Agentic AI is changing the economics of cybercrime

Asha Hemrajani

Oct 08

AI agents could make cybercrime cheaper, faster and easier to scale, putting pressure on defenders to tighten access controls and respond faster as attacks evolve.AI-enabled cybercrime and scams are ...

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Deepfakes have already been used in multimillion-dollar financial frauds, from the US$25 million ($32 million) video call scam in Hong Kong two years ago to a Singapore victim who lost nearly $5 million this year after scammers impersonated Prime Minister Lawrence Wong.\nDeepfakes and AI-generated phishing emails are the two most common types of generative AI-enabled cybercrime. However, recent reports suggest that cybercrime is beginning to shift from AI-assisted crime, where a human uses a large language model (LLM) as a tool to create deepfake impersonation images and videos to agentic AI-enabled crime, where AI agents can plan and execute across multiple stages of an attack with relatively little human intervention.\nAI agents can pursue an objective given to it by the human operator in pretty much any way a human project manager would. The AI agent can coordinate multiple tasks, use different tools at its disposal and most importantly, as we saw in the Hugging Face incident, adapt their behaviour if they meet obstacles. Hence, they can operate continuously and learn from what challenges they may encounter. \nThis is especially troubling because of the implications on financial cybercrime due to the autonomy that agents enjoy as well as the integration with other systems and the larger scale and speed that is now possible. This significantly alters the cybersecurity and fraud challenge. \nA year ago, I argued that agentic AI would expand the attack surface and force organisations to rethink how autonomous systems are secured and governed. The emerging threat is the other side of that same equation. The autonomy that businesses hope to harness is also available to criminals too.\nThe Google Threat Intelligence Group has observed this progression towards greater autonomy, with threat actors beginning to use multi-agent systems to automate and coordinate different stages of cybercrime operations. A recent cybercrime campaign shows how quickly that model is developing.\nCybersecurity researchers in a firm founded by former members of Unit 8200, the cyber warfare and signals intelligence agency of the Israel Defence Forces, reported on the theft of over 600,000 credit card details from online retailers during the period from July to September 2026. A human operator used three separate agentic AI systems to divide the work of attacking the online retailers. One agentic AI system scanned websites for vulnerabilities, another system pursued ways to exploit them, while a third coordinated the campaign and made tactical decisions. The human merely supplied brief objectives, leaving the agents to handle much of the operation.\nThe same campaign compromised at least 119 websites with card skimmers and stole more than 600,000 valid credit card details. With the operation estimated to have cost between US$12,000 to US$18,000, or about US$25 per target, the significance goes beyond the scale of the theft. Agentic AI may not create entirely new forms of cybercrime, but it could change its economics by reducing the human labour, time and coordination required to operate at scale.\nThe Google Threat Intelligence Group's September 2026 report states that this type of functionality can be built in under 6 hours.\nFrom attacker to attackee\nAI agents can also be attacked. In other words, malware payloads target AI assistants rather than humans, with the ultimate aim of committing a cybercrime. \nAgentic AI email assistants are becoming increasingly popular as workplace AI adoption accelerates. They can search and summarise inboxes, draft personalised replies, extract information from email threads, and increasingly carry out multi-step workflows such as analysing information and then preparing the corresponding email response.\nCybersecurity researchers have revealed that in a controlled lab test, agentic AI email assistants could be leveraged to create a new route for attackers: malicious instructions hidden in an ordinary email can be read by an agentic assistant and mistaken for directions from its human user.\nIn the test, researchers placed hidden instructions in an email that caused an automated Gemini agentic workflow to copy a one-time login code from another message into a draft email. When the user opens the draft email, the one-time login code is sent to an external server.   \nFaster and less labour-intensive cybercrime\nMany of the constraints on conducting cybercrime have been economic. Attackers have needed expertise, access to IT infrastructure and time in order to identify victims, discover vulnerabilities in their systems, gain entry, move through systems to steal credentials and information, and get access to funds.\nAgentic AI will ease some of those constraints by allowing a single operator to oversee more activity simultaneously and move through parts of a cybercrime operation faster simply via brief instructions from the human operator. \nFor cyber defenders and law enforcement officials, this challenges the oft-held assumption that the bigger the criminal group behind the attack, the more sophisticated and larger-scale the attack.  If another target can be added to an operation for tens rather than thousands of dollars, attackers have every incentive to cast a much wider net.\nThe way forward: Defenders need to change the economics too\nSince agentic AI can lower the cost of attacking, cyber defence should aim to raise the cost and effort required for attackers, whether human or otherwise, to succeed. That does not necessarily require an entirely new defensive playbook; it makes getting the fundamentals of cyber hygiene right even more urgent.\nHowever unglamorous it may sound, the answer still begins with the basics of cyber hygiene: faster patching of vulnerabilities, shorter credential lifespans and, most importantly, tighter privilege controls. These measures make lateral movement within victim networks more difficult. Detection and response will also need greater automation as attacks begin to move faster than human teams can realistically investigate. Increasingly, the contest will be determined not simply by who detects an attack most accurately, but by who can adapt fastest as the attack evolves.\nPrediction therefore means identifying likely attack journeys and emerging typologies before losses occur. And as fraud itself becomes more agentic, stronger detection will remain essential, but faster coordination across security teams, institutions and systems may prove even more important. As the cybersecurity community convenes at the Singapore International Cyber Week and GovWare, attention is likely to focus not only on how AI is transforming organisations, but also on how defenders can keep pace with increasingly autonomous threats.\nAsha Hemrajani is a senior fellow at the S. Rajaratnam School of International Studies, Nanyang Technological University (NTU) Singapore","kind":"highlight","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":false,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":0,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"}}}