Cybercriminals buy personal data from brokers to personalize tax scams with fake IRS messages, making fraudulent emails feel legitimate for identity theft.

Beyond mistakes or nonsense, deliberately bad information being injected into AI search summaries is leading people down potentially harmful paths. These days, rather than showing you the traditional list of links when you run a search query, Google is intent on throwing up AI Overviews instead: synthesized summaries of information scraped off the web, with some word-prediction magic added, and packaged together in a way to sound as accurate and reliable as possible. We've written before about some of the problems with these AI Overviews, which regularly contain mistakes or nonsense, and of course rip off the work of the human writers who actually know the answers to the questions you're putting into Google. There's another problem though--these AI answers can actually be dangerous. As with every other new technology through history, scams are now making their way into AI Overviews as well, apparently injecting Google's AI answers with fraudulent phone numbers that you shouldn't trust.

A whistleblower trapped inside a "pig butchering" scam compound gave WIRED a vast trove of its internal materials--including 4,200 pages of messages that lay out its operations in unprecedented detail. Just before 8am one day last April, an office manager who went by the name Amani sent out a motivational message to his colleagues and subordinates. "Every day brings a new opportunity--a chance to connect, to inspire, and to make a difference," he wrote in his 500-word post to an office-wide WhatsApp group. "Talk to that next customer like you're bringing them something valuable--because you are." He and his underlings worked inside a " pig butchering " compound, a criminal operation built to carry out scams --promising romance and riches from crypto investments--that often defraud victims out of hundreds of thousands or even millions of dollars at a time. The workers Amani was addressing were eight hours into their 15-hour night shift in a high-rise building in the Golden Triangle special economic zone in Northern Laos. Like their marks, most of them were victims, too: forced laborers trapped in the compound, held in debt bondage with no passports. They struggled to meet scam revenue quotas to avoid fines that deepened their debt.

It used to be just a sci-fi nightmare scenario, but today, AI phishing is real, and it's costing companies millions. We've already touched upon this one, but the Hong Kong phishing scam that targeted an employee at Arup deserves a deeper dive. The employee was tricked by deepfake versions of her CFO and colleagues into transferring HK 200 million across 15 transactions. The case has been widely reported and confirmed by the Hong Kong police. Every face and voice was AI-generated.

This work presents a review of attack methodologies targeting Pix, the instant payment system launched by the Central Bank of Brazil in 2020. The study aims to identify and classify the main types of fraud affecting users and financial institutions, highlighting the evolution and increasing sophistication of these techniques. The methodology combines a structured literature review with exploratory interviews conducted with professionals from the banking sector. The results show that fraud schemes have evolved from purely social engineering approaches to hybrid strategies that integrate human manipulation with technical exploitation. The study concludes that security measures must advance at the same pace as the growing complexity of attack methodologies, with particular emphasis on adaptive defenses and continuous user awareness.

Norton's award-winning security software makes it simple to keep you and your family safe from increasingly sophisticated digital threats. AI scams are getting more sophisticated. Here's how Norton is fighting back to protect you and your family. It feels like overnight AI has ended up just about everywhere. From deepfakes and ChatGPT homework, to em-dashes and political misinformation, keeping on top of the latest AI trends is almost impossible.

Scam detection remains a critical challenge in cybersecurity as adversaries craft messages that evade automated filters. We propose a Hierarchical Scam Detection System (HSDS) that combines a lightweight multi-model voting front end with a fine-tuned LLaMA 3.1 8B Instruct back end to improve accuracy and robustness against adversarial attacks. An ensemble of four classifiers provides preliminary predictions through majority vote, and ambiguous cases are escalated to the fine-tuned model, which is optimized with adversarial training to reduce misclassification. Experiments show that this hierarchical design both improves adversarial scam detection and shortens inference time by routing most cases away from the LLM, outperforming traditional machine-learning baselines and proprietary LLM baselines. The findings highlight the effectiveness of a hybrid voting mechanism and adversarial fine-tuning in fortifying LLMs against evolving scam tactics, enhancing the resilience of automated scam detection systems.

Connecticut man falls victim to fake crypto investment company promising $368,000 returns, loses life savings in wire transfer scam that the FBI says is nearly impossible to recover.

The Federal Trade Commission reports over $2.9 billion in losses from impostor bank scams using caller ID spoofing and AI voice technology to deceive victims.

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