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You can find them by searching for OpenAI chatbot warning messages, like: “I’m sorry, I cannot provide a response as it goes against OpenAI’s use case policy.”
I hadn’t thought about this before: identifying bots by searching for distinctive bot phrases.
A helpful summary of which US retail stores are using facial recognition, thinking about using it, or currently not planning on using it. (This, of course, can all change without notice.)
Three years ago, I wrote that campaigns to ban facial recognition are too narrow. The problem here is identification, correlation, and then discrimination. There’s no difference whether the identification technology is facial recognition, the MAC address of our phones, gait recognition, license plate recognition, or anything else. Facial recognition is just the easiest technology right now.
They’re not that good:
Security researchers Jesse D’Aguanno and Timo Teräs write that, with varying degrees of reverse-engineering and using some external hardware, they were able to fool the Goodix fingerprint sensor in a Dell Inspiron 15, the Synaptic sensor in a Lenovo ThinkPad T14, and the ELAN sensor in one of Microsoft’s own Surface Pro Type Covers. These are just three laptop models from the wide universe of PCs, but one of these three companies usually does make the fingerprint sensor in every laptop we’ve reviewed in the last few years. It’s likely that most Windows PCs with fingerprint readers will be vulnerable to similar exploits.
Interesting article on technologies that will automatically identify people:
With technology like that on Mr. Leyvand’s head, Facebook could prevent users from ever forgetting a colleague’s name, give a reminder at a cocktail party that an acquaintance had kids to ask about or help find someone at a crowded conference. However, six years later, the company now known as Meta has not released a version of that product and Mr. Leyvand has departed for Apple to work on its Vision Pro augmented reality glasses.
The technology is here. Maybe the implementation is still dorky, but that will change. The social implications will be enormous.
Really interesting “systematization of knowledge” paper:
“SoK: The Ghost Trilemma”
Abstract: Trolls, bots, and sybils distort online discourse and compromise the security of networked platforms. User identity is central to the vectors of attack and manipulation employed in these contexts. However it has long seemed that, try as it might, the security community has been unable to stem the rising tide of such problems. We posit the Ghost Trilemma, that there are three key properties of identity—sentience, location, and uniqueness—that cannot be simultaneously verified in a fully-decentralized setting. Many fully-decentralized systems—whether for communication or social coordination—grapple with this trilemma in some way, perhaps unknowingly. In this Systematization of Knowledge (SoK) paper, we examine the design space, use cases, problems with prior approaches, and possible paths forward. We sketch a proof of this trilemma and outline options for practical, incrementally deployable schemes to achieve an acceptable tradeoff of trust in centralized trust anchors, decentralized operation, and an ability to withstand a range of attacks, while protecting user privacy.
I think this conceptualization makes sense, and explains a lot.
A reporter used an AI synthesis of his own voice to fool the voice authentication system for Lloyd’s Bank.
Brian Krebs is reporting on a vulnerability in Experian’s website:
Identity thieves have been exploiting a glaring security weakness in the website of Experian, one of the big three consumer credit reporting bureaus. Normally, Experian requires that those seeking a copy of their credit report successfully answer several multiple choice questions about their financial history. But until the end of 2022, Experian’s website allowed anyone to bypass these questions and go straight to the consumer’s report. All that was needed was the person’s name, address, birthday and Social Security number.
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.
We’ve always known that phones—and the people carrying them—can be uniquely identified from their Bluetooth signatures, and that we need security techniques to prevent that. This new research shows that that’s not enough.
Computer scientists at the University of California San Diego proved in a study published May 24 that minute imperfections in phones caused during manufacturing create a unique Bluetooth beacon, one that establishes a digital signature or fingerprint distinct from any other device. Though phones’ Bluetooth uses cryptographic technology that limits trackability, using a radio receiver, these distortions in the Bluetooth signal can be discerned to track individual devices.
[…]
The study’s scientists conducted tests to show whether multiple phones being in one place could disrupt their ability to track individual signals. Results in an initial experiment showed they managed to discern individual signals for 40% of 162 devices in public. Another, scaled-up experiment showed they could discern 47% of 647 devices in a public hallway across two days.
The tracking range depends on device and the environment, and it could be several hundred feet, but in a crowded location it might only be 10 or so feet. Scientists were able to follow a volunteer’s signal as they went to and from their house. Certain environmental factors can disrupt a Bluetooth signal, including changes in environment temperature, and some devices send signals with more power and range than others.
One might say “well, I’ll just keep Bluetooth turned off when not in use,” but the researchers said they found that some devices, especially iPhones, don’t actually turn off Bluetooth unless a user goes directly into settings to turn off the signal. Most people might not even realize their Bluetooth is being constantly emitted by many smart devices.
Researchers are using the reflection of the smartphone in the pupils of faces taken as selfies to infer information about how the phone is being used:
For now, the research is focusing on six different ways a user can hold a device like a smartphone: with both hands, just the left, or just the right in portrait mode, and the same options in horizontal mode.
It’s not a lot of information, but it’s a start. (It’ll be a while before we can reproduce these results from Blade Runner.)
Research paper.
Sidebar photo of Bruce Schneier by Joe MacInnis.