This story is interesting:

One Civic owner purchased a cheap aftermarket head unit off Amazon, and decided to have Claude work its magic on it, like it would a smart TV. What was reportedly discovered isn’t surprising, but is worth knowing about.

Among the pre-installed bloatware—­or spyware, if you’re being less charitable­—was a voice assistant equipped with WebView, an Android system component that lets apps display content off the web. This apparently always used about a third of processing power, even while idle, which isn’t great. There were also Baidu location services, a call recorder (!), a DAB radio client that is pretty much useless in the U.S., and Adobe Acrobat, presumably so your passenger can entertain themselves with PDFs on the go.

It’s interesting because it exposes all the insecurities in this piece of IoT software. But I think the real story is that a random person was able to do this reverse-engineering with the help of AI. Expect a lot more stories like this in the future.

St. Lucie County in Florida discovered (alt link) a dozen Flock cameras whose ownership it can’t identify, and that the county government had not permitted.

I am reminded of the decade-old story of StingRay cell phone surveillance devices in Washington, DC, whose operators were also unknown.

My guess is that in the StingRay case, the devices were operated by foreign actors. This Flock case is more likely some local government entity that didn’t bother getting approval. Were I a foreign actor, I would rather hack the existing Flock network—like Israel did with Tehran’s surveillance cameras—than risk installing my own.

Regardless, once we normalize a surveillance infrastructure, both friends and foes will take advantage of it.

Researchers at Northeastern University, in collaboration with Consumer Reports, evaluated how much modern cars spy in their drivers:

The new Northeastern study shows, for the first time, data flowing among the vehicles, the vehicle apps you download when you buy your car, and third-party companies, documenting exactly what kind of data gets siphoned from our vehicles and which companies are receiving that information.

Basically, your car’s manufacturer has you under constant surveillance, and they use that data against you.

The companies on the receiving end of your data, our investigation has found, include car insurers and lenders that are partners in “telematics data exchanges,” which compile driving data on millions of drivers, thousands of data brokers that create personalized risk scores, companies selling infotainment and WiFi hotspot products, and even local and state government agencies working on planning, traffic, and safety initiatives.

Nearly every automaker sent data to outside companies.

Even more troubling, almost a quarter of the vehicle apps were found to be sending out personally identifiable information, including vehicle owners’ names, vehicle identification numbers (VINs), and precise geographic locations. That information can make it easy for companies to link driving behavior to personal data profiles created by data brokers and marketers. Such profiles are routinely sold to banks, insurers, pharmaceutical companies, lenders, and retailers, who can use it for personalized loan terms and filtered bank and insurance offers, a CR and CalMatters investigation found.

Remember the adage “If you’re not the customer, then you’re the product”? (The sentiment is older than you think.) Turns out that with modern internet-connected everything, you’re the product even if you are the customer.

Hackers captured a Flock camera and got a look (alternate link) at the software:

While much of the automatic license plate reader’s (ALPR) most sensitive storage remained encrypted and inaccessible, the joint analysis of the recovered data shows that software running on the device explicitly detects people as well as vehicles, license plates, and bicycles. The camera can produce dozens of images of a single passing vehicle and, according to several weeks of recovered logs, generated more than a million images. Its computer-vision software also sometimes isolated bumper stickers and other graphics, including, in one case, an American flag patch on a motorcyclist’s saddlebag.

If you’re wondering how the hackers got by disk encryption, one of the unencrypted partitions contained the key for an encrypted partition. That’s pretty bad security engineering.

Not sure it’s practical, but it’s certainly striking.

Governments are switching, but I’m not sure it makes a difference:

…some municipalities, including Denver, Colorado, are ditching their Flock arrays. But keep in mind that if they’re only switching from Flock to another brand of license-plate readers, like Axon, it’s like a gambling addict trying to kick the habit by switching from FanDuel to DraftKings.

[…]

Despite what you may read on the Flock website, Axon cameras are pretty effective when it comes to hoovering up personal details that can go far beyond your license plate numbers. That means a municipality that opts for Axon cameras instead of Flock units won’t necessarily reduce the amount privacy its citizens lose through their use.

This is from a 2024 company presentation:

Officers can also tap into data showing a car’s decals, bumper stickers, back and top racks—along with temporary and unique state tags.

Flock calls it a “Vehicle Fingerprint” and it’s touted as a way for law enforcement officials to get more information “even when you don’t have full plate information,” the company’s presentation shows.

The company gives police officers the ability to search that data as well, to “build stronger cases with less information upfront.” That includes being able to locate multiple vehicles law enforcement officials believe are moving together and what Flock calls a “multi geo search.”

This kind of thing is older than AI; I wrote about it in my 2014 book Beyond Fear. Edward Snowden revealed that the NSA was using cell phone location data to track phones that were habitually near each other.

As bad as Flock is, remember that anyone with broad access to cell phone location data can do the same thing.

There are over a dozen cases around the country where police officers are using the Flock surveillance camera system to obsessively and illegally stalk people.

Alternate link.

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.

Alternate link.

Multiple news outlets are reporting on Israel’s hacking of Iranian traffic cameras and how they assisted with the killing of that country’s leadership.

The New York Times has an on the intelligence operation more generally.

Interesting research: “CHAI: Command Hijacking Against Embodied AI.”

Abstract: Embodied Artificial Intelligence (AI) promises to handle edge cases in robotic vehicle systems where data is scarce by using common-sense reasoning grounded in perception and action to generalize beyond training distributions and adapt to novel real-world situations. These capabilities, however, also create new security risks. In this paper, we introduce CHAI (Command Hijacking against embodied AI), a new class of prompt-based attacks that exploit the multimodal language interpretation abilities of Large Visual-Language Models (LVLMs). CHAI embeds deceptive natural language instructions, such as misleading signs, in visual input, systematically searches the token space, builds a dictionary of prompts, and guides an attacker model to generate Visual Attack Prompts. We evaluate CHAI on four LVLM agents; drone emergency landing, autonomous driving, and aerial object tracking, and on a real robotic vehicle. Our experiments show that CHAI consistently outperforms state-of-the-art attacks. By exploiting the semantic and multimodal reasoning strengths of next-generation embodied AI systems, CHAI underscores the urgent need for defenses that extend beyond traditional adversarial robustness.

News article.