A MacVoices’ panel member’s firsthand report from Black Hat explores the conference’s enterprise security focus before the discussion turns to AI models escaping sandbox restrictions to complete assigned tasks. The panel discusses the OpenAI-Hugging Face hack, unintended AI behavior and the dangers of moving too fast. Can developers establish effective limits without sacrificing innovation or competitive advantage? The race for better AI continues.
Cameras in airline seatback screens and increasingly sophisticated driver-monitoring systems spark a debate over safety, privacy, data access, and surveillance. The MacVoices Live! panel examines EU vehicle requirements, Tesla interior cameras, dash cams, accident evidence, connected devices, and whether consumers receive enough disclosure and control over technologies designed to watch them. Panel members include: Chuck […]
At Flip The Script in San Francisco at WWDC, Vladyslav Hamolia, AI Staff Engineer for MacPaw.discusses the company’s push to become AI-first by rethinking existing products, building internal tools, and supporting third-party vendors. He profiles Eney, the MacPaw AI assistant (available in Setapp) designed to perform tasks, optimize workflows, and improve daily routines. It addresses security while balancing local-first privacy goals with hybrid cloud models.
AI is taking on a growing role in cybersecurity (whether we like it or not), from vulnerability discovery to faster exploit development. Chuck Joiner, David Ginsburg, Eric Bolden, Web Bixby, Jim Rea, Brian Flanigan-Arthurs, Jeff Gamet, and Marty Jencius look at both sides oof the issue and push back on “Bugmageddon” hype. The discussion also covers X post limits, Microsoft Teams retiring the misguided Together Mode, safer login practices, AI-run radio chaos, Google’s Apple-like naming choices, and free storage tied to phone numbers.
Your fear of flying is only going to get worse once you hear what these white hat hackers figured out to do with a popular airliner.
As Wired reports, a team of security researchers from the University of California, San Diego and Oberlin College created a device the size of a coin that can take over the autopilot system of a Boeing 737 and change its flight plan, not to mention tampering with key values in its takeoff and fuel calculations.
According to its creators, the device could also change the data on a pilot’s screen so they don’t notice anything’s amiss — or even trick a pilot into making a disastrous decision.
“If you could get 60 seconds with an airplane, what could you do?” Stefan Savage, a UCSL professor who led the project, asked Wired. “Well, it turns out there’s a port that’s externally accessible. You can get to it with no special tools in about 15 seconds. And you can shove in a piece of electronics a little bigger than a quarter that lets you basically tell the autopilot what to do and lie to the pilot about changes to the flight plan.”
The attack relies on plugging the device, which costs less than $100, into a port hidden by a hatch on the exterior of the plane, which the researchers say is easily within reach of maintenance workers and airline staff. Once the device is plugged in, it could connect to the plane’s in-flight Wi-Fi system and allow a hacker to remotely control the aircraft’s systems from terra firma.
As Savage explained to Wired, his team found this vulnerability after spending years cobbling together 737 computer parts to effectively recreate the plane’s nervous system. Inspired by credit card skimming devices that plug into ATMs to steal your information, they realized that an entire aircraft could be broken into by physically plugging a device into its internal “bus” — the system that connects different components of its computer.
And after digging through Boeing wiring diagrams, they found their way in: a port carrying data between the plane’s Flight Manage Computer and the pilot’s display unit, accessible from an unlocked hatch on the plane’s exterior.
It was like finding “the goddamn exhaust port on the Death Star,” Savage told Wired.
The havoc a hacker wreaks using their attack could be catastrophic. They could feed the pilot the wrong information about the air temperature outside or about the weight of its cargo and passengers and cause them to miss the speed they need for takeoff before running out of runway, Wired noted. Or they could suddenly change its navigation and crash it into a mountain.
The attacks could also be far more sneaky.
“It could be something as subtle as, you’re in the Pacific, you see blue everywhere, and this diverts you 3 degrees off course, and now you’re in the middle of nowhere,” Aaron Schulman, another UCSD professor working on the project, told the magazine.
Savage and Schulman shared their findings with Boeing six years ago and have worked closely with the company to explore the vulnerability.
But the researchers say that Boeing hasn’t told them about any fix to the vulnerabilities they found. In fact, they say it’s likely that it any fix may not have been implemented yet, since commercial airplanes are rarely redesigned.
You shouldn’t panic though, they insist.
“All of the authors of this paper routinely travel on Boeing 737 aircraft and expect to continue doing so,” they wrote in their paper.
Beau Woods, a former adviser to the Cybersecurity and Infrastructure Security Agency and the Boeing’s Industry Cyber Technical Council, was alarmed by the findings.
“It is entirely possible to have someone who is on staff go up to an airplane when it’s on the ground, going through maintenance, and put this type of thing in there,” Woods told Wired.
“This is something the aviation industry will want to plan to defend against,” Savage agreed. “I would not sleep on this one.”
In light of plummeting public support for its widespread automatic license plate readers, surveillance company Flock has lost dozens of contracts with cities across the US.
Citizens are increasingly disgusted by the company’s presence on public streets, opting to hack down the cameras, blind them, or sabotage them in other creative ways.
“His project, which he calls noRecognition, allows people to escape the automatic detection and algorithmic surveillance used across the U.S. and beyond.” https://t.co/OIQNOt9B0a@TechCrunch
— Christina Ayiotis, Esq., CRM, CIPP/E, AIGP (@christinayiotis) August 10, 2026
And according to cybersecurity researcher Bill Swearingen, there may be other creative ways to befuddle Flock’s spy cams. He devised with a special computer-generated pattern which, once stretched out across the vehicle in the form of a wrap, will result it in not being flagged as a car by Flock’s cameras.
During this year’s Def Con cybersecurity conference in Las Vegas, Swearingen showed off a 2009 Toyota Yaris wrapped in vinyl featuring the logo of automotive publication Donut Media (which is owned by Futurism‘s parent company Recurrent Ventures.)
Despite the car looking a bit chaotic — albeit still very much like a small passenger vehicle to the human eye — “we proved it was effective,” Swearingen told TechCrunch. The Yaris successfully defeated surveillance detection at Def Con during the pattern’s first public test.
“Privacy is a fundamental right,” he added, aiming to give people a way to “opt out of being tracked.”
To come up with the pattern, he used a reinforcement learning model that trained itself on patterns that evade detection. The model got to the point of being able to confuse all 11 open source detection algorithms he tested, per TechCrunch, in addition to Flock’s license plate readers and even Axon’s body-worn cameras used by police departments.
As part of his noRecognition crowdsourcing campaign, Swearingen has also come up with T-shirts and hoodies that feature algorithmic detection-busting patterns. He hopes to eventually put pattern-printed skins for entire cars on sale as well.
One thing is clear: as outrage over Flock’s heavy presence across the country grows, people are looking for any way to foil the tech — especially if it looks cool.
Recently, the Kyoto Prefectural Police caught one of the initial-seeders of the well-known Japanese torrent website Nyaa. Masakazu Ono, 56, from Sagamihara in the Kamigawa Prefecture, was arrested after a multi-year investigation. While this news is straightforward by itself, the interesting bit is that CODA, the Japanese anti-piracy entity that performed the initial cyber-investigation, identified Ono allegedly without joining or monitoring BitTorrent swarm traffic, with the help of the Japan Hacker Association (JHA).
CODA's description of only having "[analyzed] how data moves through torrent sites, such as index sites and tracker sites" is vague and can be parsed in a number of ways. The distinction between indexer and tracker is relevant: while an indexer is a user-visible list of torrents (usually a website), the tracker is the background service that software like qBittorrent actually connects to, and it lists everyone who's currently active in that torrent, whether sharing, downloading, or both.
Our hypothesis is that CODA kept close tabs on the Nyaa trackers to see which IP address (in this case, Ono's) consistently appeared as the first person to offer up a 100% complete set of data for a show, a theory that would fit with how long the operation took. JHA's founder, Takayuki Sugiura, was the first to decrypt the Winny P2P protocol in 2004 to find original uploader sources, and the methods used weren't too different from the aforementioned theory. It's also possible that Ono wasn't using a VPN but was logged into Nyaa, or activity at another website's cookies or CDN logs was used to establish a link to his activity at Nyaa, and from thereon, to the tracker.
An initial-seeder is someone who is the first to offer up data for others to download — in this case, shows and movies. The show that reportedly got Ono nailed was Midnight Taxi, a 2026 Japanese drama produced by NHK and WOWOW. However, the police apparently had been tracking him for quite a while and claimed that he had uploaded close to a thousand NHK recordings over the investigation's span.
The process started in 2021 when CODA first launched its Cross-Border Enforcement Project, aiming to identify Nyaa's uploaders. During three years, with the help of JHA, CODA seemingly collected enough information to get the Kyoto police involved in 2024. Three "secondary uploaders" from "reach sites" were charged in 2025, likely meaning regular users participating in the torrents, who got there through link aggregator sites. Fast-forward to a couple of weeks ago, and Masakazu Ono was formally arrested with specific charges.
The most common techniques used by law enforcement agencies are creating a tracker honeypot or participating in the torrent swarm themselves in a bid to identify who's sharing files. However, Nyaa's list of trackers is small and comprises well-known, trusted servers, so together with CODA's wording, it's unlikely this was the method used. Furthermore, Japanese law generally frowns on non-targeted data monitoring, meaning that techniques like Deep Packet Inspection (DPI) and traffic volume monitoring likely weren't used, at least until such time as Ono was identified.
Australia and Singapore are expanding cooperation on AI safety, cybersecurity, digital infrastructure, and subsea-cable resilience through their first Cyber and Digital Senior Officials Dialogue.
A bug in the tech giant's website the logo of US marketing automation company Klaviyo Inc. is seen displayed on a smartphone in front of an abstract background on a computer screen..
Steam hardware customers in Europe should “expect fake messages - email, SMS or phone,” says Valve in an email bulletin circulating today. The problem is that its European hardware distributor, CEVA Logistics, has fallen prey to a cyberattack where personal information and hardware purchase details were compromised. The most sensitive data, such as payment information, passwords, and/or Steam Guard codes, was not pilfered during the attack.
According to the official communication from Valve, CEVA was hit by a cyberattack between July 29 and August 1, 2026. The PC gaming company says that it learned that hackers had successfully swiped a range of information about Steam hardware customers on August 7.
As this data was in the hands of CEVA, it was strictly limited to delivery-related information. That means the cyberattackers now likely have a database of hardware customer information that will include fields like names, addresses, phone numbers, email addresses, and the type and price of the product(s) ordered. CEVA holds this type of information for 90 days to cover fulfillment of the hardware order. It is worth repeating that the attacked distributor won’t have spilled data such as payment information, passwords, and/or Steam Guard codes.
The likely fallout customers should be wary of
Valve candidly warns those who have purchased Steam hardware like a Steam Deck or Steam Machine in Europe over recent months to “expect fake messages - email, SMS or phone - that mention your hardware order and appear to come from Steam, Valve or a delivery company.” Likely angles used by swindlers will be to ask for extra customs or delivery fees. They may also ask you to sign into some fake web portal or other. However, you should ignore such requests, of course, and changing your Steam password or anything to do with your account settings is not necessary, insists Valve.
If you are in any doubt about a communication that comes from Steam / Valve or a distributor, please check the URLs carefully. For example, Steam Support only handles account issues at help.steampowered.com, and only store.steampowered.com, www.steampowered.com, steamcommunity.com, or help.steampowered.com are ever used for Steam account logins. You are advised to type these into a browser yourself and not click any email-embedded links. We assume poking around the official Steam App on your PC will also be fine, and many of you will have that auto-running when your PC starts up. Lastly, you are reminded that “Steam Support will never ask you for your password or a Steam Guard code. Neither will a courier.”
CEVA isolated the affected systems and notified data protection authorities after discovering the hack. It has brought in outside investigators.
Turns out there’s one thing AI chatbots are definitively better at than humans: scamming innocent people.
A new piece of research flagged by Wired found that large language models are significantly better than humans at pulling off “pig butchering” scams, a form of online investment fraud where criminals pursue fake, long-term relationships with targets in order to build trust before cleaning them out financially.
To pull off the study, researchers at Northeastern and a grab bag of international institutions pitted 22 test subjects against two forms of scammers: one an AI chatbot, the other a random human. After a seven-day courtship period, the “scammers” asked their “victims” to either download an app or play an online game, a stand-in for a financially-manipulative request (asks that, in the real world, could be used to hack a victim’s device).
Overall, just 18 percent of the human scammers acquiesced, while the AI chatbots were much more successful, courting nearly half of the participants.
While the results are troubling enough, the ease with which researchers were able to build the scammy LLMs is arguably more frightening. The study primarily used a Claude model, which was instructed not to admit under any circumstance that is was an AI chatbot.
The Claude bot obeyed with remarkable dedication, denying that it was an AI chatbot when asked, and even coming up with cover stories when challenged.
“With relatively little effort, we’re able to make an agent that can outperform a human at building this exploitable emotional trust,” Yisroel Mirsky, computer science professor at Ben Gurion University and one of the study’s co-authors, told Wired.
Equally alarming was the response from participants after the experiment was over. Though 20 out of the 22 victims were able to identify which of the two scammers was an AI chatbot, the researchers told Wired there was plenty of shock, and even denial.
“Some people were just blown away,” said University Amrita Vishwa Vidyapeetham professor Gilad Gressel. “They just had absolutely no idea.”
AI agents are escaping cybersecurity testing environments and reaching real-world systems, raising questions about whether safety infrastructure, industry standards and regulation can keep pace with increasingly powerful models.
A security researcher has designed an algorithm that can create computer-generated patterns capable of hiding people, faces, and vehicles from detection by surveillance cameras.
Google recently changed how it refers and assigns names to hacking groups. TechCrunch spoke with one of the world’s foremost experts on tracking hackers to understand why companies give hackers codenames.
Last month, OpenAI made a headline-generating claim: that a group of its AI models had conspired to break free, access the internet, and hack into the internal systems of open source AI platform Hugging Face, which confirmed the infiltration.
The incident rattled the tech industry, seemingly illustrating how the threat of AI models turning into rogue cybersecurity threats had become a reality. Months earlier, Anthropic’s Mythos AI model had already also drawn attention after it was similarly found to have broken containment. Then, this week, Meta also said its own frontier model had been implicated in yet another inadvertent hack of a third party company, closely followed by security researchers saying Chinese open-weight model Kimi K3 had done the same.
But while it’s not hard to see an emerging trend, some thorny questions about how severe the situation really is are starting to crop up, with some experts arguing these incidents could’ve easily been avoided.
For one, the slow and surprisingly deliberate way OpenAI’s models moved during the Hugging Face hack — right beneath OpenAI’s nose — gives a whiff that the company may have been careless in monitoring the experimental AI.
During a presentation at the Black Hat conference this week, OpenAI security engineer Michael Dalton and safety researcher Eric Wallace expanded on what went down during the hack. Wallace explained that a “team of agents” that were “working together,” had been “finding exploits, sharing them with one another, moving laterally through our systems and external systems, and doing this over the course of days and weeks,” as quoted by Wired.
The agents even left a lengthy track record of their schemings on an internal message board, which ultimately contained hundreds of thousands of messages. It also raises a question: with OpenAI’s immense resources, why wasn’t anybody monitoring these frontier models as they rampaged through the net?
The AI models shared exploits with each other on this messaging board, an “explosion in communication and intelligence from models,” per Wallace. They acted in sometimes strikingly human — and therefore messy — ways, splitting up tasks and even accidentally deleting each other’s work, leading to what Wired characterized as “petty drama.”
In other words, these AI agents were leaving an enormous trail of bread crumbs that alert OpenAI’s many human researchers could have spotted. And the same, obviously, goes for their colleagues at Anthropic, Meta and Moonshot AI, the creator of Kimi.
“A simple analysis of the actual risk has an actual simple answer,” security and compliance consultant Davi Ottenheimer told the publication at the time. “The OpenAI mistakes were dead simple.”
“I’d call it more of a defensive failure than exceptionally good offense,” AI hacking agents company Pensar R&D head Kyle Ryan told TechCrunch..
Whether the hack was as much of a “pivotal moment both for our company as well as the AI industry as a whole,” as Dalton put it during this week’s conference, remains debatable. For one, these AI companies are highly motivated to characterize their models as a major threat to cybersecurity to stand out against neck-in-neck competition.
According to Dalton, OpenAI is vowing to beef up “security prevention, detection, and response techniques” while “consciously slowing down research in order to enhance security and to upgrade the security principles.”
When every leading AI lab has had the same thing happen, it’s worth asking whether they should have taken those steps proactively.
Researchers found common points of failure, like software used to organize and display web content, could have allowed hackers to run riot through government websites.
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