He Stole Hundreds of iPhones and Looted People’s Life Savings. He Told Us How.
A convicted iPhone thief explains how a vulnerability in Apple’s software got him fast cash—and then a stint in a high-security prison
A convicted iPhone thief explains how a vulnerability in Apple’s software got him fast cash—and then a stint in a high-security prison
RUSH CITY, Minn.—Before the guards let you through the barbed-wire fences and steel doors at this Minnesota Correctional Facility, you have to leave your phone in a locker. Not a total inconvenience when you’re there to visit a prolific iPhone thief.
I wasn’t worried that Aaron Johnson would steal my iPhone, though. I came to find out how he’d steal it.
“I’m already serving time. I just feel like I should try to be on the other end of things and try to help people,” Johnson, 26 years old, told me in an interview we filmed inside the high-security prison where he’s expected to spend the next several years.
For the past year, my colleague Nicole Nguyen and I have investigated a nationwide spate of thefts, where thieves watch iPhone owners tap their passcodes, then steal their targets’ phones—and upend their financial and digital lives.
Johnson, along with a crew of others, operated in Minneapolis for at least a year during 2021 and 2022. In and around bars at night, he would befriend young people, slyly learn their passcodes and take their phones. Using that code, he’d lock victims out of their Apple accounts and loot thousands of dollars from their bank apps. Finally, he’d sell the phones themselves.
It was an elaborate, opportunistic scheme that exploited the Apple ecosystem and targeted trusting iPhone owners who figured a stolen phone was just a stolen phone.
Last week, Apple announced Stolen Device Protection, a feature that likely will protect against these passcode-assisted crimes.
Yet even when you install the software, due in iOS 17.3, there will be loopholes. The biggest loophole? Us. By hearing how Johnson did what he did, we can learn how to better secure the devices that hold so much of our lives.
Johnson isn’t a sophisticated cybercriminal. He said he got his start pickpocketing on the streets of Minneapolis. “I was homeless,” he said. “Started having kids and needed money. I couldn’t really find a job. So that’s just what I did.”
Soon he realised the phones he was nabbing could be worth a lot more—if only he had a way to get inside them. Johnson said no one taught him the passcode trick, he just stayed up late one night fiddling with a phone and figured out how to use the passcode to unlock a bounty of protected services.
“That passcode is the devil,” he said. “It could be God sometimes—or it could be the devil.”
According to the Minneapolis Police Department’s arrest warrant, Johnson and the other 11 members of the enterprise allegedly accumulated nearly $300,000. According to him, it was likely more.
“I had a rush for large amounts at a time,” he said. “I just got too carried away.”
In March, Johnson, who had prior robbery and theft convictions, pleaded guilty to racketeering and was sentenced to 94 months. He told the judge he was sorry for what he did.
Here’s how the nightly operation would go down, according to interviews with Johnson, law-enforcement officials and some of the victims:
Pinpoint the victim. Dimly lit and full of people, bars became his ideal location. College-age men became his ideal target. “They’re already drunk and don’t know what’s going on for real,” Johnson said. Women, he said, tended to be more guarded and alert to suspicious behaviour.
Get the passcode. Friendly and energetic, that’s how victims described Johnson. Some told me he approached them offering drugs. Others said Johnson would tell them he was a rapper and wanted to add them on Snapchat. After talking for a bit, they would hand over the phone to Johnson, thinking he’d just input his info and hand it right back.
“I say, ‘Hey, your phone is locked. What’s the passcode?’ They say, ‘2-3-4-5-6,’ or something. And then I just remember it,” Johnson described. Sometimes he would record people typing their passcodes.
Once the phone was in his hand, he’d leave with it or pass it to someone else in the crew.
Lock them out—fast. Within minutes of taking the iPhones, Johnson was in the Settings menu, changing the Apple ID password. He’d then use the new password to turn off Find My iPhone so victims couldn’t log in on some other phone or computer to remotely locate—and even erase—the stolen device.
Johnson was changing passwords fast—“faster than you could say supercalifragilisticexpialidocious,” he said. “You gotta beat the mice to the cheese.”
Take the money. Johnson said he would then enrol his face in Face ID because “when you got your face on there, you got the key to everything.” The biometric authentication gave Johnson quick access to passwords saved in iCloud Keychain.
Savings, checking, cryptocurrency apps—he was looking to transfer large sums of money out. And if he had trouble getting into those money apps, he’d look for extra information, such as Social Security numbers, in the Notes and Photos apps.
By the morning, he’d have the money transferred. That’s when he’d head to stores to buy stuff using Apple Pay. He’d also use the stolen Apple devices to buy more Apple devices, most often $1,200 iPad Pro models, to sell for cash.

Sell the phones. Finally, he’d erase the phone and sell it to Zhongshuang “Brandon” Su who, according to his arrest warrant, sold them overseas.
While Johnson did steal some Android phones, he went after iPhones because of their higher resale value. At bars, he’d scope out the scene—looking for iPhone Pro models with their telltale trio of cameras. He said Pro Max with a terabyte of storage could get him $900. Su also bought Johnson’s purchased iPads.
Su pleaded guilty to receiving stolen property and was sentenced to 120 days at an adult corrections facility in Hennepin County, Minn. Neither Su nor his lawyer responded to requests for comment.
On a good weekend, Johnson said, he was selling up to 30 iPhones and iPads to Su and making around $20,000—not including money he’d taken from victims’ bank apps, Apple Pay and more.
A week after my trip to Minnesota, Apple announced Stolen Device Protection. The security setting will likely foil most of Johnson’s tricks, but it won’t be turned on automatically.
If you don’t turn it on, you’re as vulnerable as ever. Switching it on adds a line of defence to your phone when away from familiar locations such as home or work.

To change the Apple ID password, a thief would need Face ID or Touch ID biometric scans—that is, your face or your finger. The passcode alone won’t work. And the process has a built-in hourlong delay, followed by another biometric scan. This same slow process is also required for adding a new Face ID and disabling Find my iPhone.
Some functions, such as accessing saved passwords in iCloud Keychain or erasing the iPhone, are available without the delay but still require Face ID or Touch ID.
A criminal might still be motivated to kidnap a person with lots of money, then slowly break through these layers of security. However, the protections will likely dissuade thieves who just want to grab phones and flee the scene.
So what loopholes remain? A thief who gets the passcode could still buy things with Apple Pay. And any app that isn’t protected by an additional password or PIN—like your email, Venmo, PayPal and more—is also vulnerable.
That’s why you should also:
The most obvious is Johnson’s advice: Watch your surroundings and don’t give your passcode out.
If this crime has taught us anything, it’s that a single device now contains access to our entire lives—our memories, our money and more. It’s on us to protect them.
—Nicole Nguyen contributed to this article.
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Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
A sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.
The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.
The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.
The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.
This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.
Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.
Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.
JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.
The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.
That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.
A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.
When interest rates rise, those assumptions become less forgiving.
September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.
The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.
CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.
Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.
Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.
The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.
Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.
Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.
The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.
That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.
The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.
What has changed is the market’s willingness to overlook the price and financing structure of that growth.
The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.
More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.
For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.
Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.
The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.
Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026
Philadelphia Semiconductor Index: Down approximately 11.4 per cent
Micron Technology: Down approximately 14.6 per cent
Samsung Electronics: Down approximately 18 per cent
SK Hynix: Down approximately 24 per cent
US September inflation: 3.7 per cent annually
Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion
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