe it's a textbook example, isn't it critical server side vulnerabilities?
And crucially, they weren't hidden deep they were frankly trivially.
Speaker 1 (00:43):
Exploitable with common tools too.
Speaker 2 (00:44):
Yeah, exactly. So our aim here is to unpack the
technical flaws. Go beyond just the headlines.
Speaker 1 (00:50):
We need to look at the authentication, the authorization, the
data handling. How did this promising idea turn into such
a catastrophic leak?
Speaker 2 (00:59):
And the stakes were so high from the start, right.
Speaker 1 (01:02):
The whole model was built on handling incredibly sensitive.
Speaker 2 (01:05):
Data, verified user phone numbers, paired.
Speaker 1 (01:07):
With actual audio recordings of.
Speaker 2 (01:10):
Calls and the text transcripts too.
Speaker 1 (01:12):
So if you're building apps iOS, Android, Harmony, OS, whatever,
and you handle any sensitive user input, this is it's
a stark warning about where your security perimeter really needs
to be.
Speaker 2 (01:25):
Absolutely, So let's start with the concept itself. What was
Neon right?
Speaker 1 (01:31):
It was designed to pay users for recording their phone calls, with.
Speaker 2 (01:35):
The explicit goal of feeding that audio data and the
transcripts into AI models.
Speaker 1 (01:41):
For training testing, improving them. Basically monetizing conversations directly.
Speaker 2 (01:46):
An engine build purely on user calls.
Speaker 1 (01:48):
And the launch numbers proved people were interested. You mentioned
the seventy five k downloads. App figures confirmed it hit
the top five free iPhone apps.
Speaker 2 (01:55):
That kind of velocity is rare. But even then there
were murmurs. Weren't there warnings?
Speaker 1 (02:00):
Yeah, sources were already saying be careful, too, many unknowns
about the company, their claims about keeping data safe anonymous.
Speaker 2 (02:06):
And that caution turned out to be spot on. The
app vanished almost as soon as the exposure came to light.
Speaker 1 (02:11):
Okay, so let's get technical. The architecture failure. How did
a simple logged in user manage to bypass whatever security
Neon had because they must have had some basic authentication.
Speaker 2 (02:23):
You'd assume so, But this hits the core problem. It
was a classic insecure direct object reference IDOOR.
Speaker 1 (02:32):
Okay, explain that right.
Speaker 2 (02:33):
So the issue wasn't some super complex hack. It was
a fundamental failure and authorization on Neon's back end. The system.
It just didn't check properly. I think what didn't check
if the user making the request It's called user A
actually had permission to access the data they were asking for,
if that data belonged to say, user B.
Speaker 1 (02:55):
So the server just took the request based on some
ID number.
Speaker 2 (02:57):
Pretty much, it seems it didn't properly valid that the
identity tied to the session token, you know, the log
in proof actually match the owner of the data ID
being requested.
Speaker 1 (03:06):
And what makes idoor so dangerous here technically is the
predictability aspect. Right, It suggests they were likely using guessable.
Speaker 2 (03:12):
IDs, exactly sequential IDs maybe more something easily iterated like
user one, User two, User three in the API path
instead of proper randomize UIDs.
Speaker 1 (03:23):
Universal unique identifiers dose long random strings.
Speaker 2 (03:27):
Right, If you use predictable IDs like one, two, three,
any authenticated user can just change that number in the
API request they send.
Speaker 1 (03:35):
And try to pull someone else's data, which.
Speaker 2 (03:37):
Is precisely what seems to have happened. Ye and crucially
for our audience, how is this found?
Speaker 1 (03:42):
Not with anything exotic, as you said, it was a
standard network traffic analysis tool.
Speaker 2 (03:47):
Specifically burp suite was mentioned.
Speaker 1 (03:49):
Burp suite. Okay, that's fundamental for anyone in testing or development.
Speaker 2 (03:53):
Absolutely it shows the attack didn't need zero DA's or
complex scripts. A researcher just use burp suite to watch
the traffic flowing between the Neon app on their phone
and the back end server.
Speaker 1 (04:03):
So they watched their own apps legitimate calls first.
Speaker 2 (04:06):
Correct, they observed how the app fetched their own recent
call data, identified the API structure, likely saw those predictable IDs,
and then simply tweaked the request.
Speaker 1 (04:16):
Change the target user id in the call.
Speaker 2 (04:18):
Yeah, change the id, and sent the modified request to
the server to see what came back.
Speaker 1 (04:22):
And what came back was pretty bad. Let's talk about
that payload. This wasn't just a little metadata leak, no,
not at all.
Speaker 2 (04:29):
It was comprehensive. The manipulated API calls returned first all
the metadata, both phone numbers, on the call duration, timestamp,
even the money earned.
Speaker 1 (04:38):
Okay, it's bad enough.
Speaker 2 (04:39):
But then second, the actual text based transcripts of the
recorded calls.
Speaker 1 (04:44):
Wow, the full conversation text yes.
Speaker 2 (04:46):
And third the path to the raw audio, the actual recording.
Speaker 1 (04:50):
How was that exposed? Was it locked down somehow?
Speaker 2 (04:53):
It appears the URL for the audio file was basically
just a public web address, probably pointing to cloud storage
like an S three bucket or maybe a simple CDN unauthenticated.
Seemingly yes, the server, having already failed the authorization check
for the idoor request, just handed back this URL, and
the URL itself was the key click the link, get
the audio.
Speaker 1 (05:12):
So no secondary check, no temporary token needed for the
audio file itself.
Speaker 2 (05:17):
It doesn't look like it. Once you had that URL,
which the idoor vulnerability gave you, you could apparently just
download or stream the raw private audio.
Speaker 1 (05:27):
So the API wasn't just leaking metadata and transcripts. It
was effectively broadcasting private conversations. You could potentially see the
most recent calls from other users.
Speaker 2 (05:38):
That's what the report suggests. Yes, it highlights how one
flaw that idoor could compromise the entire data pipeline.
Speaker 1 (05:46):
End to end authorization just failed completely first, check the
user against the resource ID failed. Second, protect the actual
data file failed.
Speaker 2 (05:55):
Again, precisely a cascading failure.
Speaker 1 (05:58):
Okay, let's shift to the response. Alex Kim did take
the servers down.
Speaker 2 (06:02):
Quickly, he did, and notified users via email, but that.
Speaker 1 (06:05):
Email got criticized, didn't it for what it left out?
Speaker 2 (06:07):
Heavily criticized. While he mentioned temporarily taking the app down
to add extra layers of security, he completely omitted the core.
Speaker 1 (06:15):
Issue that phone numbers, recordings, and transcripts were exposed and
might have been accessed by others exactly.
Speaker 2 (06:22):
That lack of transparency is a huge problem. It destroys
user trust and makes a proper security response much harder.
Speaker 1 (06:29):
And when reporters asked follow up questions, it seems the
picture got even murkier regarding their preparedness.
Speaker 2 (06:35):
Yeah, key questions weren't answered immediately, like did the app
even have a security review before launch?
Speaker 1 (06:40):
A basic step you'd think you would.
Speaker 2 (06:43):
And maybe more critically, could they even tell if anyone
else found the flaw? Did they have the logs the
detailed API access logs session tracking to determine who might
have accessed what and when before the reporter found it.
Speaker 1 (06:56):
That visibility is crucial for incident response right and for
regulations absolutely fundamental.
Speaker 2 (07:01):
You need to know the scope of the breach. It
seems Neon lack that visibility.
Speaker 1 (07:06):
Which brings us back to that persistent question, how do
apps with such well frankly basic and severe back end
flaws get to the top of the app stores.
Speaker 2 (07:14):
It's a really important point. We have to remember that
the platform protections what Apple and Google check for in
their reviews mostly stop at the device itself.
Speaker 1 (07:21):
They check the client side code for malware policy violations primarily.
Speaker 2 (07:26):
Yes, they aren't and really can't be, doing deep audits
of every back end, server, logic, and API security. That
attack surface is just too vast and complex, So the app.
Speaker 1 (07:38):
Store review isn't a guarantee of server side security, not
at all.
Speaker 2 (07:42):
We've seen this repeatedly. Think about bumble Hinge exposing location
data recently, or that t dating app leaking government IDs.
Those will back end issues too.
Speaker 1 (07:51):
So the responsibility lands squarely on the developer to secure
the API. Okay, let's pivot to solutions, then actionable steps.
How do you prevent this specific ID pattern?
Speaker 2 (08:01):
Okay, first and most immediate fix, change your identifiers stop
using predictable sequential IDs for user data or resources.
Speaker 1 (08:10):
Use UIDs instead.
Speaker 2 (08:11):
Exactly use high entropy, cryptographically secure UIDs. They're just too
long and random for an attacker to guess or cycle
through in an API call.
Speaker 1 (08:19):
So if the request asks for user data X and
excess some random thirty two character string, guessing the next
user string why becomes practically impossible? Right.
Speaker 2 (08:27):
That's layer one. Layer two, which you need anyway, is
proper granular authorization, meaning you must implement specific access control
checks at every single API en point. Every time the
server has to verify does the authenticated user token actually
match the owner of the specific resource ID being requested?
Speaker 1 (08:46):
Even if the ID is a UID.
Speaker 2 (08:48):
Even if it's a UED, the cerverse still needs that check. Okay,
this session token belongs to user A, and this data
object u UID also belongs to user I access granted
we need both.
Speaker 1 (08:59):
Got it u uides make guessing harder. Authorization make sure
even a known idea can only be accessed by the
owner correct. But those fixes tackle the server logic and
the idoor itself. What about stopping tools like burf suite
from intercepting and manipulating even legitimate calls? In the first place.
Speaker 2 (09:16):
Ah okay, now you're getting into more advanced client side verification.
This is where things like runtime attestation come in.
Speaker 1 (09:21):
Runtime attestation that sounds key for preventing this kind of manipulation.
Speaker 2 (09:25):
Explain that, So, runtime attestation is basically a way for
your API server to check something crucial. It's this request
really coming from my official, unmodified app if you're running
in a secure expected environment.
Speaker 1 (09:36):
So it's not just checking if the user is logged in,
but if the app itself is legitimate and hasn't been
tampered with.
Speaker 2 (09:42):
Precisely, it verifies the integrity of the client app and
its runtime environment. It's looking for signs of rooting, jail breaking, debuggers,
attached code modification, or things like book suite acting as
a proxy.
Speaker 1 (09:56):
And if broop suite is intercepting the traffic.
Speaker 2 (09:58):
The attestation check fail, the client can't provide the necessary
proof of integrity to the server.
Speaker 1 (10:04):
So the server then refuses to return the sensitive data,
even if the attacker sends a technically valid request like
that IDO.
Speaker 2 (10:11):
That's the idea. If the attestation fails, the server knows
the request is coming from an untrusted environment, potentially manipulated
and it should block it or refuse to serve the
sensitive payload. It protects the API endpoint itself from being
exploited by these manipulated requests.
Speaker 1 (10:27):
That seems like a critical layer, especially for sensitive apps.
Speaker 2 (10:30):
It's becoming increasingly necessary. It moves beyond just trusting the
session token.
Speaker 1 (10:35):
So that really feels like the ultimate lesson here for
developers listening. Platform security is one thing, but it stops
at the device edge. Real threats today demand rigorous API
security plus that dynamic runtime client verification.
Speaker 2 (10:49):
Absolutely, the Neon case is just a stark reminder of
what happens when back end authorization fails so completely. A
viral hit turned into a public data leak almost instantly
because the API was wide open to basic manipulation. Protecting
that API interaction is.
Speaker 1 (11:06):
Just paramount, especially with data like call recordings.
Speaker 2 (11:09):
Couldn't be more sensitive, really, which.
Speaker 1 (11:11):
Leaves us with a final, maybe provocative thought for you,
the listener, to consider next time you're looking at an
app security. If an app's entire business model is built
on super fast growth and monetizing very sensitive user data,
how much real incentive is there for the developer to
invest the significant time and money required for top tier
security things like robust UIDs, granular access controls, runtime attestation
(11:35):
versus just doing the bare minimum to launch.
Speaker 2 (11:38):
That's a tough question. It's about balancing priorities, risk and costs.
Isn't it something to definitely think about when designing your
next API contract or evaluating someone else's Indeed.
Speaker 1 (11:50):
Well, we hope this analysis helps you stay informed and
ultimately stay secure in your own work.
Speaker 2 (11:55):
That was useful.
Speaker 1 (11:57):
This episode was produced using insights gathered from human sources,
and the process was assisted by artificial intelligence.
Popular Podcasts
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
Las Culturistas with Matt Rogers and Bowen Yang
Ding dong! Join your culture consultants, Matt Rogers and Bowen Yang, on an unforgettable journey into the beating heart of CULTURE. Alongside sizzling special guests, they GET INTO the hottest pop-culture moments of the day and the formative cultural experiences that turned them into Culturistas. Produced by the Big Money Players Network and iHeartRadio.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com