How Hackers Can Up Their Game by Using ChatGPT

Consumers, beware: AI chatbots like ChatGPT are likely to drive an increase in the use and effectiveness of online fraud tools such as phishing and spear-phishing messages.

In fact, it could already be happening. Phishing attacks around the world grew almost 50% in 2022 from a year earlier, according to Zscaler, a cloud-security provider. And, some experts say, artificial-intelligence software that makes phishing messages sound more believable are part of the problem. AI reduces or eliminates language barriers and grammatical mistakes, helping scammers impersonate a target’s colleagues, friends or relatives.

“This new era is going to be worse than what we had before,” says Meredith Broussard, research director at the New York University Alliance for Public Interest Technology. “And what we had before was really, really bad.”

High stakes

AI chatbots have exploded in popularity, with perhaps the best-known being ChatGPT, developed by the AI-research company OpenAI, a strategic partner of Microsoft. But dozens of chatbots, using what are referred to as large language models, are becoming more widely available and can closely mimic human communication based on data they amass. These models can be used for many purposes, such as helping office workers create routine memos more quickly. But they can also be used by criminals—to defraud victims, for instance, or to spread malicious viruses.

Telltale signs of a phishing attack have long included mistakes in grammar or spelling. But AI can give a phishing attack more credibility—and reach—not just because of its ability to generate fluent, grammatical messages in many languages, but also because of its ability to mimic the speaking or writing styles of individuals.

“The whole point with large language models is their ability to emulate what humans sound like,” says Etay Maor, senior director of security strategy at Cato Networks, a cloud networking and security provider.

Thus, given the opportunity to learn the style in which a certain person writes emails and texts, Maor says, an AI program can be used to mimic communications from a company executive.

“It’s all about trust, and if I can make you think I’m one of you, you’re going to begin to do things with more trust and less skepticism,” says Roger Grimes, a computer-security professional with KnowBe4, a security-awareness training and simulated-phishing platform.

Using AI, Grimes says, criminals can quickly determine industry-specific terms that give them more ability to target companies such as hospitals, banks and fintech.

Targeted campaigns

AI’s usefulness in phishing and spear-phishing attacks doesn’t stop with its ability to mimic authentic human communication. The analytic skills of machine learning can also be useful in determining who best to target in an organization and how exactly to attack them.

Sean McNee, vice president of research and data at DomainTools, an internet intelligence company, offers a hypothetical example. Say an accountant at a company innocently posts on social media about his frustrations with a recent audit. AI could determine the accountant’s peers, his company’s reporting structure and who else at the company might be most susceptible to an attack. The attacker then could create a spear-phishing email purporting to be from the chief financial officer referring to a discrepancy in the audit and asking the recipient to open an attached spreadsheet that contains a virus.

Ramayya Krishnan, dean of Carnegie Mellon University’s Heinz College, recommends being proactive to protect against such attacks.

First, before acting on something, he says, people should always verify the legitimacy of the request through independent means. This means before clicking on a link or sending money, the recipient should call the individual through a familiar phone number or walk into the person’s office to confirm the request, Krishnan says.

Maintain a healthy dose of skepticism for everything you receive, Maor says. Ask yourself, why is my bank emailing me? Why is there a sense of urgency? Why is there an attachment to click on? It’s also advisable to hover over a link before clicking to see if it leads to an expected URL. “If you have some reason to think something is amiss, don’t click on it,” Maor says.

Other guardrails

Strong regulation of AI could also help, says Broussard, who is also an associate professor at the Arthur L. Carter Journalism Institute of New York University.

AI itself should also be enlisted to help identify malicious content with its origins in AI, says Dave Ahn, chief architect at Centripetal, a cybersecurity company. But first the models for doing so will have to evolve and the data will have to improve. Data on successful AI-based attacks will help cybersecurity experts train new models to identify malicious activity better, says Ahn.

Other possible security measures include giving users a way to distinguish their content as authentic. The use of hidden patterns known as “watermarks,” for instance, can be buried in AI-generated texts to help identify whether the words are written by a human or computer, Krishnan says. But the applicability of these tools is limited.

Says Krishnan, “We’re not near deploying them at scale where it’s a solution to the bad-actor potential we have today.”

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.

The debt behind the AI build-out

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.

Inflation changes the calculation

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.

China adds another source of pressure

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.

A reset rather than the end of the boom

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.

Fact box

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