A recent story of a writer who was mistakenly identified, tracked, and arrested using data from Flock cameras has gone viral.
The New Jersey plates that were allegedly stolen from the LA dealer were 34 03 DTM, not 34 10 DTM. But when the police report was created and the plate was entered into Flock’s system, it was just recorded as 34 DTM. Just the five large characters, no little number in the middle. And Flock’s AI tech wasn’t registering that non-standard little number when it began picking up the Range Rover around town. It just saw 34 DTM in large type and started alerting the local police.
As we all stood there shaking our heads, including my wife, who was finally allowed to join me, I connected the final dot. A lot of vehicles in JLR’s media fleet have a New Jersey manufacturer plate with the same alphanumeric structure34 ## DTMand Officer Ganshyn observed that meant it was now a nationwide issue. Anywhere a police department has a partnership with Flock, any other JLR-owned car with the same plate structure is going to get flagged as stolen. In fact, four other 34 ## DTM cars were being tracked around Minnesota that week, according to Officer Ganshyn. I was just the first one to get nabbed. The only way to stop it would be for the LAPD to correct their initial report and update Flock’s system, which Jaguar Land Rover was now racing to make happen following the phone call.
Flock has responded to the bad press. First, they affirmed that their systems were working correctly, and blamed the police:
The obvious question was that Flock cameras were looking for 34 DTM, and the plate on the car I was driving was 34 10 DTM. Why was that flagged as a match?
“The way that the ML [machine learning] works is it correctly read what it was supposed to read. It was fed those characters that you said, 34 DTM, and it spit back out [a result] with the characters, 34 DTM,” Thomas said. “It was asked, can you find this? And it did find that. It just didn’t say if there’s more here, then don’t do it. It just simply said, is it there? And the answer was yes.”
He explained that even if the 10 was normal size, Flock would still have flagged it as a match, because that’s how they’ve set it up according to law enforcement’s requests. Sometimes partial plates are all they have to go on at first.
“The way that law enforcement likes to use these tools is, if any of the characters that they have put into these hot lists get read, they want to get those alerts,” he said. “Now, what we try to train officers to do is to do what you said, which is to verify that 34 DTM is what I’m looking for, and what I’m seeing is 34 10 DTM.”
Second, Flock’s CEO has apologized for calling privacy advocates terrorists:
The CEO of Flock Safety, the company that runs an enormous network of cameras used by police departments across the U.S., hasn’t been shy about taking on Flock’s critics. Last year, he even called one group that tracks the location of Flock cameras “terrorists.” But he’s had a change of heart. Or, at the very least, a change in PR strategy.
Meanwhile, the police are using (alternate source) the Flock camera network to track people in addition to cars:
Police departments around the country have used Flock cameras at least hundreds of times to search for specific people, not cars, using searches such as “heavy-set male with a black and white hat,” “person on skateboard,” and “person wearing orange vest and construction hat,” according to data reviewed by 404 Media. Sometimes searches reference a target’s race or signs of their political affiliation.
And, like all police surveillance technologies, there are abuses.
The surveillance company Leonardo wants more data:
A surveillance company plans to add sensors to automatic license plate readers (ALPRs) that would mean the devices, as well as capture the license plate of passing vehicles, would also sweep up unique identifiers of mobile phones, wearables, and other Bluetooth-enabled devices in those cars, potentially letting law enforcement identify specific drivers or passengers.
The technology, called SignalTrace, would turn ALPR cameras from devices focused on tracking cars to ones that can more readily track the location of particular people. ALPR cameras have become a commonly deployed technology all across the U.S.; SignalTrace would make some of those cameras capable of collecting much more data.
Yes, it’s bad that more companies are collecting this level of surveillance data. But all of this pales in comparison to the type and quantity of data our smartphones already collect about us.
Dutch journalist Just Vervaart, working for regional media network Omroep Gelderland, followed the directions posted on the Dutch government website and mailed a postcard with a hidden tracker inside. Because of this, they were able to track the ship for about a day, watching it sail from Heraklion, Crete, before it turned towards Cyprus. While it only showed the location of that one vessel, knowing that it was part of a carrier strike group sailing in the Mediterranean could potentially put the entire fleet at risk.
[…]
Navy officials reported that the tracker was discovered within 24 hours of the ship’s arrival, during mail sorting, and was eventually disabled. Because of this incident, the Dutch authorities now ban electronic greeting cards, which, unlike packages, weren’t x-rayed before being brought on the ship.
Unlike many of Flock’s cameras, which are designed to capture license plates as people drive by, Flock’s Condor cameras are pan-tilt-zoom (PTZ) cameras designed to record and track people, not vehicles. Condor cameras can be set to automatically zoom in on people’s faces as they walk through a parking lot, down a public street, or play on a playground, or they can be controlled manually, according to marketing material on Flock’s website. We watched Condor cameras zoom in on a woman walking her dog on a bike path in suburban Atlanta; a camera followed a man walking through a Macy’s parking lot in Bakersfield; surveil children swinging on a swingset at a playground; and film high-res video of people sitting at a stoplight in traffic. In one case, we were able to watch a man rollerblade down Brookhaven, Georgia’s Peachtree Creek Greenway bike path. The Flock camera zoomed in on him and tracked him as he rolled past. Minutes later, he showed up on another exposed camera livestream further down the bike path. The camera’s resolution was good enough that we were able to see that, when he stopped beneath one of the cameras, he was watching rollerblading videos on his phone.
Troubleshooting critical issues by deciphering a user’s journey on the Grab app is an extremely challenging task. With countless user journeys and multiple paths through the User Interface (UI), it’s akin to searching for a needle in a vast haystack. This challenge frequently resonates with us, the dedicated developers at Grab, as we strive to understand user behaviors, views, and interactions.
The challenge
The distinction between resolving an issue effectively versus spending hours on a wild goose chase is understanding our user journey in real-time.
The development team initially attempted to address the issue of the incomplete user journey tracking by implementing a system where a click stream event would be sent with every user interaction. However, this approach presented significant challenges due to the sheer volume of UI components—often numbering in the hundreds—and the reliance on individual developers to correctly instrument each one.
A common pitfall was that developers would occasionally overlook or forget to instrument certain user interactions, leading to breaks in the recorded user journey. This created a highly frustrating situation for both the development and product teams, as the integrity of the user journey data was consistently compromised. Despite continuous efforts to patch these bugs and address the omissions, the team found themselves in a perpetual state of reaction, constantly trying to catch up with newly discovered breaches rather than proactively preventing them. This reactive approach consumed valuable resources and hindered the ability to gain a complete and accurate understanding of user behavior.
Diagnosing system failures, application bugs, or poor user experiences in complex applications becomes inefficient without real-time performance metrics and detailed session tracking. When engineering teams rely on outdated or fragmented data, they are forced to piece together issue narratives reactively, long after the issues occur. This significantly delays the Mean Time To Resolution (MTTR). Such a reactive approach leads to increased downtime, higher operational costs, customer dissatisfaction, and a waste of developers’ time, as they spend more time “hunting” for clues rather than deploying solutions or new features.
Our ‘Eureka’ moment: AutoTrack SDK
The pivotal breakthrough that provides our unique advantage was the creation of auto tracking user journeys—our “Eureka” moment. To deliver this, we developed the new Software Development Kit (SDK) called AutoTrack.
AutoTrack is system that comprehensively records application state, UI view state, as well as user interactions – a solution that pieces together a chronicle of the user journey, from launch to interactions, as they navigate through the screens. AutoTrack SDK is built on the three core pillars:
Application state
User interactions
UI screens
Let’s delve deeper into the mechanics of how this operates.
Application state
Understanding the application state is fundamental to comprehending user behavior and, consequently, executing effective troubleshooting. The application state provides crucial insights into how a user interacts with the app, particularly concerning its visibility and how it was initiated. This encompasses tracking when the app moves between the background and foreground, as well as the various launch mechanisms.
Figure 1. Application state user flow.
Key aspects of application state that are vital to monitor include: Application lifecycle transitions:
Background state: When the app is running but not actively displayed to the user (e.g., the user switches to another app, or the device is locked). Understanding how frequently and for how long an app resides in the background can inform power consumption analysis and the effectiveness of background tasks.
Foreground state: When the app is actively in use and displayed to the user. Monitoring transitions into and out of the foreground provides a real-time view of user engagement.
Inactive state: A temporary state where the app is in the foreground but not receiving events (e.g., an incoming call temporarily interrupts the app).
Suspended state: An app that is in the background and has been explicitly suspended by the operating system to free up resources.
Terminated state: When the app has been completely closed or crashed. Differentiating between intentional termination and crashes is critical for identifying stability issues.
Application launch mechanisms:
The way an app is launched significantly impacts the initial user experience and can influence subsequent interactions. Tracking these different launch types is essential for understanding user entry points and for debugging issues that might be specific to a particular launch method.
Explicit user launch: This is the most straightforward launch mechanism, where the user directly taps on the app icon from their device’s home screen or app drawer. This indicates a deliberate intent to use the app and often signifies a primary entry point for regular users.
Deeplinks: Deeplinks are URLs that, when clicked, open a specific page or section within a mobile app rather than a web page. They are powerful tools for enhancing user experience and engagement by providing direct access to relevant content.
Push notifications: Push notifications are messages sent by an app to a user’s device even when the app is not actively in use. Tapping on a push notification often launches the app and directs the user to a specific context related to the notification’s content.
Figure 2. Code sample for tracking application lifecycle transition.
User interactions
Real-time session tracking is a crucial component in understanding user behavior and optimizing app performance. By meticulously tracking a wide array of user interactions, the system provides invaluable insights into how users navigate and engage with the app. This granular data forms the bedrock for constructing comprehensive user journeys, allowing development teams to visualise the path a user takes from their initial entry point to achieving their goals within the app.
This deep understanding of user interactions is the most important pillar in creating accurate and insightful user journey maps. These maps, in turn, are instrumental in identifying patterns of user behavior, both positive and negative. For instance, tracking helps to identify pain points, bugs, or areas of confusion that might lead to user frustration or abandonment.
Figure 3. Sample code for real-time session tracking.
UI screen
The system leverages lifecycle events from UIViewController (iOS), Activity (Android), and Fragments (Android) to accurately identify and track which specific screen is currently displayed to the user. This granular level of screen tracking is crucial because it significantly enriches the contextual information available to us. By understanding the precise UI that users are interacting with, we can account for the dynamic nature of our app. Different geographical regions, diverse user segments, and varying operational scenarios can lead to distinct user interfaces being presented. This capability ensures that our analysis and troubleshooting efforts are always based on the actual user experience, allowing for more precise problem identification and more effective solutions.
Figure 6. Sample code of UIViewController configuration.
UI screen data
On top of that, whenever the screen appears, we capture the screen metadata where we read the full screen hierarchy. With the Screen hierarchy JSON data at hand, we employ it to train an AI model. This model, consequently, can generate an HTML file, which mirrors the user’s screen and interaction.
Disclaimer: information is redacted in compliance with GDPR/PDPA, personal data protection laws.
Figure 7. Screen hierarchy.
Applications of AutoTrack
Key applications of AutoTrack data:
Reconstructing user journeys and reproducing elusive bugs: One of the most significant benefits of AutoTrack is its ability to meticulously record user interactions within the app. This detailed session data allows our teams to precisely recreate the user journey that led to a reported issue. For bugs that are notoriously difficult to reproduce, this capability is a game-changer, eliminating hours of manual guesswork and dramatically accelerating the identification and resolution of underlying problems.
Automated issue assignment: When an issue is reported, AutoTrack data can be leveraged to automatically assign it to the most relevant team. By analysing the context of the issue within the recorded session, including the specific features or modules involved, the system can intelligently route the problem to the engineers best equipped to address it. This automation reduces triage time, ensures issues are handled by subject matter experts, and improves overall response efficiency.
Automating UI test case generation: The rich dataset provided by AutoTrack offers a powerful foundation for automating the creation of UI test cases. By observing how users interact with the interface, we can automatically generate test scripts that mimic real-world usage patterns. This not only speeds up the testing phase but also leads to more comprehensive test coverage, identifying edge cases and user flows that might otherwise be missed by manually written tests.
Understanding analytics event triggers: AutoTrack data provides a granular view into when and why specific analytics events are triggered within the application. This allows us to validate the accuracy of our analytics instrumentation, ensure that events are firing as expected, and gain deeper insights into user behavior. By understanding the precise context surrounding event triggers, we can refine our data collection strategies and derive more meaningful insights from our analytics.
Key takeaways and what’s next
AutoTrack replaces fragile manual instrumentation with a unified, real-time view of application state, screen context, and user interactions. That end-to-end trace makes elusive bugs reproducible, routes issues to the right owners, and seeds reliable UI tests—turning guesswork into grounded evidence so teams can ship fixes faster and with greater confidence.
Looking ahead, we are expanding AutoTrack across surfaces and deepening the context it captures—pairing sessions with network and performance signals, strengthening privacy guardrails, and integrating with automated triage and test generation. Look forward to reading more of our deep dives on auto-generated UI tests and how these journeys will power proactive quality across Grab’s app.
Join us
Grab is a leading superapp in Southeast Asia, operating across the deliveries, mobility and digital financial services sectors. Serving over 800 cities in eight Southeast Asian countries, Grab enables millions of people everyday to order food or groceries, send packages, hail a ride or taxi, pay for online purchases or access services such as lending and insurance, all through a single app. Grab was founded in 2012 with the mission to drive Southeast Asia forward by creating economic empowerment for everyone. Grab strives to serve a triple bottom line – we aim to simultaneously deliver financial performance for our shareholders and have a positive social impact, which includes economic empowerment for millions of people in the region, while mitigating our environmental footprint.
Powered by technology and driven by heart, our mission is to drive Southeast Asia forward by creating economic empowerment for everyone. If this mission speaks to you, join our team today!
Mother Jones has a long article on surveillance arms manufacturers, their wares, and how they avoid export control laws:
Operating from their base in Jakarta, where permissive export laws have allowed their surveillance business to flourish, First Wap’s European founders and executives have quietly built a phone-tracking empire, with a footprint extending from the Vatican to the Middle East to Silicon Valley.
It calls its proprietary system Altamides, which it describes in promotional materials as “a unified platform to covertly locate the whereabouts of single or multiple suspects in real-time, to detect movement patterns, and to detect whether suspects are in close vicinity with each other.”
Altamides leaves no trace on the phones it targets, unlike spyware such as Pegasus. Nor does it require a target to click on a malicious link or show any of the telltale signs (such as overheating or a short battery life) of remote monitoring.
Its secret is shrewd use of the antiquated telecom language Signaling System No. 7, known as SS7, that phone carriers use to route calls and text messages. Any entity with SS7 access can send queries requesting information about which cell tower a phone subscriber is nearest to, an essential first step to sending a text message or making a call to that subscriber. But First Wap’s technology uses SS7 to zero in on phone numbers and trace the location of their users.
A retired veteran named Lee Schmidt wanted to know how often Norfolk, Virginia’s 176 Flock Safety automated license-plate-reader cameras were tracking him. The answer, according to a U.S. District Court lawsuit filed in September, was more than four times a day, or 526 times from mid-February to early July. No, there’s no warrant out for Schmidt’s arrest, nor is there a warrant for Schmidt’s co-plaintiff, Crystal Arrington, whom the system tagged 849 times in roughly the same period.
You might think this sounds like it violates the Fourth Amendment, which protects American citizens from unreasonable searches and seizures without probable cause. Well, so does the American Civil Liberties Union. Norfolk, Virginia Judge Jamilah LeCruise also agrees, and in 2024 she ruled that plate-reader data obtained without a search warrant couldn’t be used against a defendant in a robbery case.
Researchers have discovered a new way to covertly track Android users. Both Meta and Yandex were using it, but have suddenly stopped now that they have been caught.
The details are interesting, and worth reading in detail:
Tracking code that Meta and Russia-based Yandex embed into millions of websites is de-anonymizing visitors by abusing legitimate Internet protocols, causing Chrome and other browsers to surreptitiously send unique identifiers to native apps installed on a device, researchers have discovered. Google says it’s investigating the abuse, which allows Meta and Yandex to convert ephemeral web identifiers into persistent mobile app user identities.
The covert tracking—implemented in the Meta Pixel and Yandex Metrica trackers—allows Meta and Yandex to bypass core security and privacy protections provided by both the Android operating system and browsers that run on it. Android sandboxing, for instance, isolates processes to prevent them from interacting with the OS and any other app installed on the device, cutting off access to sensitive data or privileged system resources. Defenses such as state partitioning and storage partitioning, which are built into all major browsers, store site cookies and other data associated with a website in containers that are unique to every top-level website domain to ensure they’re off-limits for every other site.
Russia is proposing a rule that all foreigners in Moscow install a tracking app on their phones.
Using a mobile application that all foreigners will have to install on their smartphones, the Russian state will receive the following information:
Residence location
Fingerprint
Face photograph
Real-time geo-location monitoring
This isn’t the first time we’ve seen this. Qatar did it in 2022 around the World Cup:
“After accepting the terms of these apps, moderators will have complete control of users’ devices,” he continued. “All personal content, the ability to edit it, share it, extract it as well as data from other apps on your device is in their hands. Moderators will even have the power to unlock users’ devices remotely.”
The Secret Service has used a technology called Locate X which uses location data harvested from ordinary apps installed on phones. Because users agreed to an opaque terms of service page, the Secret Service believes it doesn’t need a warrant.
Way back in 2018, people noticed that you could find secret military bases using data published by the Strava fitness app. Soldiers and other military personal were using them to track their runs, and you could look at the public data and find places where there should be no people running.
Six years later, the problem remains. Le Mondehasreportedthat the same Strava data can be used to track the movements of world leaders. They don’t wear the tracking device, but many of their bodyguards do.
Several Bluetooth tag companies have committed to making their future products compatible with the new standard. Apple and Google said they will continue collaborating with the Internet Engineering Task Force to further develop this technology and address the issue of unwanted tracking.
This seems like a good idea, but I worry about false alarms. If I am walking with a friend, will it alert if they have a Bluetooth tracking device in their pocket?
Consumer Reports is reporting that Facebook has built a massive surveillance network:
Using a panel of 709 volunteers who shared archives of their Facebook data, Consumer Reports found that a total of 186,892 companies sent data about them to the social network. On average, each participant in the study had their data sent to Facebook by 2,230 companies. That number varied significantly, with some panelists’ data listing over 7,000 companies providing their data. The Markup helped Consumer Reports recruit participants for the study. Participants downloaded an archive of the previous three years of their data from their Facebook settings, then provided it to Consumer Reports.
This isn’t data about your use of Facebook. This data about your interactions with other companies, all of which is correlated and analyzed by Facebook. It constantly amazes me that we willingly allow these monopoly companies that kind of surveillance power.
Here’s the Consumer Reports study. It includes policy recommendations:
Many consumers will rightly be concerned about the extent to which their activity is tracked by Facebook and other companies, and may want to take action to counteract consistent surveillance. Based on our analysis of the sample data, consumers need interventions that will:
Reduce the overall amount of tracking.
Improve the ability for consumers to take advantage of their right to opt out under state privacy laws.
Empower social media platform users and researchers to review who and what exactly is being advertised on Facebook.
Improve the transparency of Facebook’s existing tools.
The New York Times has a long article on the investigative techniques used to identify the person who stabbed and killed four University of Idaho students.
Pay attention to the techniques:
The case has shown the degree to which law enforcement investigators have come to rely on the digital footprints that ordinary Americans leave in nearly every facet of their lives. Online shopping, car sales, carrying a cellphone, drives along city streets and amateur genealogy all played roles in an investigation that was solved, in the end, as much through technology as traditional sleuthing.
[…]
At that point, investigators decided to try genetic genealogy, a method that until now has been used primarily to solve cold cases, not active murder investigations. Among the growing number of genealogy websites that help people trace their ancestors and relatives via their own DNA, some allow users to select an option that permits law enforcement to compare crime scene DNA samples against the websites’ data.
A distant cousin who has opted into the system can help investigators building a family tree from crime scene DNA to triangulate and identify a potential perpetrator of a crime.
[…]
On Dec. 23, investigators sought and received Mr. Kohberger’s cellphone records. The results added more to their suspicions: His phone was moving around in the early morning hours of Nov. 13, but was disconnected from cell networks - perhaps turned off—in the two hours around when the killings occurred.
The Anti-Theft Mode feature will make the devices invisible to Scan and Secure, the company’s in-app feature that lets you know if any nearby Tiles are following you. But to activate the new Anti-Theft Mode, the Tile owner will have to verify their real identity with a government-issued ID, submit a biometric scan that helps root out fake IDs, agree to let Tile share their information with law enforcement and agree to be subject to a $1 million penalty if convicted in a court of law of using Tile for criminal activity. So although it technically makes the device easier for stalkers to use Tiles silently, it makes the penalty of doing so high enough to (at least in theory) deter them from trying.
Interesting theory. But it won’t work against attackers who don’t have any money.
Hulls believes the approach is superior to Apple’s solution with AirTag, which emits a sound and notifies iPhone users that one of the trackers is following them.
My complaint about the technical solutions is that they only work for users of the system. Tile security requires an “in-app feature.” Apple’s AirTag “notifies iPhone users.” What we need is a common standard that is implemented on all smartphones, so that people who don’t use the trackers can be alerted if they are being surveilled by one of them.
The two people who shut down four Washington power stations in December were arrested. This is the interesting part:
Investigators identified Greenwood and Crahan almost immediately after the attacks took place by using cell phone data that allegedly showed both men in the vicinity of all four substations, according to court documents.
Nowadays, it seems like an obvious thing to do—although the search is probably unconstitutional. But way back in 2012, the Canadian CSEC—that’s their NSA—did some top-secret work on this kind of thing. The document is part of the Snowden archive, and I wrote about it:
The second application suggested is to identify a particular person whom you know visited a particular geographical area on a series of dates/times. The example in the presentation is a kidnapper. He is based in a rural area, so he can’t risk making his ransom calls from that area. Instead, he drives to an urban area to make those calls. He either uses a burner phone or a pay phone, so he can’t be identified that way. But if you assume that he has some sort of smart phone in his pocket that identifies itself over the Internet, you might be able to find him in that dataset. That is, he might be the only ID that appears in that geographical location around the same time as the ransom calls and at no other times.
There’s a whole lot of surveillance you can do if you can follow everyone, everywhere, all the time. I don’t even think turning your cell phone off would help in this instance. How many people in the Washington area turned their phones off during exactly the times of the Washington power station attacks? Probably a small enough number to investigate them all.
Researchers claim that supposedly anonymous device analytics information can identify users:
On Twitter, security researchers Tommy Mysk and Talal Haj Bakry have found that Apple’s device analytics data includes an iCloud account and can be linked directly to a specific user, including their name, date of birth, email, and associated information stored on iCloud.
Apple has long claimed otherwise:
On Apple’s device analytics and privacy legal page, the company says no information collected from a device for analytics purposes is traceable back to a specific user. “iPhone Analytics may include details about hardware and operating system specifications, performance statistics, and data about how you use your devices and applications. None of the collected information identifies you personally,” the company claims.
Apple was just sued for tracking iOS users without their consent, even when they explicitly opt out of tracking.
The collective thoughts of the interwebz
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