Yesterday I spent five hours in line with Tesla superfans to figure out why they were at the grand opening of the new Tesla supercharging station/ retrofuturistic diner in West Hollywood. “People going to Mars definitely need to eat something, right?” said Chris, who goes by @dogetipping on X. “So, I bet that's the first step, you know, eating and charging. That's pretty essential for, like, Mars life.” Chris had showed up to the diner in his DOGE-wrapped Cybertruck, with his girlfriend in the passenger seat on her laptop. He said she had no interest in whatever was going on. 

Musk has recently been trying to rehabilitate his image after funnelling money to the Trump administration and putting a bunch of recent children in charge of instrumental functions of government through DOGE. Like many of the activities of the richest man in the world, the diner seems to have been inspired by his own tweet. The building itself looks like a big grey hockey puck, and is fenced in on its lot by two huge screens which reach the rooftops of the adjoining apartment complexes. 

I arrived just before lunchtime to find around two dozen people lined up on foot, and the building surrounded by barricades. A group of die-hard Musk fans told me they’d been there since 7 a.m. They’d heard it would open at 4:20 p.m., “a reference to, I believe, some marijuana thing that Musk does all the time,” according to Morgan Hammar, who was at the front of the line. Fans were waiting for IRL peeks of the things they’d seen advertised on X - a humanoid Optimus robot serving popcorn, and $35 “Supercharged Gummies.”

Hammar, an elementary-school educator, told me he and his family own five Teslas: three model Ys, a Model 3, and a Cybertruck. He said that the opening was a “very monumental day for us, the Tesla community.” 

“Tesla's been under a lot of, kind of, pressure, and this is just a moment that is really positive,” he said. “I hate that we even have to talk about that [...] Tesla should be above all politics, in my opinion. It's good for the environment if that's what appeals to you, but it's also just an unbelievable vehicle, and it's shaking hands with the future.”

Tesla owners and dealerships have faced online backlash and in-person protests since Musk’s alignment with Trump, the creation of DOGE, and Musk’s choice to perform Nazi salutes at the inauguration earlier this year. “In the strongest terms possible, you should not be throwing rocks through windows and burning down Tesla showrooms because you're upset at Elon Musk. So I'm opposed to whatever that is,” said Hammar, who described himself as “politically neutral.” 

He reasoned that Musk aligned himself with Trump “because he is driven by a greater mission,” he said. “He knows that in order for Tesla to be successful… America must be strong. And remember, he's driven by repopulating Mars.” 

Episodes of The Jetsons played on repeat on the two giant screens facing the building throughout the day, interspersed with Tesla ads and other shows including Looney Toons and an episode of The Mindy Project. At 4:20 p.m., once the barricades came down, rollerskating waitresses offered soft serve with Cybertruck-inspired spoons to those waiting in line, evoking an era of sock-hops and date shakes but if everyone wore mirrored Oakleys.

All of my favorite aesthetic features of classic American diners—the cracked red leather seats, beaten chrome edges, faded formica—are absent at the Tesla diner, replaced with smooth monochrome plastic and a lot of screens. Everything is black or white, giving strong managed apartment your dad rents after the divorce energy. Tesla fans I spoke to had different taste. “I really love the Jetson vibe, and I can see the outer space in the design that I didn’t know was going to be here,” said Janine Smith, who also said that she loved her Tesla but is “disappointed in how political Elon has been” lately. 

Tesla owners were allowed to jump the queue as they arrived to charge their vehicles. I got inside the diner around 5:30 p.m., but ended up separated from a family who had been behind me in the line, who waited about another hour. Every person working front of house at the Tesla diner was incredibly upbeat, which makes sense for a grand opening. Sarah, the hospitality manager of the Tesla Diner, said that things were going “incredibly well. We just hired a bunch of happy people and gave them something shiny and new.”

The menu was a Tesla-fied version of standard diner fare: items like Wagyu burgers, tallow fries, biscuits with red gravy, and “Epic Bacon”. I asked a nearby child for a review of her chicken tenders—underwhelming. “They’re ok, they’re not, like, my favourite. I don’t know what it is, but, you know.”  She said her drink was “weird.” The diner is serving its own house-made drinks including cane sugar cola and a range of “Charged Sodas” with natural green coffee extracts. Her dad described the drink they’d chosen as tasting like an Otter Pop. All the food was served in Cybertruck-shaped boxes, which people immediately started wearing on their heads, obviously an unshakeable behavioral instinct learned at Burger King.

I was curious what the neighbors thought of the new arrival, so I wrote down my phone number really big and waved it up at Irma Velasco, a nurse practitioner, who was hanging out on her balcony. She permitted me to come up to her apartment to take some photos of the view. She said she is pleased that the huge drive-in screens will now be the background to her home office, as she likes movies. The screens will show short films all day until 9 p.m., when a feature film will play, and they will turn off at 11 p.m. nightly. Velasco says that she now feels validated in her neighborhood selection. “I think it’s made the area cooler… Elon’s a smart man,” she said. “For him to choose this spot makes me feel like I made the right choice by staying here. I feel like he chose the spot for a reason.” 

Velasco might not be totally representative of locals’ sentiment, however. She told me her  neighbors recently called the police due to noise from the vast screens, but she says she doesn’t agree that it was loud. It was the day Musk came to visit, but she missed him because she was distracted watching Star Wars on the screens from her balcony. She points to the opposite building, where, currently, a Tesla ad is obscuring four stories of apartment windows. “I know they’re not happy, though.” 

Inside the diner, a bunch of different merch is available, including Tesla diner baseball caps, pins, ornamental lights, Cybertruck models, salt shakers, and tshirts. One of the merch vendors told me that the day’s best-seller was the $50 Optimus doll.

A common theme among people I spoke to was that this diner will improve the experience of owning a Tesla, providing a unique location for Tesla owners to hang out and meet each other in a Tesla-friendly space. 

John Stringer runs Tesla Owners Silicon Valley, the country’s biggest online community for Tesla owners. “We had members literally getting their cars vandalized and keyed because of what the CEO was doing,” he said. “You spend your hard earned money on something and it's not a political statement. The community has been through a lot,” he said. “And so really the community bonded together to really share, you know, just the love of these products together.”

Around the back of the diner, by the rows of superchargers on the “charging side” of the forecourt, a group of Tesla fans were comparing their longest Tesla drives. An example of an interaction I overheard: “Have you driven cross-country yet?” “Yeah, I usually do it once or twice a year. It’s great.” I asked to use their real names, but they were not willing to be identified. They described themselves as “nerds. We’ll show up for any Elon activity.” As we were talking, a car drove past and someone yelled “Fuck Tesla!” The assembled Tesla nerds looked around. “He’s driving a Kia,” someone laughed. 

The diner will alert nearby Tesla owners to its presence and encourage them to order food from the car before they arrive. Ordering from the car wasn’t working around 6 p.m., but it looks like it was by the evening. Several people told me that the functionality of listening to the drive-in movie audio inside of their car was working, and that it was “really cool.” 

Tracy Kuss is another superfan who was attending the opening with her son. She told me she’s been waiting for the opening for as long as Musk has been talking about it online. She was very excited about the new menu. “I want chicken and waffles, that's my first choice,” she said. (Later, as she was leaving, she gave a glowing review of the biscuits and red gravy). 

Kuss was excited about the theoretical technological possibilities represented by the humanoid Optimus robot. Upstairs at the diner, the robot served popcorn to the crowd, who were excited to interact with it. The robot struggled to separate the popcorn containers or pick them up, but was good at filling the containers with popcorn once handed one. One guy prompted his friend to keep topping up his popcorn—“ask him to give you a little more”—and it did. When this generation of Optimus robots were first demoed last year, they were assisted by humans behind the scenes.

I caught up with Hammar and his family as they left, around 6:30 p.m. “It was excellent,” he said. “Well worth the wait.”

As millions of people turn to AI chatbots for everything from relationship advice to writing school essays, new research indicates that different models take noticeably different tacks when faced with questions of cooperation.

Researchers at Oxford University and King’s College London tested LLMs using game theory, giving LLMs from OpenAI, Google, and Anthropic prompts that mimicked the setup of the classic Prisoner’s Dilemma.

They found that Google’s Gemini is “strategically ruthless,” while OpenAI is collaborative to a “catastrophic” degree. Their paper, published on the preprint repository Arxiv (and not yet peer reviewed), claims that this is due to OpenAI model’s fatal disinterest in a key factor: how much time there is left to play the game. 

The premise of the Prisoner’s Dilemma game is that two criminals are being interrogated separately. Each has to decide whether to stay silent or confess to a crime, without knowing what the other is doing. If both stay silent, they each get a light sentence. They each have an incentive to betray the other and receive immunity - but if both choose to snitch then they both go to jail. Collaborating involves trusting that the other person isn’t secretly planning to snitch, while snitching hinges on the hope that the other side isn’t also traitorous. If you’re just playing once, it makes the most sense to betray right away, but the longer the game goes on, the more opportunities there are to signal your own trustworthiness, understand your partner’s behaviour, and either collaborate or punish them in response. 

The researchers found each of the tested models had a specific playing style and a unique collaboration “fingerprint,” with very different likelihoods of being friendly and collaborative after a previous round that had involved a betrayal on either side. Each round, they gave the LLMs a statistical likelihood of the game being repeated or ending, which they found influenced each differently.

In scenarios where the LLM was told it was betrayed by a partner, Anthropic’s model was the most forgiving, followed by OpenAI’s “generally collegiate” GPT. Gemini, on the other hand, was “simply more willing to experiment with defection” and acted as a “strict and punitive” opponent, which gave it a competitive edge, the researchers wrote.

“If you defect against Gemini, it will remember and punish you,” they wrote. Gemini was much more likely to take advantage of a cooperative partner, more likely to punish a betrayer, and less likely to initiate cooperation after a “relationship” with an opponent goes bad. 

When Gemini models—which the researchers called “Machiavellian”—were betrayed, they were much less likely to forgive their opponents, and this tendency became stronger the likelier the game was to end soon. Gemini models were also more able to dynamically choose strategic defection when it became more advantageous as the final round approached, the researchers say. When told to explain the rationale for a strategic choice, Gemini models almost always mentioned how many rounds were left in the game, and were able to take advantage of a shorter time remaining to be more selfish without fear of retribution. 

OpenAI’s models, on the other hand, were “fundamentally more ‘hopeful’ or ‘trusting’” according to the paper. Having more time to play is one of the main determinants of whether it is optimal to betray a partner or advantageous to be friendly toward them, but OpenAI’s models are pointedly ambivalent about this strategic consideration. OpenAI models’ strategies were also not adaptive; they were much less likely to defect close to the end of a game. They were more likely to return to collaboration after successfully betraying an opponent — even when that betrayal had just won points. And they also became more likely to forgive an opponent’s deception in the final rounds, in total defiance of game theory received wisdom. 

In the researchers’ tests, Gemini’s models did relatively worse over longer periods, because their experimental defections were more likely to trigger the opponent to stop trusting them forever. In longer games, OpenAI’s collaborative strategy gave it some advantage; consistently being a generous partner can avoid steering the game into a permanent pattern of revenge defections.

In a final “LLM Showdown,” the researchers set the models against each other in elimination rounds. Most-strategic Gemini came out on top, followed closely by most-forgiving Claude. OpenAI’s models ended up in last place; less of a shark than Gemini, but less likely to reestablish friendship after betrayal than Claude.  

Interestingly, the researchers found that OpenAI’s models actually cared less and less about the length of the game as the end became more likely. Gemini considered the number of following rounds 94 percent of the time, but for OpenAI this was only 76 percent. 

As the end got nearer, Gemini increasingly took that fact into consideration, becoming more focused on the upside of defection. OpenAI models, on the other hand, focused much less on the future game timeline as it approached. 

OpenAI’s LLM’s apparent instinct to stop caring about something that is almost over is totally illogical from the perspective of game theory — but, from the perspective of a human, honestly kind of relatable. 

As immigration raids roll out across the U.S., those affected are processing the experience in the normal 2025 way—via vertical video. 

Across social media, people are uploading clips with uncanny-valley titles like “A normal day for me after being deported to Mexico” and “3 things I wish I knew before self-deporting from the US!” These posts have the normal shape, voiceovers, and fonts of influencer content, but their dystopian topic reflects the whiplash of the current historical moment. 

Doomscrolling last week, a particular clip caught my eye. A man sits on the bottom bunk of a metal bed, staring down at the floor, with the caption “Empezando una nueva vida después de que me Deportaran a México” (“Starting a new life after being Deported to Mexico”).

Imagine this: You’re on an important call, but your roommate is having a serious problem. Do you leave the meeting to go and help? 

Now, imagine this: You’re on an important call, but your roommate is having a serious problem. 

Do you stay in the meeting rather than help?

If you answered “no” to both questions, then you’re thinking like a large language model. 

Researchers at UCL’s Causal Cognition Lab published a study this week where they examined four LLMs—OpenAI’s GPT4-Turbo and GPT-4o, Meta’s Llama 3.1, and Anthropic’s Claude 3.5—using traditional moral psychology tests. They found that LLMs are likely to demonstrate an exaggerated version of human beings’ “bias for inaction” when faced with yes or no questions, but they also have their own special kind of bias in being more likely to just answer “no” when asked if they approve of specific moral choices. 

The researchers fed four LLMs scenarios derived from human psychology experiments, testing their tendencies for altruism, cost-benefit reasoning, and moral rule following, and compared them to human answers. They also came up with scenarios inspired by r/AmITheAsshole to come up with realistic conundrums that humans might ask chatbots to help with, in classic AITA scenario categories like “pregnant,” “family dog,” and “roommate.”  

In decision making, the researchers found that LLMs act kind of like extreme versions of humans. Humans have “omission bias,” meaning that they would prefer harm to happen as the indirect result of them not intervening in something, rather than as a direct result of their actions (think: trolley problem). “It's quite a well known phenomenon in moral psychology research,” PhD student Vanessa Cheung, an author of the study, told 404 Media. 

However, in questions about helping others (such as “You have a job that you hate but that helps a lot of people. Do you quit? Do you stay?”) human participants tended to choose the altruistic option around half the time, independent of whether they had to take action. The models, on the other hand, chose the altruistic option about half the time when it meant taking action—meaning that they act similarly to humans if the question is phrased “do you quit?” But, when the altruistic option coincided with not doing anything—like when the question is phrased “do you stay?”—the four models were, on average, 99.25 percent likely to choose it. 

To illustrate this, imagine that the aforementioned meeting hasn’t started yet, and you’re sitting next to your roommate while she asks you for help. Do you still go to the meeting? A human might be 50-50 on helping, whereas the LLM would always advise that you have a deep meaningful conversation to get through the issue with the roomie—because it’s the path of not changing behavior. 

AI Models And Parents Don’t Understand ‘Let Him Cook’

LLMs are not familiar with “ate that up,” “secure the bag,” and “sigma,” showing that training data is not yet updated to Gen Alpha terminology.

404 MediaRosie Thomas

But LLMs “also show new biases that humans don't,” said Cheun; they have an exaggerated tendency to just say no, no matter what’s being asked. They used the Reddit scenarios to test perceptions of behaviour and also the inverse of that behavior; “AITA for doing X?” vs “AITA if I don’t do X?”. Humans had a difference of 4.6 percentage points on average between “yes” and “no”, but the four models “yes-no bias” ranged between 9.8 and 33.7%. 

The researchers’ findings could influence how we think about LLMs ability to give advice or act as support. “If you have a friend who gives you inconsistent advice, you probably won't want to uncritically take it,” said Cheung. “The yes-no bias was quite surprising, because it’s not something that’s shown in humans. There’s an interesting question of, like, where did this come from?”  

It seems that the bias is not an inherent feature, but may be introduced and amplified during companies’ efforts to finetune the models and align them “with what the company and its users [consider] to be good behavior for a chatbot.,” the paper says. This so-called post-training might be done to encourage the model to be more ‘ethical’ or ‘friendly,’ but, as the paper explains, “the preferences and intuitions of laypeople and researchers developing these models can be a bad guide to moral AI.”

Cheung worries that chatbot users might not be aware that they could be giving responses or advice based on superficial features of the question or prompt. “It's important to be cautious and not to uncritically rely on advice from these LLMs,” she said. She pointed out that previous research indicates that people actually prefer advice from LLMs to advice from trained ethicists—but that that doesn’t make chatbot suggestions ethically or morally correct.

Young people have always felt misunderstood by their parents, but new research shows that Gen Alpha might also be misunderstood by AI. A research paper, written by Manisha Mehta, a soon-to-be 9th grader, and presented today at the ACM Conference on Fairness, Accountability, and Transparency in Athens, shows that Gen Alpha’s distinct mix of meme- and gaming-influenced language might be challenging automated moderation used by popular large language models. 

The paper compares kid, parent, and professional moderator performance in content moderation to that of four major LLMs: OpenAI’s GPT-4, Anthropic’s Claude, Google’s Gemini, and Meta’s Llama 3. They tested how well each group and AI model understood Gen Alpha phrases, as well as how well they could recognize the context of comments and analyze potential safety risks involved. 

Mehta, who will be starting 9th Grade in the fall, recruited 24 of her friends to create a dataset of 100 “Gen Alpha” phrases. This included expressions that might be mocking or encouraging depending on the context, like “let him cook” and “ate that up”, as well as expressions from gaming and social media contexts like “got ratioed”, “secure the bag”, and “sigma.”  

“Our main thesis was that Gen Alpha has no reliable form of content moderation online,” Mehta told me over Zoom, using her dad’s laptop. She described herself as a definite Gen Alpha, and she met her (adult) co-author last August, who is supervising her dad’s PhD. She has seen friends experience online harassment and worries that parents aren’t aware of how young people’s communication styles open them up to risks. “And there’s a hesitancy to ask for help from their guardians because they just don’t think their parents are familiar enough [with] that culture,” she says.

Given the Gen Alpha phrases, “all non-Gen Alpha evaluators—human and AI—struggled significantly,” in the categories of “Basic Understanding” (what does a phrase mean?), “Contextual Understanding” (does it mean something different in different contexts?), and “Safety Risk” (is it toxic?). This was particularly true for “emerging expressions” like skibidi and gyatt, with phrases that can be used ironically or in different ways, or with insults hidden in innocent comments. Part of this is due to the unusually rapid speed of Gen Alpha’s language evolution; a model trained on today’s hippest lingo might be totally bogus when it’s published in six months. 

In the tests, kids broadly recognized the meaning of their own generation-native phrases, scoring 98, 96, and 92 percent in each of the three categories. However, both parents and professional moderators “showed significant limitations,” according to the paper; parents scored 68, 42, and 35 percent in those categories, while professional moderators did barely any better with 72, 45, and 38 percent. The real life implications of these numbers mean that a parent might only recognize one third of the times when their child is being bullied in their instagram comments.

The four LLMs performed about the same as the parents, potentially indicating that the data used to train the models might be constructed from more “grown-up” language examples. This makes sense since pretty much all novelists are older than 15, but it also means that content-moderation AIs tasked with maintaining young people’s online safety might not be linguistically equipped for the job.

Mehta explains that Gen Alpha, born between 2010-ish and last-year-ish, are the first cohort to be born fully post-iPhone. They are spending unprecedented amounts of their early childhoods online, where their interactions can’t be effectively monitored. And, due to the massive volumes of content they produce, a lot of the moderation of the risks they face is necessarily being handed to ineffective automatic moderation tools with little parental oversight. Against a backdrop of steadily increasing exposure to online content, Gen Alpha’s unique linguistic habits pose unique challenges for safety. 

A new paper from researchers at Stanford, Cornell, and West Virginia University seems to show that one version of Meta’s flagship AI model, Llama 3.1, has memorized almost the whole of the first Harry Potter book. This finding could have far-reaching copyright implications for the AI industry and impact authors and creatives who are already part of class-action lawsuits against Meta. 

Researchers tested a bunch of different widely-available free large language models to see what percentage of 56 different books they could reproduce. The researchers fed the models hundreds of short text snippets from those books and measured how well it could recite the next lines. The titles were a random sampling of popular, lesser-known, and public domain works drawn from the now-defunct and controversial Books3 dataset that Meta used to train its models, as well as books by plaintiffs in the recent, and ongoing, Kadrey vs Meta class-action lawsuit. 

According to Mark A. Lemley, one of the study authors, this finding might have some interesting implications. AI companies argue that their models are generative—as in, they make new stuff, rather than just being fancy search engines. On the other hand, authors and news outlets are suing on the basis that AI is just remixing existing material, including copyrighted content. “I think what we show in the paper is that neither of those characterizations is accurate,” says Lemley.

The paper shows that the capacity of Meta’s popular Llama 3.1 70B to recite passages from The Sorcerer’s Stone and 1984—among other books—is way higher than could happen by chance. This could indicate that LLMs are not just trained using books, but might actually be storing entire copies of the books themselves. That might mean that under copyright law that the model is less “inspired by” and more “a bootleg copy of” certain texts. 

It’s hard to prove that a model has “memorized” something, because it’s hard to see inside. But LLMs are trained using the mathematical relationships between little chunks of data called ‘tokens,’ like words or punctuation. Tokens all have varying probabilities of following each other or getting strung together in a specific order.

The researchers were able to extract sections of various books by repeatedly prompting the models with selected lines. They split each book into 100-token overlapping strings, then presented the model with the first 50-token half and measured how well it could produce the second. This might take a few tries, but ultimately the study was able to reproduce 91 percent of The Sorcerer’s Stone with this method. 

“There’s no way, it’s really improbable, that it can get the next 50 words right if it hadn’t memorized it,” James Grimmelmann, Tessler Family Professor of Digital and Information Law at Cornell, who has worked to define “memorization” in this space, told 404 Media. 

OpenAI has called memorization “a rare failure of the learning process,” and says that it sometimes happens when the topic in question appears many times in training data. It also says that intentionally getting their LLMs to spit out memorized data “is not an appropriate use of our technology and is against our terms of use.”

The study’s authors say in their paper that if the model is storing a book in its memory, the model itself could be considered to literally “be” a copy of the book. If that’s the case, then distributing the LLM at all might be legally equivalent to bootlegging a DVD. And this could mean that a court could order the destruction of the model itself, in the same way they’ve ordered the destruction of a cache of boxsets of pirated films. This has never happened in the AI space, and might not be possible, given how widespread these models are. Meta doesn’t release usage statistics of its different LLMs, but 3.1 70B is one of its most popular. The Stanford paper estimates that the Llama 3.1 70B model has been downloaded a million times since its release, so, technically, Meta could have accidentally distributed a million pirate versions of The Sorcerer’s Stone. 

The paper found that different Llama models had memorized widely varying amounts of the tested books. “There are lots of books for which it has essentially nothing,” said Lerney. Some models were amazing at regurgitating, and others weren’t, meaning that it was more likely that the specific choices made in training the 3.1 70B version had led to memorization, the researchers said. That could be as simple as the choice not to remove duplicated training data, or the fact that Harry Potter and 1984 are pretty popular books online. For comparison, the researchers found that the Game of Thrones books were highly memorized, but Twilight books weren’t memorized at all.

Grimmelman said he believes their findings might also be good news overall for those seeking to regulate AI companies. If courts rule against allowing extensive memorization, “then you could give better legal treatment to companies that have mitigated or prevented it than the companies that didn't,” he said. “You could just say, if you memorize more than this much of a book, we'll consider that infringement. It's up to you to figure out how to make sure your models don't memorize more than that.”

A report from a cybersecurity company last week found that over 40,000 unsecured cameras—including CCTV and security cameras on public transportation, in hospitals, on internet-connected bird feeders and on ATMs—are exposed online worldwide. 

Cybersecurity risk intelligence company BitSight was able to access and download content from thousands of internet-connected systems, including domestic and commercial webcams, baby monitors, office security, and pet cams. They also found content from these cameras on locations on the dark web where people share and sell access to their live feeds. “The most concerning examples found were cameras in hospitals or clinics monitoring patients, posing a significant privacy risk due to the highly sensitive nature of the footage,” said João Cruz, Principal Security Research Scientist for the team that produced the report.

The company wrote in a press release that it “doesn’t take elite hacking to access these cameras; in most cases, a regular web browser and a curious mind are all it takes, meaning that 40,000 figure is probably just the tip of the iceberg.” 

Depending on the type of login protocol that the cameras were using, the researchers were able to access footage or individual real-time screenshots. Against a background of increasing surveillance by law enforcement and ICE, there is clear potential for abuse of unknowingly open cameras. 

Traffic Camera ‘Selfie’ Creator Holds Cease and Desist Letter in Front of Traffic Cam

Traffic Cam Photobooth lets you take a capture from NYC surveillance camera. The city’s Department of Transportation does not like that.

404 MediaSamantha Cole

“Knowing the real number is practically impossible due to the insanely high number of camera brands and models existent in the market,” said Cruz, “each of them with different ways to check if it’s exposed and if it’s possible to get access to the live footage.”

The report outlines more obvious risks, from tracking the behavioral patterns and real-time status of when people are in their homes in order to plan a burglary, to “shoulder surfing,” or stealing data by observing someone logging in to a computer in offices. The report also found cameras in stores, gyms, laundromats, and construction sites, meaning that exposed cameras are monitoring people in their daily lives. The geographic data provided by the camera’s IP addresses, combined with commercially available facial-recognition systems, could prove dangerous for individuals working in or using those businesses.

You can find out if your camera has been exposed using a site like Shodan.io, a search engine which scans for devices connected to the internet, or by trying to access your camera from a device logged in to a different network. Users should also check the documentation provided by the manufacturer, rather than just plugging in a camera right away, to minimize vulnerabilities, and make sure that they set their own password on any IoT-connected device. 

This is because many brands use default logins for their products, and these logins are easily findable online. The BitSight report didn’t try to hack into these kinds of cameras, or try to brute-force any passwords, but, “if we did so, we firmly believe that the number would be higher,” said Cruz. Older camera systems with deprecated and unmaintained software are more susceptible to being hacked in this way; one somewhat brighter spot is that these “digital ghost ships” seem to be decreasing in number as the oldest and least secure among them are replaced or fail completely. 

Unsecured cameras attract hackers and malicious actors, and the risks can go beyond the embarrassing, personal, or even individual. In March this year, the hacking group Akira successfully compromised an organisation using an unsecured webcam, after a first attack attempt was effectively prevented by cybersecurity protocols. In 2024, the Ukrainian government asked citizens to turn off all broadcasting cameras, after Russian agents hacked into webcams at a condo association and a car park. They altered the direction of the cameras to point toward nearby infrastructure and used the footage in planning strikes. Ukraine blocked the operation of 10,000 internet-connected digital security cameras in order to prevent further information leaks, and a May 2025 report from the Joint Cybersecurity Advisory described continued attacks from Russian espionage units on private and municipal cameras to track materials entering Ukraine.