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ATM Jackpotting Escalates: The Evolving Threat To Cash Dispensers

Historic Exploits And The Evolution Of ATM Hacking

In 2010, security researcher Barnaby Jack demonstrated at the Black Hat conference how an ATM could be hacked to dispense cash, drawing attention to vulnerabilities that were largely theoretical at the time. The demonstration marked an early turning point in public awareness of ATM cybersecurity risks and foreshadowed techniques later adopted by criminal groups.

The Rise Of ATM Jackpotting As A Criminal Enterprise

ATM jackpotting has since evolved from a research demonstration into a large-scale criminal activity. According to a recent FBI security bulletin, more than 700 attacks on cash machines were recorded in 2025, generating an estimated $20 million in illegal withdrawals. Attackers combine physical access methods, such as using generic keys to open machines, with malware designed to trigger rapid cash dispensing.

Dissecting The Ploutus Malware Threat

One of the most widely used tools in these attacks is Ploutus malware. The software targets Windows-based operating systems used by many ATMs and exploits vulnerabilities in the XFS (Extensions for Financial Services) software, which controls communication among components such as PIN pads, card readers, and cash dispensers. Once installed, the malware allows attackers to command machines to release cash without affecting customer accounts.

Business Implications And Future Trends

The FBI notes that Ploutus attacks focus on ATM infrastructure rather than on individual bank accounts, making them harder to detect through traditional fraud-monitoring systems. This creates new challenges for financial institutions, which must protect both physical hardware and digital systems.

As jackpotting techniques continue to evolve, banks and operators are increasing investment in stronger access controls, system monitoring, and software security. These measures are becoming essential to reducing operational risk and maintaining trust in cash infrastructure.

Copyright Law Struggles To Keep Up With AI Training

Courts Are Still Applying Old Copyright Rules To AI

AI companies train models on enormous amounts of published material, including books, articles and academic research. Whether using that content without authors’ permission violates copyright law remains unresolved.

Much of the debate centres on fair use, which allows copyrighted material to be used without permission in certain circumstances. Courts consider factors such as the purpose of the use, how much material was involved and its impact on the original market.

Anthropic Case Sets An Important Precedent

A major case involving Anthropic and a group of authors provided one of the clearest rulings so far. Judge William Alsup found that using copyrighted books to train AI models was lawful, comparing the process to people reading and studying literature before creating something new.

Anthropic was nevertheless ordered to pay $1.5 billion in a settlement. The penalty concerned books the company had obtained from illegal online libraries rather than the AI training itself.

For AI companies, that distinction could prove significant because it separates studying copyrighted material from directly copying it.

Competition Could Be The Key Issue

A case involving Thomson Reuters and Ross Intelligence offers a different perspective. A court ruled that Ross could not claim fair use after using Reuters’ copyrighted material to develop a competing AI-powered legal research platform.

The decision suggests courts may be less willing to consider AI training fair use when copyrighted content is used to build a product that directly competes with the original.

For authors, an unresolved question is whether AI-generated content should be considered competition for the works used to train these models.

The Law Has Yet To Catch Up

US copyright law predates generative AI by decades, leaving courts to apply old principles to new technology. Questions also remain over copyright protection for AI-generated works. In Thaler v. Perlmutter, a court ruled that material created entirely by AI cannot receive copyright protection.

Major AI companies remain involved in copyright litigation, and different courts could reach different conclusions. For now, there is no universal rule: the legality of AI training will depend on the circumstances of each case and how courts ultimately interpret copyright and fair use.

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